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Relevant bibliographies by topics / Bioinformatics, Medicine

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Academic literature on the topic 'Bioinformatics, Medicine'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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Journal articles on the topic "Bioinformatics, Medicine"

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Borodin, E. A. "PERSONIFIED MEDICINE AND BIOINFORMATICS." Amur Medical Journal, no. 4 (2018): 77–80. http://dx.doi.org/10.22448/amj.2018.4.77-80.

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Kim, Ju Han. "Bioinformatics and genomic medicine." Genetics in Medicine 4 (December 2002): 62S—65S. http://dx.doi.org/10.1097/00125817-200211001-00013.

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Anand, Sneh, and Jayashree Santhosh. "Bioinformatics in Clinical Medicine." IETE Technical Review 18, no. 4 (2001): 253–61. http://dx.doi.org/10.1080/02564602.2001.11416971.

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Fernald, Guy Haskin, Emidio Capriotti, Roxana Daneshjou, Konrad J. Karczewski, and Russ B. Altman. "Bioinformatics challenges for personalized medicine." Bioinformatics 27, no. 13 (2011): 1741–48. http://dx.doi.org/10.1093/bioinformatics/btr295.

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Fernald, G. H., E. Capriotti, R. Daneshjou, K. J. Karczewski, and R. B. Altman. "Bioinformatics challenges for personalized medicine." Bioinformatics 27, no. 16 (2011): 2323. http://dx.doi.org/10.1093/bioinformatics/btr408.

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Chang, I.-Shou, Ping-Chiang Lyu, Jenn-Kang Hwang, H. Sunny Sun, and Chao A. Hsiung. "Taiwan Bioinformatics Institute and the GMBD Bioinformatics Core." Asia-Pacific Biotech News 10, no. 24 (2006): 1432–34. http://dx.doi.org/10.1142/s0219030306002175.

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Bayat, A. "Science, medicine, and the future: Bioinformatics." BMJ 324, no. 7344 (2002): 1018–22. http://dx.doi.org/10.1136/bmj.324.7344.1018.

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Torres, Angela, and Juan J. Nieto. "Fuzzy Logic in Medicine and Bioinformatics." Journal of Biomedicine and Biotechnology 2006 (2006): 1–7. http://dx.doi.org/10.1155/jbb/2006/91908.

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The purpose of this paper is to present a general view of the current applications of fuzzy logic in medicine and bioinformatics. We particularly review the medical literature using fuzzy logic. We then recall the geometrical interpretation of fuzzy sets as points in a fuzzy hypercube and present two concrete illustrations in medicine (drug addictions) and in bioinformatics (comparison of genomes).

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Gu, P., and H. Chen. "Modern bioinformatics meets traditional Chinese medicine." Briefings in Bioinformatics 15, no. 6 (2013): 984–1003. http://dx.doi.org/10.1093/bib/bbt063.

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Butte, Atul J. "Translational bioinformatics applications in genome medicine." Genome Medicine 1, no. 6 (2009): 64. http://dx.doi.org/10.1186/gm64.

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Dissertations / Theses on the topic "Bioinformatics, Medicine"

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Stetson, Lindsay C. "Computational Approaches for Cancer Precision Medicine." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1428050439.

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Tong, Dong Ling. "Genetic algorithm-neural network : feature extraction for bioinformatics data." Thesis, Bournemouth University, 2010. http://eprints.bournemouth.ac.uk/15788/.

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With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of various preprocessing techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the integrity of the findings has been compromised by the improper use of techniques and the ill-conceived objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data.

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Papke, Todd Alan. "Personalized audio warning alerts in medicine." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/1378.

