Relevant bibliographies by topics / Bioinformatics. eng

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  2. Dissertations / Theses
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  5. Conference papers

Academic literature on the topic 'Bioinformatics. eng'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Bioinformatics. eng"

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Guang, Ma. "In Silicon Cloning and Bioinformatics Analysis of the Raphanus Sativus WUS Gene." Engineering 05, no. 10 (2013): 509–12. http://dx.doi.org/10.4236/eng.2013.510b104.

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Lozano-Muniz, Susana, and Maria del Carmen Urzua-Hernandez. "Structural Bioinformatics of Protein & DNA, as Early Stimulation in Basic Education of Rural and Indigenous Communities of Oaxaca." Engineering 05, no. 10 (2013): 255–58. http://dx.doi.org/10.4236/eng.2013.510b053.

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Wang, Xinyu, Jiaojiao Yang, and Xueren Gao. "Identification of key genes associated with lung adenocarcinoma by bioinformatics analysis." Science Progress 104, no. 1 (2021): 003685042199727. http://dx.doi.org/10.1177/0036850421997276.

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Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer, comprising around 40% of all lung cancer. Until now, the pathogenesis of LUAD has not been fully elucidated. In the current study, we comprehensively analyzed the dysregulated genes in lung adenocarcinoma by mining public datasets. Two sets of gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. The dysregulated genes were identified by using the GEO2R online tool, and analyzed by R packages, Cytoscape software, STRING, and GPEIA online tools. A total of 275 common dysregulated genes were identified in two independent datasets, including 54 common up-regulated and 221 common down-regulated genes in LUAD. Gene Ontology (GO) enrichment analysis showed that these dysregulated genes were significantly enriched in 258 biological processes (BPs), 27 cellular components (CCs), and 21 molecular functions (MFs). Furthermore, protein-protein interaction (PPI) network analysis showed that PECAM1, ENG, KLF4, CDH5, and VWF were key genes. Survival analysis indicated that the low expression of ENG was associated with poor overall survival (OS) of LUAD patients. The low expression of PECAM1 was associated with poor OS and recurrence-free survival of LUAD patients. The cox regression model developed based on age, tumor stage, ENG, PECAM1 could effectively predict 5-year survival of LUAD patients. This study revealed some key genes, BPs, CCs, and MFs involved in LUAD, which would provide new insights into understanding the pathogenesis of LUAD. In addition, ENG and PECAM1 might serve as promising prognostic markers in LUAD.
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Listik, Eduardo, Ben Horst, Alex Seok Choi, Nam Y. Lee, Balázs Győrffy, and Karthikeyan Mythreye. "A bioinformatic analysis of the inhibin-betaglycan-endoglin/CD105 network reveals prognostic value in multiple solid tumors." PLOS ONE 16, no. 4 (2021): e0249558. http://dx.doi.org/10.1371/journal.pone.0249558.

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Inhibins and activins are dimeric ligands belonging to the TGFβ superfamily with emergent roles in cancer. Inhibins contain an α-subunit (INHA) and a β-subunit (either INHBA or INHBB), while activins are mainly homodimers of either βA (INHBA) or βB (INHBB) subunits. Inhibins are biomarkers in a subset of cancers and utilize the coreceptors betaglycan (TGFBR3) and endoglin (ENG) for physiological or pathological outcomes. Given the array of prior reports on inhibin, activin and the coreceptors in cancer, this study aims to provide a comprehensive analysis, assessing their functional prognostic potential in cancer using a bioinformatics approach. We identify cancer cell lines and cancer types most dependent and impacted, which included p53 mutated breast and ovarian cancers and lung adenocarcinomas. Moreover, INHA itself was dependent on TGFBR3 and ENG/CD105 in multiple cancer types. INHA, INHBA, TGFBR3, and ENG also predicted patients’ response to anthracycline and taxane therapy in luminal A breast cancers. We also obtained a gene signature model that could accurately classify 96.7% of the cases based on outcomes. Lastly, we cross-compared gene correlations revealing INHA dependency to TGFBR3 or ENG influencing different pathways themselves. These results suggest that inhibins are particularly important in a subset of cancers depending on the coreceptor TGFBR3 and ENG and are of substantial prognostic value, thereby warranting further investigation.
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Eun, Jung Woo, Chul Won Seo, Geum Ok Baek, et al. "Circulating Exosomal MicroRNA-1307-5p as a Predictor for Metastasis in Patients with Hepatocellular Carcinoma." Cancers 12, no. 12 (2020): 3819. http://dx.doi.org/10.3390/cancers12123819.

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Exosomal microRNAs (exo-miRs) contribute to cancer metastasis. To identify pro-metastatic circulating exo-miRs in hepatocellular carcinoma (HCC), next-generation sequencing-based plasma exo-miR profiles of 14 patients with HCC (eight non-metastatic and six with metastasis within 1 year of follow-up) were analyzed. Sixty-one miRs were significantly overexpressed among patients with metastatic HCC. Candidate miRs were selected through integrative analyses of two different public expression datasets, GSE67140 and The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA_LIHC). Integrative analyses revealed 3 of 61 miRs (miR-106b-5p, miR-1307-5p, and miR-340-5p) commonly overexpressed both in metastasis and vascular invasion groups, with prognostic implications. Validation was performed using stored blood samples of 150 patients with HCC. Validation analysis showed that circulating exo-miR-1307-5p was significantly overexpressed in the metastasis group (p = 0.04), as well as in the vascular invasion and tumor recurrence groups. Circulating exo-miR-1307-5p expression was significantly correlated with tumor stage progression (p < 0.0001). Downstream signaling pathways of miR-1307 were predicted using TargetScan and Ingenuity Pathway Analysis. On comprehensive bioinformatics analysis, the downstream pathway of miR-1307-5p, promoting epithelial–mesenchymal transition (EMT), showed SEC14L2 and ENG downregulation. Our results show that circulating exo-miR-1307-5p promotes metastasis and helps predict metastasis in HCC, and SEC14L2 and ENG are target tumor suppressor genes of miR-1307 that promote EMT.
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Suh, K. Stephen, Sreeja Sarojini, Maher Youssif, et al. "Tissue Banking, Bioinformatics, and Electronic Medical Records: The Front-End Requirements for Personalized Medicine." Journal of Oncology 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/368751.