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Modern Electronic Health Record (EHR) systems are now integral to healthcare. Having evolved from hospital billing and laboratory systems in the 80's, EHR systems have grown considerably as we learn to represent more and more aspects of patient encounter, diagnosis and treatment digitally. EHR user interfaces, however, lag considerably behind their consumer-electronics counterparts in usability, most notably with respect to customizability. This limitation is especially evident in the implementation of audible alerts that are coupled to sensors or timing devices in intensive-care settings. The most current standard, (ISO/IEC 60601-1-8) has been designed for alerts that are intended to signal situations of varying priorities: however, it is not universally implemented, and has been criticized for the difficulty that healthcare providers have in discriminating between individual alarms, and for the failure to incorporate prior research with respect to "sense of urgency" as it applies to alarm efficacy. In the present work, however, we consider that there are more effective means to allow a user to identify an alarm correctly than "sense of urgency" response. This thesis focuses on the problem of correct identification of alerts: what happens when a human subject is allowed to create or designate (i.e., personalize) one's own alerts? Given the ubiquity, low costs and commoditization of consumer-electronics devices, we believe that it is just a matter of time before such devices become the norm in critical care and replace existing, special-purpose devices for information delivery at the point of patient care. We built a tool, PASA (Personalized Alert Study Application), that would allow us to capture and edit sounds and orchestrate studies that would contrast any two types of sounds. PASA facilitated a study where study participant's responses to "personalized" sounds were contrasted with sounds that meet the ISO/IEC 60601-1-8:2012 standard. We performed two sub-studies that contrasted responses to two banks of 6-alerts and 10-alerts. The 6-alert study was repeated with the same subjects after two weeks without training to measure recall. We observed that accuracy, reaction time, and retention were significantly improved with the personalized sounds. For example, the median errors for the 6-alert baseline study were 4 for personalized vs. 27 for standard alerts. For the 6-alert repeat study it was 7 vs. 43. The median for the 10-alert study was 1 for personalized vs. 55 for standard alerts. Accuracy for recognition, while remaining constant for personalized alerts, degraded considerably for standardized alerts as the number of alerts increased from 6 to 10. We conclude that personalization of alerts may improve information delivery and reduce cognitive overload on the health care provider. This potential positive effect at the point of patient care merits further studies in a clinical or simulated clinical setting.

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Yu, Jennifer. "Bioinformatics Analysis of Vasorin in Gliomas." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1484927314447688.

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Swenson, Hugo. "Detection of artefacts in FFPE-sample sequence data." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392623.

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Next generation sequencing is increasingly used as a diagnostic tool in the clinical setting. This is driven by the vast increase in molecular targeted therapy, which requires detailed information on what genetic variants are present in patient samples. In the hospital setting, most cancer diagnostics are based on Formalin Fixed Paraffin Embedded (FFPE) samples. The FFPE routine is very beneficial for logistical purposes and for some histopathological analyses, but creates problems for molecular diagnostics based on DNA. These problems derive from sample immersion informalin, which results in DNA fragmentation, interstrand DNA crosslinking and sequence artefacts due to hydrolytic deamination. Distinguishing such artefacts from true somatic variants can be challenging, thus affecting both research and clinical analyses. In order to identify FFPE-artefacts from true variants in next generation sequencing data from FFPE samples, I developed the novelprogram FUSAC (FFPE tissue UMI based Sequence Artefact Classifier) for the facility Clinical Genomics in Uppsala. FUSAC utilizes UniqueMolecular Identifiers (UMI's) to identify and group sequencing reads based on their molecule of origin. By using UMI's to collapse duplicate paired reads into consensus reads, FFPE-artefacts are classified through comparative analysis of the positive and negative strand sequences. My findings indicate that FUSAC can succesfully classify UMI-tagged next generation sequencing reads with FFPE-artefacts, from sequencing reads with true variants. FUSAC thus presents a novel approach in bioinformatic pipelines for studying FFPE-artefacts.

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Kronk, Clair Artemis. "Gender, Sex, and Sexual Orientation in Medicine: A Linguistic Analysis." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617107411106107.

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Sathirapongsasuti, Jarupon Fah. "Post-Genomic Approaches to Personalized Medicine: Applications in Exome Sequencing, Microbiome, and COPD." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11574.

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Since the completion of the sequencing of the human genome at the turn of the century, genomics has revolutionized the study of biology and medicine by providing high-throughput and quantitative methods for measuring molecular activities. Microarray and next generation sequencing emerged as important inflection points where the rate of data generation skyrocketed. The high dimensionality nature and the rapid growth in the volume of data precipitated a unique computational challenge in massive data analysis and interpretation. Noise and signal structure in the data varies significantly across types of data and technologies; thus, the context of the data generation process itself plays an important role in detecting key and oftentimes subtle signals. In this dissertation, we discuss four areas where contextualizing the data aids discoveries of disease-causing variants, complex relationships in the human microecology, interplay between gene and environment, and genetic regulation of gene expression. These studies, each in its own unique way, have helped made possible discoveries and expanded the horizon of our understanding of the human body, in health and disease.