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Personalized medicine promises patient-tailored treatments that enhance patient care and decrease overall treatment costs by focusing on genetics and “-omics” data obtained from patient biospecimens and records to guide therapy choices that generate good clinical outcomes. The approach relies on diagnostic and prognostic use of novel biomarkers discovered through combinations of tissue banking, bioinformatics, and electronic medical records (EMRs). The analytical power of bioinformatic platforms combined with patient clinical data from EMRs can reveal potential biomarkers and clinical phenotypes that allow researchers to develop experimental strategies using selected patient biospecimens stored in tissue banks. For cancer, high-quality biospecimens collected at diagnosis, first relapse, and various treatment stages provide crucial resources for study designs. To enlarge biospecimen collections, patient education regarding the value of specimen donation is vital. One approach for increasing consent is to offer publically available illustrations and game-like engagements demonstrating how wider sample availability facilitates development of novel therapies. The critical value of tissue bank samples, bioinformatics, and EMR in the early stages of the biomarker discovery process for personalized medicine is often overlooked. The data obtained also require cross-disciplinary collaborations to translate experimental results into clinical practice and diagnostic and prognostic use in personalized medicine.
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Puig, Oscar, Eugene Joseph, Malgorzata Jaremko, et al. "Comprehensive next generation sequencing assay and bioinformatic pipeline for identifying pathogenic variants associated with hereditary cancers." Journal of Clinical Oncology 35, no. 15_suppl (2017): e13105-e13105. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e13105.

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e13105 Background: Diagnosis of hereditary cancer syndromes involves time-consuming comprehensive clinical and laboratory work-up, however, timely and accurate diagnosis is pivotal to the clinical management of cancer patients. Germline genetic testing has shown to facilitate the diagnostic process, allowing for identification and management of individuals at risk for inherited cancers. However, the laboratory diagnostics process requires not only development and validation of comprehensive gene panels to improve diagnostic yields, but a quality driven workflow including an end-to-end bioinformatics pipeline, and a robust process for variant classification. We will present a gene panel for the evaluation of hereditary cancer syndromes, conducted utilizing our novel end-to-end workflow, and validated in the CLIA-approved environment. Methods: A targeted Next-Generation Sequencing (NGS) panel consisting of 130 genes, including exons, promoters, 5’-UTRs, 3’-UTRs and selected introns, was designed to include genes associated with hereditary cancers. The assay was validated using samples from the 1000 genomes project and samples with known pathogenic variants. Elements software was utilized for end-to-end bioinformatic process ensuring adherence with the CLIA quality standards, and supporting manual curation of sequence variants. Results: Preliminary data from our current panel of genes associated with hereditary cancer syndromes revealed high sensitivity, specificity, and positive predictive value. Accuracy was confirmed by analysis of known SNVs, indels, and CNVs using 1000 Genomes and samples carrying pathogenic variants. The bioinformatics software allowed for an end-to-end quality controlled process of handling and analyzing of the NGS data, showing applicability for a clinical laboratory workflow. Conclusions: We have developed a comprehensive and accurate genetic testing process based on an automated and quality driven bioinformatics workflow that can be used to identify clinically important variants in genes associated with hereditary cancers. It's performance allows for implementation in the clinical laboratory setting.
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Saygin, Didem, Tracy Tabib, Humberto E. T. Bittar, et al. "Transcriptional profiling of lung cell populations in idiopathic pulmonary arterial hypertension." Pulmonary Circulation 10, no. 1 (2020): ??? http://dx.doi.org/10.1177/2045894020908782.

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Despite recent improvements in management of idiopathic pulmonary arterial hypertension, mortality remains high. Understanding the alterations in the transcriptome–phenotype of the key lung cells involved could provide insight into the drivers of pathogenesis. In this study, we examined differential gene expression of cell types implicated in idiopathic pulmonary arterial hypertension from lung explants of patients with idiopathic pulmonary arterial hypertension compared to control lungs. After tissue digestion, we analyzed all cells from three idiopathic pulmonary arterial hypertension and six control lungs using droplet-based single cell RNA-sequencing. After dimensional reduction by t-stochastic neighbor embedding, we compared the transcriptomes of endothelial cells, pericyte/smooth muscle cells, fibroblasts, and macrophage clusters, examining differential gene expression and pathways implicated by analysis of Gene Ontology Enrichment. We found that endothelial cells and pericyte/smooth muscle cells had the most differentially expressed gene profile compared to other cell types. Top differentially upregulated genes in endothelial cells included novel genes: ROBO4, APCDD1, NDST1, MMRN2, NOTCH4, and DOCK6, as well as previously reported genes: ENG, ORAI2, TFDP1, KDR, AMOTL2, PDGFB, FGFR1, EDN1, and NOTCH1. Several transcription factors were also found to be upregulated in idiopathic pulmonary arterial hypertension endothelial cells including SOX18, STRA13, LYL1, and ELK, which have known roles in regulating endothelial cell phenotype. In particular, SOX18 was implicated through bioinformatics analyses in regulating the idiopathic pulmonary arterial hypertension endothelial cell transcriptome. Furthermore, idiopathic pulmonary arterial hypertension endothelial cells upregulated expression of FAM60A and HDAC7, potentially affecting epigenetic changes in idiopathic pulmonary arterial hypertension endothelial cells. Pericyte/smooth muscle cells expressed genes implicated in regulation of cellular apoptosis and extracellular matrix organization, and several ligands for genes showing increased expression in endothelial cells. In conclusion, our study represents the first detailed look at the transcriptomic landscape across idiopathic pulmonary arterial hypertension lung cells and provides robust insight into alterations that occur in vivo in idiopathic pulmonary arterial hypertension lungs.
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Xu, Jingjing, Bin Chen, Zhengbo He, and Youjin Hao. "Cloning, Characterization and Bioinformatic Analysis of the Gene Encoding the Larval Serum Protein 2 in Diapause of the Onion Maggot, Delia Antiqua." Engineering 05, no. 10 (2013): 487–90. http://dx.doi.org/10.4236/eng.2013.510b100.

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Gruenstaeudl, Michael. "annonex2embl: automatic preparation of annotated DNA sequences for bulk submissions to ENA." Bioinformatics 36, no. 12 (2020): 3841–48. http://dx.doi.org/10.1093/bioinformatics/btaa209.

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Abstract Motivation The submission of annotated sequence data to public sequence databases constitutes a central pillar in biological research. The surge of novel DNA sequences awaiting database submission due to the application of next-generation sequencing has increased the need for software tools that facilitate bulk submissions. This need has yet to be met with the concurrent development of tools to automate the preparatory work preceding such submissions. Results The author introduce annonex2embl, a Python package that automates the preparation of complete sequence flatfiles for large-scale sequence submissions to the European Nucleotide Archive. The tool enables the conversion of DNA sequence alignments that are co-supplied with sequence annotations and metadata to submission-ready flatfiles. Among other features, the software automatically accounts for length differences among the input sequences while maintaining correct annotations, automatically interlaces metadata to each record and displays a design suitable for easy integration into bioinformatic workflows. As proof of its utility, annonex2embl is employed in preparing a dataset of more than 1500 fungal DNA sequences for database submission. Availability and implementation annonex2embl is freely available via the Python package index at http://pypi.python.org/pypi/annonex2embl. Supplementary information Supplementary data are available at Bioinformatics online.
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Dissertations / Theses on the topic "Bioinformatics. eng"

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Yamasaki, Lílian Hiromi Tomonari. "Análise por ferramentas de bioinformática da proteína não-estrutural 5A do vírus da hepatite C genótipo 1 e 3 em amostras pré-tratamento /." São José do Rio Preto : [s.n.], 2010. http://hdl.handle.net/11449/94818.

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Resumo: A infecção pelo vírus da Hepatite C (HCV) é considerada um grande problema de saúde pública, desde a sua descoberta em 1989. Entretanto a terapia mais utilizada atualmente, baseada no uso de Peginterferon, tem sucesso em aproximadamente 50% dos pacientes com o genótipo 1. Embora os mecanismos envolvidos nesta resistência viral ainda não sejam esclarecidos, sugere-se que fatores virais e do hospedeiro participam deste. A proteína não-estrutural 5A (NS5A) está envolvida em diversos processos celulares e é um componente essencial para o HCV. Entretanto, sua estrutura e função ainda não foram bem elucidadas. A partir destes fatos, os objetivos do presente estudo foram elaborar um modelo teórico da NS5A e investigar as propriedades estruturais e funcionais in silico. Foram analisadas 345 sequências da proteína NS5A do HCV de 23 pacientes infectados com o genótipo 1 ou 3. As composições de aminoácidos e de estrutura secundária demonstraram que há diferença entre os genótipos, podendo indicar que há diferenças nas interações proteína-proteína entre os genótipos, o que pode estar relacionado com a diferença da taxa de resistência ao tratamento. A análise funcional foi realizada com o ProtFun, que sugeriu que a NS5A estaria envolvida nas funções celulares de metabolismo intermediário central, tradução, crescimento, tranporte, ligação e hormônio. Estas funções variaram entre os domínios, suportando a hipótese de que a NS5A é uma proteína multifuncional. A análise pelo PROSITE indicou vários sítios de glicosilação, fosforilação e miristoilação, que são altamente conservados e podem ter função importante na estabilização da estrutura e função, sendo assim possíveis alvos de novos antivirais. Alguns deles estão em regiões relacionadas com a resposta ao tratamento. Outro... (Resumo completo, clicar acesso eletrônico abaixo)<br>Abstract: Hepatitis C virus (HCV) infects almost 3% of people worldwide and it is considered the main cause of liver chronic diseases and transplants. Until today, there is no effective vaccine and the current most used therapy, based on Peginterferon, is successful only in 50% of patients infected by genotype 1. Although the outcomes of this treatment resistance are unclear, it is suggested host and virus factors may participate in this mechanism. Non-structural 5A (NS5A) protein is involved in several cellular and virus processes and it is a critical component of HCV. However, its structure and function are still uncertain. Regarding these facts, the present study attachments were to elaborate a model of the NS5A protein and to investigate NS5A structural and functional features, using in silico tools. It was analyzed 345 sequences of HCV NS5A protein from 23 patients infected by genotypes 1 or 3. Residues and secondary structure composition of all sequences demonstrated that there are differences between genotypes. It may indicate that there are differences in interactions between genotypes, which could be related with the distinct average of treatment resistance. In addition, among those that varied between genotypes, there were amino acids in regions that studies suggested as related with virus persistence. Functional analysis was performed with ProtFun. It suggested that NS5A is involved with central intermediary metabolism, translation, growth, transport, ligation and hormone functions in the cell. These functions vary between the domains, strengthening the hypothesis that NS5A is a multifunctional protein. Prosite motif search indicated that there are many glicosilation, fosforilation and myristoilation sites, which are highly conserved and may play an important role in structural stabilization and... (Complete abstract click electronic access below)<br>Orientador: Paula Rahal<br>Coorientador: Helen Andrade Arcuri<br>Banca: Fernanda Canduri<br>Banca: Carlos Alberto Montanari<br>Mestre
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Acencio, Marcio Luis. "Construção e análise da rede integrada de interações entre genes humanos envolvida com a regulação da trnsição G1/S do ciclo celular plea adesão à matriz extracelular /." Botucatu : [s.n.], 2011. http://hdl.handle.net/11449/102703.