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Yako, Yandiswa. "Bioinformatics-based strategies to identify PFHBII-causing and HCM main locus and/or HCM modifying mutations." Thesis, Stellenbosch : University of Stellenbosch, 2004. http://hdl.handle.net/10019.1/16473.

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Thesis (MSc)--University of Stellenbosch, 2004.<br>ENGLISH ABSTRACT: Progressive familial heart block type II (PFHBII) is an inherited cardiac conduction disorder of unknown aetiology, which has been described in a South African family. The disorder was mapped to a 2.9 centimorgan (cM) locus on chromosome 1q32.2-32.3. Clinically, PFHBII manifests cardiac conduction aberrations, that progress to a disease of the heart muscle, dilated cardiomyopathy (DCM). DCM is also reported as an end phase in hypertrophic cardiomyopathy (HCM), another heart muscle disorder. These cardiomyopathies are genetically heterogeneous with some of the genes reported as causes of both disorders. Therefore, genes identified as causes of HCM and DCM were considered plausible candidates for PFHBII mutation analysis. Additionally, this study provided an opportunity to assess potential modifiers of HCM. HCM exhibits marked phenotypic variability, observed within and between families harbouring the same causative mutation. Genes within the PFHBII locus were selected for PCR-SSCP analysis based on homology to genes previously reported as causing conduction system disorders associated with arrhythmias, DCM and/or HCM. Results were confirmed by direct sequencing and association between the detected variants and HCM parameters was assessed using a quantitative transmission disequilibrium test (QTDT). Eleven plausible candidate genes were selected within the PFHBII locus and two of the genes, PFKFB2 and ATF3, that encode for 6-phosphofructo-2,6-bisphosphatase (PFK-2/FBPase-2) and activating transcription factor 3 (ATF3), respectively, were analysed for PFHBII-causing and HCM main locus and/or HCM modifying mutations. Mutation analysis of PFKFB2 and ATF3 in the PFHBII family revealed no PFHBII causal mutation. PFKFB2 and ATF3 were later localised outside the PFHBII locus, and, therefore, were excluded as PFHBII plausible candidates. Further analysis of the two genes for HCM main locus and/or HCM modifying mutations in the HCM panel identified several sequence variants. QTDT analysis of these variants showed no significant association. Completion of the Human Genome Project (HGP) and annotation of new genes within the PFHBII locus allowed the identification of more PFHBII plausible candidate genes. Identification of causal mutations in plausible PFHBII candidate genes will allow molecular diagnosis of PFHBII pathophysiology. Furthermore, identification of both HCM-modifying and HCM-causing genes will give insight into the phenotypic variability noted among South African HCM-affected individuals and into the molecular cause of the disease among individuals with HCM-like clinical features.<br>AFRIKAANSE OPSOMMING: Progressiewe familiële hartblok tipe II (PFHBII) is ŉ oorgeërfde hart geleidingsiekte van onbekende etiologie wat in ŉ Suid-Afrikaanse familie beskryf is. Die siekte is ŉ 2.9 sentimorgan (cM) lokus op chromosoom 1q32.2-32.3 gekarteer. Klinies presenteer PFHBII met geleidingsfwykings wat uitloop op gedilateerde kardiomiopatie (DCM). DCM word ook gerapporteer as ŉ endfase in hipertrofiese kardiomiopatie (HCM), ŉ ander hartspiersiekte. Die kardiomiopatieë is geneties heterogeen, met ŉ aantal gene wat as oorsaak van altwee siektetoestande gerapporteer word. Daarom is alle gene wat geïdentifiseer is as oorsake van DCM en HCM, as moontlike kandidaatgene vir PFHBII mutasieanaliese beskou. Bykomend het hierdie studie die geleentheid gebied om potensiële modifiseerders van HCM te assesseer. HCM toon beduidende fenotipiese variasie binne en tussen families wat dieselfde siekteveroorsakende mutasie het. Gene binne die PFHBII-lokus is geselekteer vir PCR-SSCP-analiese gebaseer op homologie met gene wat voorheen gerapporteer is om betrokke te wees by geleidingsiesisteemsiektes, geassosieerde arritmieë, DCM en/of HCM. Resultate is bevestig deur volgordebepaling. Assosiasie tusssen ontdekte variante en die siekteparameter is bepaal met ŉ kwantitatiewe transmissie disekwilibrium toets (QTDT). Elf moontlike kandidaatgene in die PFHBII-lokus is geselekteer en twee van die gene, PFKFB2 en ATF3, wat kodeer vir 6-fosfofrukto-2,6-bifosfatase (PFK-2/FBPase-2) en aktiveringstranskripsiefaktor 3 (ATF3) respektiewelik, is vir PFHBII-oorsakende en HCMhooflokus en/of HCM-modifiseerende mutasies ondersoek. Mutasie-analiese van PFKFB2 en ATF3 in die PFHBII-familie het nie ŉ siekteveroorsakende mutasie onthul/uitgelig nie. PFKFB2 en ATF3 is later buite die PFHBII-lokus geplaas en dus ook as moontlike PFHBII-kandidate uitgesluit. Verdere ondersoek van díe twee gene vir HCM-hooflokus en/of HCM-modifiserende mutasies in die HCM-paneel het ŉ aantal volgorde variante geïdentifiseer. QTDT-analiese van die variante het geen beduidende assosiasies aangetoon nie. Voltooiing van die Menslike Genoom Projek (HGP) en annotasie van nuwe gene in die PFHBIIlokus het tot die identifikasie van verdere moontlike PFHBII-kandidaatgene gelei. Identifikase van siekte-veroorsaakende mutasies in die moontlike PFHBII-kandidaatgene sal die molekulêre diagnose van PFHBII toelaat en insig in die patofisiologie van die siekte gee. Verder, identifikasie van beide HCM-veroorsakende of HCM-modifiserende gene kan insig gee in die fenotipiese varieerbaarheid wat onder Suid-Afrikaanse HCM-geaffekteerde individue waargeneem word en ook in die molekulêre oorsake van die siekte in individue met HCMsoortige kliniese kenmerke.