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Orientador: Ney Lemke<br>Banca: José Carlos Merino Mombach<br>Banca: Marcos Roberto de Mattos Fontes<br>Banca: Tie Koide<br>Banca: Deilson Elgui de Oliveira<br>Resumo: Virtualmente, todas as células normais, com exceção das células hematopoieticas, precisam estar aderi das à matriz extracelular para que elas possam se proliferar. Na ausência de adesão, essas células não se proliferam mais e acabam sofrendo apoptose. Porém, após transformação oncogênica, as células adquirem a capacidade de proliferação na ausência de adesão à matriz extracelular. Essa capacidade, cuja base molecular está na regulação anormal da transição G tiS do ciclo celular pela adesão, é uma das propriedades fundamentais das células cancerosas e também requisito para que essas células adquiriam sua capacidade metastática. Como as metástases correspondem a aproximadamente 90% das mortes por câncer, a elucidação dos mecanismos moleculares subjacentes à regulação da transição G IIS do ciclo celular pela adesão à matriz extracelular é, portanto, essencial para o desenvolvimento de drogas que possam inibir a formação das metástases. Com o intuito de elucidar esses mecanismos, nós adotamos neste trabalho uma abordagem estritamente computacional baseada em teoria das redes e aprendizado de máquina através do desenvolvimento de novos métodos de (i) construção de redes que representam a provável regulação entre dois diferentes processos (nesse caso, regulação da transição G l/S pela adesão à matriz extracelular), (á) predição de interações oncogênicas, (iii) determinação de sub-redes de vias de sinalização oncogênica entre dois genes de interesse em uma rede (batizado de graph2sig) e (iv) predição de potenciais alvos de drogas. A rede potencialmente envolvida na regulação da transição G l/S do ciclo celular pela matriz extracelular construída (Gccam) possui ~ 2000 genes e ~ 20.000 interações e representa ~ 78% dos processos biológicos conhecidamente envolvidos nessa regulação... (Resumo completo, clicar acesso eletrônico abaixo)<br>Abstract: Virtually all normal cells, excluding the hematopoietic cells, require anchorage to the extracellular matrix for their proliferation and survival. When such cells are deprived of anchorage, they arrest in the G1 phase of the cell cycle and eventually undergo apoptosis. Cancer cells, on the other hand, acquire the ability to perform anchorage-independent proliferation as a result of the disruption of the regulation of the G1/S cell cycle transition by adhesion to extracellular matrix. Anchorage-independent proliferation is the foundation for tumorigenicity and metastatic capability of cancer cells. As metastases are the cause of 90% of human cancer deaths, it is crucial to decipher the molecular mechanisms underlying the regulation of the G1/S cell cycle transition by the adhesion to extracellular cell matrix. In order to decipher such mechanisms, we developed in this present work machine learning and graph theory-based computational methods for the (i) construction of networks representing the regulatory relationships between two biological processes of interest, (ii) prediction of oncogenic interactions, (iii) extraction of oncogenic signaling subnetworks between two genes and (iv) prediction of druggable genes. The network representing the regulatory relationships between G1/S cell cycle transition and adhesion to extracellular matrix, Gccam, is comprised by 2,000 genes and 20,000 interactions. Moreover, 78% of known biological process involved in the regulation of G1/S cell cycle transition by adhesion to extracellular matrix are embedded in Gccam. Through the prediction of oncogenic interactions and the extraction of oncogenic signaling subnetworks between EGFR and CDC6, genes that encode proteins likely to be relevant to anchorage-independent proliferation, we postulate the following hypotheses for the molecular mechanisms underlying the anchorage-independent proliferation: cancer... (Complete abstract click electronic access below)<br>Doutor
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Gustafsson, Johan. "Finding potential electroencephalography parameters for identifying clinical depression." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256392.

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This master thesis report describes signal processing parameters of electroencephalography (EEG) signals with a significant difference between the signals from the animal model of clinical depression and the non-depressed animal model. The signal from the depressed model had a weaker power in gamma (30 - 80 Hz) than the non-depressed model during awake and it had a stronger power in delta (1.5 - 4 Hz) during sleep. The report describes the process of using visualisation to understand the shape of the signal which helps with interpreting results and helps with the development of parameters. A generic tool for time-frequency analysis was improved to cope with the size of the weeklong EEG dataset. A method for evaluating the quality of how well the EEG parameters are able to separate the strains with as short recordings as possible was developed. This project shows that it is possible to separate an animal model of depression from an animal model of non-depression based on its EEG and that EEG-classifiers may work as indicative classifiers for depression. Not a lot of data is needed. Further studies are needed to verify that the results are not overly sensitive to recording setup and to study to what extent the results are translational. It might be some of the EEG parameters with significant differences described here are limited to describe the difference between the two strains FSL and SD. But the classifiers have reasonable biological explanations that makes them good candidates for being translational EEG-based classifiers for clinical depression.
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Martin, Paul. "Post-GWAS bioinformatics and functional analysis of disease susceptibility loci." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/postgwas-bioinformatics-and-functional-analysis-of-disease-susceptibility-loci(cc0e6cee-5c32-4b75-b3d3-f7c18b6f126d).html.

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Genome-wide association studies (GWAS) have been tremendously successful in identifying genetic variants associated with complex diseases, such as rheumatoid arthritis (RA). However, the majority of these associations lie outside traditional protein coding regions and do not necessarily represent the causal effect. Therefore, the challenges post-GWAS are to identify causal variants, link them to target genes and explore the functional mechanisms involved in disease. The aim of the work presented here is to use high level bioinformatics to help address these challenges. There is now an increasing amount of experimental data generated by several large consortia with the aim of characterising the non-coding regions of the human genome, which has the ability to refine and prioritise genetic associations. However, whilst being publicly available, manually mining and utilising it to full effect can be prohibitive. I developed an automated tool, ASSIMILATOR, which quickly and effectively facilitated the mining and rapid interpretation of this data, inferring the likely functional consequence of variants and informing further investigation. This was used in a large extended GWAS in RA which assessed the functional impact of associated variants at the 22q12 locus, showing evidence that they could affect gene regulation. Environmental factors, such as vitamin D, can also affect gene regulation, increasing the risk of disease but are generally not incorporated into most GWAS. Vitamin D deficiency is common in RA and can regulate genes through vitamin D response elements (VDREs). I interrogated a large, publicly available VDRE ChIP-Seq dataset using a permutation testing approach to test for VDRE enrichment in RA loci. This study was the first comprehensive analysis of VDREs and RA associated variants and showed that they are enriched for VDREs, suggesting an involvement of vitamin D in RA.Indeed, evidence suggests that disease associated variants effect gene regulation through enhancer elements. These can act over large distances through physical interactions. A newly developed technique, Capture Hi-C, was used to identify regions of the genome which physically interact with associated variants for four autoimmune diseases. This study showed the complex physical interactions between genetic elements, which could be mediated by regions associated with disease. This work is pivotal in fully characterising genetic associations and determining their effect on disease. Further work has re-defined the 6q23 locus, a region associated with multiple diseases, resulting in a major re-evaluation of the likely causal gene in RA from TNFAIP3 to IL20RA, a druggable target, illustrating the huge potential of this research. Furthermore, it has been used to study the genetic associations unique to multiple sclerosis in the same region, showing chromatin interactions which support previously implicated genes and identify novel candidates. This could help improve our understanding and treatment of the disease. Bioinformatics is fundamental to fully exploit new and existing datasets and has made many positive impacts on our understanding of complex disease. This empowers researchers to fully explore disease aetiology and to further the discovery of new therapies.
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Anwar, Maryam. "A bioinformatics approach to building an otic gene regulatory network." Thesis, King's College London (University of London), 2016. https://kclpure.kcl.ac.uk/portal/en/theses/a-bioinformatics-approach-to-building-an-otic-gene-regulatory-network(506591df-8f55-4585-a7a1-86d18d65af1a).html.

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During development, the coordinated and sequential action of signals and regulatory factors controls how cells become different from each other and acquire specific fates. This information can be integrated in gene regulatory networks (GRNs) that model these processes over time and consider temporal and spatial changes of gene expression and how these are regulated. During early development, vertebrate sensory organs arise from the pre-placodal region at the border of the neural plate. Subsequently, FGF signalling plays a crucial role in inducing otic-epibranchial progenitors that ultimately give rise to the otic and epibranchial placodes. Downstream of FGF signalling, many transcription factors are activated. However, their regulatory relationships are not very clear. This project uses a bioinformatics approach to establish a GRN to model how multipotent progenitors transit through sequential regulatory states until they are committed to the ear lineage. To this end, using systematic perturbation experiments, new ear-specific genes have been identified some of which respond early to FGF. Focussing on these early genes, I have used phylogenetic footprinting combined with histone ChIP-seq to identify novel enhancers. Subsequently, I have investigated transcription factor binding sites within these enhancers to identify a small group of common regulators. In parallel, using mRNA-seq and perturbation data, I have reverse-engineered GRNs that recapitulate known interactions and predict new ones. Using a combination of these approaches, I have ultimately enriched a preliminary literature-based GRN by placing otic genes and their interactions into a hierarchy. Thus, this network is a resource for identifying key otic regulators and their targets and provides guidelines for future experiments.
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Martínez, Fundichely Alexander 1978. "Bioinformatic characterization and analysis of polymorphic inversions in the human genome." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/384837.

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Within the great interest in the characterization of genomic structural variants (SVs) in the human genome, inversions present unique challenges and have been little studied. This thesis has developed "GRIAL", a new algorithm focused specifically in detect and map accurately inversions from paired-end mapping (PEM) data, which is the most widely used method to detect SVs. GRIAL is based on geometrical rules to cluster, merge and refine both breakpoints of putative inversions. That way, we have been able to predict hundreds of inversions in the human genome. In addition, thanks to the different GRIAL quality scores, we have been able to identify spurious PEM-patterns and their causes, and discard a big fraction of the predicted inversions as false positives. Furthermore, we have created â ˘ AIJInvFESTâ˘A˙I, the first database of human polymorphic inversions, which represents the most reliable catalogue of inversions and integrates all the associated information from multiple sources. Currently, InvFEST combines information from 30 different studies and contains 1092 candidate inversions, which are categorized based on internal scores and manual curation. Finally, the analysis of all the data generated has provided information on the genomic patterns of inversions, contributing decisively to the understanding of the map of human polymorphic inversions.<br>Dentro del estudio de las variantes estructurales en el genoma humano, las inversiones han sido las menos han consolidado sus resultados y constituye uno de los principales retos en la actualidad. Esta tesis aborda el tema a través de la implementación de "GRIAL" un nuevo algoritmo específicamente diseñado para la detección más precisa posible de las inversiones usando el mapeo de secuencias apareadas (del inglés PEM) que es el método más utilizado para estudiar la variación estructural. GRIAL se basa en reglas geométricas para agrupar los patrones de PEM que señalan un posible punto de rotura (del inglés breakpoint) de inversión, además une cada breakpoint correspondientes a inversiones independientes y refina lo más exacto posible su localización. Su uso nos permitió predecir cientos de inversiones. Un gran aporte de nuestro método es la creación de índices (del inglés score) de fiabilidad para las predicciones mediante los cuales identificamos patrones de inversión incorrectos y sus causas. Esto nos permitió filtrar nuestro resultado eliminando un gran número de predicciones posiblemente falsas. Además se creó "InvFEST", la primera base de datos especialmente dedicada a inversiones polimórficas en el genoma humano la cual representa el catálogo más fiable de inversiones, integrando además a cada inversión conocida la información asociada disponible. Actualmente InvFEST contiene (y mantiene la clasificación según el nivel de certeza) un catálogo de 1092 inversiones clasificadas, a partir de datos de 30 estudios diferentes. Finalmente el análisis de toda la información generada nos permitió describir algunos patrones de las inversiones polimórficas en el genoma humano contribuyendo de este modo a la comprensión de esta variante estructural y el estado de su información en los estudios del genoma humano.<br>Inversió genòmica
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Codicè, Francesco. "Rete neurale per la predizione end-to-end dello stato di ossidazione delle cisteine e la connettività dei ponti disolfuro." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20593/.