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Ferretti, Yuri. "Ferramenta computacional para análise integrada de dados clínicos e biomoleculares." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-05042016-093735/.

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A massificação dos estudos da medicina translacional permite aos pesquisadores que usufruam de fontes de dados das mais diversas áreas. Uma área de suma importância e a bioinformatica, que agrega o alta capacidade de processamento computacional disponível atualmente, com a infindável quantidade de dados gerada por métodos de sequenciamento de ultima geração, para entregar aos pesquisadores uma quantidade rica de dados para serem analisados. Apesar da disponibilidade desses dados, a expertise necessária para analisa-los dificulta que profissionais com pouco conhecimento em bioinformatica, estatística e ciência da computação possam realizar pesquisas e analises com estes dados. Dada esta situação, este trabalho consistiu em criar uma ferramenta que tira proveito da integração de múltiplas bases de dados proporcionada pelo framework IPTrans, permitindo que usuários da área biomédica realizem analises com os dados contidos nessas bases. Com base em outras ferramentas existentes e em um levantamento de requisitos junto a potenciais usuários, foram identificadas as funcionalidades mais importantes e assim foi projetada e implementada a IPTrans Advanced Analysis Tool (IPTrans A2Tool). Esta ferramenta permite que usuários façam analises de expressão diferencial mais comuns como heatmaps, volcano plots, consenso de agrupamentos e blox-plot. Além disso, a ferramenta proporciona um algoritmo de mineração de dados baseado na extração de regras de associação entre dados clínicos e biomoleculares, que permite ao usuário descobrir novas associações entre a expressão dos genes dados clínicos e fenotípicos. Adicionalmente a este trabalho, foi criado também o BioBank Warden, um sistema de controle de dados clínicos e amostras biomoleculares, que foi utilizado como uma das fontes de dados para o IPTrans A2Tool. Este sistema permite que usuários adicionem informações clinicas de pacientes e também das amostras extraídas para a realização de estudos. Uma avaliação preliminar de usabilidade, realizada junto a profissionais da área biomédica, mostrou que as ferramentas possuem potencial para serem utilizadas no contexto da medicina translacional.<br>The great number of translational medicine studies allows researchers to make benefit of data sources from various fields. An area of great importance is bioinformatics, which combines the high computational processing capabilities found nowadays with the endless amount of data generated by next-generation sequencing methods, to give researchers a rich amount of data to be analyzed. Despite the availability of such data, the expertise required to analyze it makes difficult for professionals with little knowledge in bioinformatics, statistics or computer science, to conduct research and analysis on this data. Given this situation, this work was intended to create a tool that takes advantage of multiple databases integration capabilities provided by IPTrans and that allows users to perform analysis on the data contained in these databases. To accomplish that other tools were studied in order to observe which features our framework should aggregate and thus was created the IPTrans A2Tool (IPTrans Advanced Analysis Tool). This tool allows users to perform differential expression analysis and generate output as heatmaps, volcano plots, consensus clustering and blox-plots. In addition, the tool provides an association rule extraction algorithm between clinical and biomolecular data, allowing the user to discover hidden associations between the expression of analyzed genes and clinical data. As a by-product of this work was also created the BioBank Warden a clinical data and biomolecular samples management system that was used as one of the data sources for IPTrans A2Tool. This system allows users to add patients clinical information and also of samples taken for carrying out studies. In addition, the system provides a strong research group and project permission management that ensures only authorized people to have access to patients data.