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Le proteine sono macromolecole fondamentali in moltissimi processi biologici essenziali per gli organismi viventi e diverse sono le funzioni che possono svolgere: possono ad esempio fungere da anticorpi per proteggere gli organismi da patogeni esterni oppure possono avere ruoli di natura strutturale. Le proteine sono costituite da catene di aminoacidi che ne determinano la forma, ossia il modo in cui una proteina si ripiega, che a sua volta determina la funzione svolta. Fra i vari fattori che hanno importanza nella conformazione delle proteine vi è l'aminoacido cisteina . Esso ha come singolarità funzionale il fatto di poter formare forti legami chiamati ponti disolfuro . I ponti disolfuro hanno un importante ruolo a livello sia strutturale che funzionale nelle proteine. Questi legami covalenti si formano per l'ossidazione di coppie di cisteine . Essendo questi legami particolarmente interessanti, sono stati sviluppati diversi metodi computazionali in silico per la predizione, data la sequenza relativa ad una proteina, delle cisteine coinvolte in questi legami. Il problema è comunemente affrontato con due approcci: nel primo, data la sequenza di aminoacidi di una proteina, si predice lo stato di ossidazione delle cisteine, ossia si predice binariamente per ogni cisteina se é coinvolta o meno in un ponte disolfuro; nel secondo approccio si predice lo schema di connettività delle cisteine ovvero si predicono quali sono le coppie di cisteine legate da ponti disolfuro. In questa tesi si descrive la costruzione di una rete neurale basata sull'approccio del multitask learning , in altre parole si tratta dell'addestramento di un modello per effettuare predizioni diverse contemporaneamente, sfruttando la condivisione di parte dei parametri del modello. E' descritta la costruzione di una rete neurale multitask per la predizione in un unico modello dello stato di ossidazione delle cisteine e dello schema di connettività delle stesse, partendo dalla sequenza di aminoacidi.
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Duck, Geraint. "Extraction of database and software usage patterns from the bioinformatics literature." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/extraction-of-database-and-software-usage-patterns-from-the-bioinformatics-literature(fac16cb8-5b5b-4732-b7af-77a41cc64487).html.

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Method forms the basis of scientific research, enabling criticism, selection and extension of current knowledge. However, methods are usually confined to the literature, where they are often difficult to find, understand, compare, or repeat. Bioinformatics and computational biology provide a rich opportunity for resource creation and discovery, with a rapidly expanding "resourceome". Many of these resources are difficult to find due to the large choice available, and there are only a limited number of sufficiently populated lists that can help inform resource selection. Text mining has enabled large scale data analysis and extraction from within the scientific literature, and as such can provide a way to help explore the vast wealth of resources available, which form the basis of bioinformatics methods. As such, this thesis aims to survey the computational biology literature, using text mining to extract database and software resource name mentions. By evaluating the common pairs and patterns of usage of these resources within such articles, an abstract approximation of the in silico methods employed within the target domain is developed. Specifically, this thesis provides an analysis of the difficulties of resource name extraction from the literature, then using this knowledge to develop bioNerDS - a rule-based system that can detect database and software name mentions within full-text documents (with a final F-score of 67%). bioNerDS is then applied to the full-text document corpus from PubMed Central, the results of which are then explored to identify the differences in resource usage between different domains (bioinformatics, biology and medicine) through time, different journals and different document sections. In particular, the well established resources (e.g., BLAST, GO and GenBank) remain pervasive throughout the domains, although they are seeing a slight decline in usage. Statistical programs see high levels of usage, with R in bioinformatics and SPSS in medicine being frequently mentioned throughout the literature. An overview of the common resource pairs has been generated by pairing database and software names which directly co-occur after one another in text. Combining and aggregating these resource pairs together across the literature enables the generation of a network of common resource patterns within computational biology, which provides an abstract representation of the common in silico methods used. For example, sequence alignment tools remain an important part of several computational biology analysis pipelines, and GO is a strong network sink (primarily used for data annotation). The networks also show the emergence of proteomics and next generation sequencing resources, and provide a specialised overview of a typical phylogenetics method. This work performs an analysis of common resource usage patterns, and thus provides an important first step towards in silico method extraction using text-mining. This should have future implications in community best practice, both for resource and method selection.
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Nambiar, Kate. "Bioinformatic analysis of peptide microarray immunoassay data for serological diagnosis of infectious diseases." Thesis, University of Brighton, 2017. https://research.brighton.ac.uk/en/studentTheses/de2be38a-5941-4bc5-adb9-1c200b07c193.