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Patel, Amit, Richard Veerman, Jodi Polaha, et al. "Addressing Gaps in Immunization Rates in a Family Medicine Residency Clinic." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/asrf/2018/schedule/200.

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Adult immunizations effectively reduce morbidity, mortality, and transmission rates of multiple diseases; however, outpatient providers often a struggle to convince patients to accept vaccinations. This project’s aim is to address vaccination rates in our adult population, focusing first on the influenza vaccine in year one (2016), and then on pneumococcal vaccine in year two (2017), by 1) using a strong quality improvement strategy (known as a Champion Team) and 2) implementing a clinic program consisting of provider training, improved documentation, and informative posters targeted at patients. A quality improvement strategy known as a “Champion Team” provided a strong mechanism through which we developed and implemented the interventions across both years. Specifically, the Champion Team consisted of key stakeholders (nurses, residents, physician faculty, and informatics expert) who identified, developed, and evaluated the program. Programming included an annual health care professional training session for each vaccine (early fall of 2016 and 2017 for flu, spring 2017 for pneumococcal), improved documentation strategies and nursing uptake, and informative posters in the clinic. We assayed data from our patient electronic health record to evaluate: the percentage of our patient population for whom an immunization was documented relative to the number of unique patients seen in our clinic during that time frame. This approach in year one showed a marked increase in influenza vaccination rates in our clinic. During the 2014/2015 and 2015/2016 flu seasons our clinic vaccination rates were 39.98% and 42.05% respectively. After implementation of our champion team and clinic wide program to increase rates in 2016 our vaccination rates for the 2016/2017 flu seasons was 50.88%. Pneumonia data for a full year are under analyses and will be included in this presentation. We anticipate a similar increase in rates for our pneumococcal vaccinations. Our Champion Team and clinic wide program were perceived as relatively low-effort interventions yet appeared to increase vaccinations over the course of this study. The replication of these findings across pneumonia data (pending) and, in future work, with the herpes zoster vaccine (planned for Year 3), will increase our confidence that increases in rates were attributable to these very accessible interventions.

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Books on the topic "Bioinformatics, Medicine"

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Freitas, Ana T., and Arcadi Navarro, eds. Bioinformatics for Personalized Medicine. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28062-7.

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Cesario, Alfredo, and Frederick Marcus, eds. Cancer Systems Biology, Bioinformatics and Medicine. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1567-7.

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Wang, Xiangdong. Bioinformatics of Human Proteomics. Springer Netherlands, 2013.

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Rashidi, Hooman H. Bioinformatics basics: Applications in biological science and medicine. CRC Press, 2000.

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Conference, International Biometric Society Indian Region. Statistical advances in biosciences and bioinformatics. Allied Publishers, 2006.

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Bioinformatics methods in clinical research. Humana Press, 2010.

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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. John Wiley & Sons, 2010.

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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. John Wiley & Sons, 2010.

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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. John Wiley & Sons, 2010.