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Understanding antibody - antigen interactions occurring in infectious diseases is important in understanding aetiology, can help facilitate diagnosis, and could offer potential targets for vaccine or therapeutic antibody development. Peptide arrays – collections of short peptides immobilised on solid planar supports – offer a high throughput and highly parallel method of identifying immunogenic epitopes and relating patterns of antibody identification to clinical disease states. As technology advances, so the density and complexity of peptide arrays of becomes ever higher. Managing the large volume of data that modern high density microarrays generate requires sophisticated bioinformatics in order to minimise errors and biases. In this thesis I introduce a new software package, pmpa, that uses R, the open source statistical programming platform and an object orientated framework from the Bioconductor project. The package facilitates analysis of peptide microarray data including functions for reading scanned data files, quality assessment and pre-processing. It is both flexible and modular – integrating with existing software in the Bioconductor repository. Data pre-processing is key to any microarray analysis. Noise due to technical variation can obscure true biological effects if careful steps are not taken. The aim of pre-processing is to minimise noise while preserving biological variation. No consensus exists as to the optimal method of pre-processing making comparison between studies difficult. This thesis explores two key aspects of pre-processing: background correction and normalisation using two experimental datasets – a titration series of a monoclonal anti C.difficle Toxin B monoclonal antibody, and dataset with an anti-Toxin A antibody spiked into non immune sera to examine biases introduced by the pre-processing and whether they improve measures such as precision and differential identification. Finally the analysis method is applied to two studies identifying antibody signatures in infectious diseases: the first investigating immune responses to C. difficile – a major hospital acquired infection and the leading identifiable cause of antibiotic associated diarrhoea, and the second characterising antibody signatures that define paediatric tuberculosis infection. The real world application of the methodology identifies signatures of immune responses characterising clinical disease eg. relapsing vs. single episode C. difficile infection, but also highlights a number of limitations of the technique such as batch confounding and response variability.
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Xiang, Jie. "Functional genomic study and bioinformatic analysis on natural bioactive peptides." Thesis, Queen's University Belfast, 2017. https://pure.qub.ac.uk/portal/en/theses/functional-genomic-study-and-bioinformatic-analysis-on-natural-bioactive-peptides(4c3d14a3-7484-463b-8c82-cdc9abc5052e).html.

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An extraordinary diverse components of animal toxins have emerged along with the long-term evolution of animals, which have unique functions that protect them to survive in the wild. Specifically, the granular glands of amphibians contain multiple chemical compounds, among which, peptides are one of the major types of the constituents. This thesis is divided into six chapters. Chapter 1, as general introduction, describes the detailed background on peptide-based therapeutic agents, the promising therapeutic application of animal toxins, and a brief review on anuran skins and their secretions. Chapter 2 presents the materials and methods employed in this project. In chapter 3, a novel bradykinin-related peptide was isolated and identified from the skin secretion of odorrana livida using shotgun cloning and tandem mass spectrometry fragmentation sequencing approach. This peptide exhibited a dose-dependent contractile property on rat bladder and ileum and increased the contraction frequency on rat uterus ex vivo smooth muscle preparations. It also showed vasorelaxant activity on rat tail artery smooth muscle. In addition, the peptide was modified by substituting the penultimate amino acid in the amino terminus from phenylalanine to leucine. Theanalogue completely abolished parental peptide activity, but showed an inhibition effects on bradykinin-induced rat tail artery smooth muscle relaxation. By using specific antagonists for bradykinin B1 and B2 receptors, we found that bradykinin b2receptor is highly likely to be involved in the rat tail artery related effects caused bythis novel bradykinin-related peptide and its analogue. Chapter 4 and 5 are about thediscovery of two pairs of novel antimicrobial peptides belonging to Bombinin and VIBombinin H families, respectively, from the skin secretion of Bombina genus. In chapter 4, the sequence modification was applied on bombinin HL by replacing Lisomer-leucine to D-isomer leucine from the second position of the amino terminus. Both the wild type and modified peptides displayed well-defined α-helical structure in bacterial membrane mimicking environment. BHL-bombinin displayed broad-spectrum bactericidal activity against a wide range of microorganisms, while bombinin H only exhibited a mild bacteriostatic effect on gram positive bacteria. The synergistic antimicrobial effects were observed between BHL-bominin with bombininH and between bombinin H with ampicillin. In addition, haemolytic and cytotoxic examination exhibited a highly synergistic selectivity and low cytotoxicity on mammalian cells of these three peptides. In chapter 5, the sequence modification was employed in the BHK-bombinin by replacing the glutamic acid with lysine at the 23rdposition from amino terminus, which increased the net charge and expanded thenonpolar face of the original peptide. According to the results from in vitro function alanalysis, this modification strategy significantly improved the selectivity index of thepeptide with increased antimicrobial activity and decreased haemolysis activity. The combined antimicrobial evaluation on both natural and modified peptides showed synergistic inhibition activity against both gram positive bacteria and fungi yeast. In summary, this thesis reveals the combined strategy of using a molecular cloning technique and mass spectrometric method for novel host-defensive peptides identification from amphibian skin secretions. In vitro and ex vivo functionalevaluations were subsequently employed which not only bring us a better understanding on the diversity of natural sourced bioactive peptides but also emphasized the research value for characterizing their in depth mechanisms.
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Books on the topic "Bioinformatics. eng"

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1961-, Lässig M., and Valleriani A, eds. Biological evolution and statistical physics. Springer-Verlag, 2002.

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(Editor), M. Lässig, and A. Valleriani (Editor), eds. Biological Evolution and Statistical Physics (Lecture Notes in Physics). Springer, 2002.

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Biological evolution and statistical physics. Springer Berlin Heidelberg, 2002.

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Book chapters on the topic "Bioinformatics. eng"

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Platts, Adrian E., and Stephen A. Krawetz. "Tools and Approaches for an End-to-End Expression Array Analysis." In Bioinformatics for Systems Biology. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-440-7_13.

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Al-Jumeily, Dhiya, Shamaila Iram, Abir Jaffar Hussain, Vialatte Francois-Benois, and Paul Fergus. "Early Detection Method of Alzheimer’s Disease Using EEG Signals." In Intelligent Computing in Bioinformatics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09330-7_4.

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Crasto, Nieves, and Richa Upadhyay. "Wavelet Decomposition Based Automatic Sleep Stage Classification Using EEG." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56148-6_45.

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Sedjelmaci, Ibticeme, and Fethi Bereksi Reguig. "Some False ECG Waves Detections Revised by Fractal Dimensions." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78759-6_29.

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Álvarez, Pedro, Francisco J. Romero, Antonio García, Luis Parrilla, Encarnación Castillo, and Diego P. Morales. "Classification Algorithms for Fetal QRS Extraction in Abdominal ECG Signals." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56148-6_47.

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Moriyama, Takuya, Yuichi Shiraishi, Kenichi Chiba, Rui Yamaguchi, Seiya Imoto, and Satoru Miyano. "OVarCall: Bayesian Mutation Calling Method Utilizing Overlapping Paired-End Reads." In Bioinformatics Research and Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38782-6_4.

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Boulnemour, Imen, Bachir Boucheham, and Slimane Benloucif. "Improved Dynamic Time Warping for Abnormality Detection in ECG Time Series." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31744-1_22.

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Pérez-Tirador, Pablo, Gabriel Caffarena, Constantino A. García, Abraham Otero, Rafael Raya, and Rodrigo Garcia-Carmona. "Hardware Accelerator to Compute the Minimum Embedding Dimension of ECG Records." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31744-1_23.

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He, Liang-hua, and Bin Liu. "Motor Imagery EEG Signals Analysis Based on Bayesian Network with Gaussian Distribution." In Intelligent Computing in Bioinformatics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09330-7_29.

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Escobar, Juan José, Julio Ortega, Jesús González, and Miguel Damas. "Assessing Parallel Heterogeneous Computer Architectures for Multiobjective Feature Selection on EEG Classification." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31744-1_25.

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Conference papers on the topic "Bioinformatics. eng"

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Fando, Roman, and Maria Klavdieva. "Bioinformatics: Past and Present." In 2018 International Conference on Engineering Technologies and Computer Science (EnT). IEEE, 2018. http://dx.doi.org/10.1109/ent.2018.00013.

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Fando, Roman. "The history of bioinformatic in Russia." In 2020 International Conference Engineering Technologies and Computer Science (EnT). IEEE, 2020. http://dx.doi.org/10.1109/ent48576.2020.00022.

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Skapura, Nicholas, and Guozhu Dong. "Distribution skew-based binning: Towards mining highly discriminative patterns from EEG/EMG time series." In 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2015. http://dx.doi.org/10.1109/bibe.2015.7367635.

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Zhou, Yan-Bo, Shi-Min Cai, Tao Zhou, and Pei-Ling Zhou. "MP-Based Method on Detecting and Eliminating the Synchronous ECG Artifacts in the EEG Signals." In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5516334.

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Liu, Bo, Ying Wei, Yu Zhang, and Qiang Yang. "Deep Neural Networks for High Dimension, Low Sample Size Data." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/318.

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Deep neural networks (DNN) have achieved breakthroughs in applications with large sample size. However, when facing high dimension, low sample size (HDLSS) data, such as the phenotype prediction problem using genetic data in bioinformatics, DNN suffers from overfitting and high-variance gradients. In this paper, we propose a DNN model tailored for the HDLSS data, named Deep Neural Pursuit (DNP). DNP selects a subset of high dimensional features for the alleviation of overfitting and takes the average over multiple dropouts to calculate gradients with low variance. As the first DNN method applied on the HDLSS data, DNP enjoys the advantages of the high nonlinearity, the robustness to high dimensionality, the capability of learning from a small number of samples, the stability in feature selection, and the end-to-end training. We demonstrate these advantages of DNP via empirical results on both synthetic and real-world biological datasets.
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"Involving End-users in Domain-Specific Languages Development - Experiences from a Bioinformatics SME." In 8th International Conference on Evaluation of Novel Software Approaches to Software Engineering. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004450000970108.

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Hong, Jia-Hua, Ming-Chun Liang, Ming-Yang Haung, Tsung-Heng Tsai, Qiang Fang, and Shuenn-Yuh Lee. "Analog front-end circuit with low-noise amplifier and high-pass sigma-delta modulator for an EEG or ECoG acquisition system." In 2011 International Symposium on Bioelectronics and Bioinformatics (ISBB). IEEE, 2011. http://dx.doi.org/10.1109/isbb.2011.6107634.

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Alqahtani, Fehaid, Stamos Katsigiannis, and Naeem Ramzan. "On the use of ECG and EMG Signals for Question Difficulty Level Prediction in the Context of Intelligent Tutoring Systems." In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2019. http://dx.doi.org/10.1109/bibe.2019.00077.

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Lu, Xudong, Huilong Duan, and Huiying Zheng. "XML-ECG: An XML-Based ECG Presentation for Data Exchanging." In 2007 1st International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icbbe.2007.295.

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Ellawala, Nadun, and Subramaniam Thayaparan. "Hardware Implementation of EEG Classifier Using LDA." In 2019 2nd International Conference on Bioinformatics, Biotechnology and Biomedical Engineering (BioMIC) - Bioinformatics and Biomedical Engineering. IEEE, 2019. http://dx.doi.org/10.1109/biomic48413.2019.9034742.

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