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Cesario, Alfredo. Cancer Systems Biology, Bioinformatics and Medicine: Research and Clinical Applications. Springer Science+Business Media B.V., 2011.

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Book chapters on the topic "Bioinformatics, Medicine"

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Nahler, Gerhard. "bioinformatics." In Dictionary of Pharmaceutical Medicine. Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_121.

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Steele, Paul E., John A. Lynch, Jeremy J. Corsmo, David P. Witte, John B. Harley, and Beth L. Cobb. "Laboratory Medicine and Biorepositories." In Translational Bioinformatics. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1104-7_7.

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Aoki-Kinoshita, Kiyoko F. "RINGS Bioinformatics." In Glycoscience: Biology and Medicine. Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-54841-6_19.

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Sunil Krishnan, G., Amit Joshi, and Vikas Kaushik. "Bioinformatics in Personalized Medicine." In Advances in Bioinformatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6191-1_15.

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Aoki-Kinoshita, Kiyoko F. "Glycoinformatics: Bioinformatics Overview." In Glycoscience: Biology and Medicine. Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-54841-6_17.

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Piazza, Ornella, Giuseppe De Benedictis, and Geremia F. Zito Marinosci. "Proteomics in Anaesthesia and Intensive Care Medicine." In Translational Bioinformatics. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5811-7_16.

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Mendez-Gonzalez, Jesus, and Juan Sandoval. "Epigenomic Biomarkers for the Advance of Personalized Medicine." In Translational Bioinformatics. Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9927-0_9.

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Hartler, Jürgen, Harald C. Köfeler, Christopher J. O. Baker, Ravi Tharakan, and Gerhard G. Thallinger. "Lipidomics, Mass Spectrometry, and Bioinformatics." In Computational Medicine. Springer Vienna, 2012. http://dx.doi.org/10.1007/978-3-7091-0947-2_6.

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Chen, Jiajia, and Bairong Shen. "RNA Bioinformatics for Precision Medicine." In Advances in Experimental Medicine and Biology. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1503-8_2.

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van Kampen, Antoine H. C., and Perry D. Moerland. "Taking Bioinformatics to Systems Medicine." In Methods in Molecular Biology. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3283-2_2.

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Conference papers on the topic "Bioinformatics, Medicine"

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"STA-3: Bioelectromagnetic Medicine and Bioinformatics." In IEEE EUROCON 2017 -17th International Conference on Smart Technologies. IEEE, 2017. http://dx.doi.org/10.1109/eurocon.2017.8011167.

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Baxevanis, Andreas D. "Transforming Medicine: Genomics, Bioinformatics, and Human Health." In 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering. IEEE, 2007. http://dx.doi.org/10.1109/bibe.2007.4375768.

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Ritchie, Marylyn D., Jason H. Moore, and Ju Han Kim. "Translational Bioinformatics: Biobanks in the Precision Medicine Era." In Pacific Symposium on Biocomputing 2020. WORLD SCIENTIFIC, 2019. http://dx.doi.org/10.1142/9789811215636_0067.

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Jasinski, Joseph M. ""Computational Biology and Bioinformatics"." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.259775.

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Zhang, Jinhe, Zhengyu Liu, Minxin Cai, et al. "Bioinformatics Analysis of Integrin aVß3." In 2015 7th International Conference on Information Technology in Medicine and Education (ITME). IEEE, 2015. http://dx.doi.org/10.1109/itme.2015.105.

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Zhou, Hongmei, Jing Yang, Jinrui Guo, et al. "Rule-based text mining of traditional Chinese medicine patterns with Chinese herbal medicines and formulae on hypertension." In 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2013. http://dx.doi.org/10.1109/bibm.2013.6732708.

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Rath, L. S. "Ayurinformatics- the application of bioinformatics in the ayurvedic system of medicine." In 9th International Conference on Information Technology (ICIT'06). IEEE, 2006. http://dx.doi.org/10.1109/icit.2006.32.

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Baumbach, Jan. "From gene panels to systems medicine." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621177.

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Xiaofang, Zhou, Li Xue, Hu Yangyang, Zhang Wenqiang, and Li Fufeng. "Lip analysis in traditional Chinese medicine." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217864.

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Wang, Le, and Ze Luo. "Contextualized Relevance Feedback for Precision Medicine." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983396.

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