Academic literature on the topic 'Computational biology, bioinformatics'
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Journal articles on the topic "Computational biology, bioinformatics"
Claverie, J. M. "From Bioinformatics to Computational Biology." Genome Research 10, no. 9 (September 1, 2000): 1277–79. http://dx.doi.org/10.1101/gr.155500.
Full textFatumo, Segun A., Moses P. Adoga, Opeolu O. Ojo, Olugbenga Oluwagbemi, Tolulope Adeoye, Itunuoluwa Ewejobi, Marion Adebiyi, Ezekiel Adebiyi, Clement Bewaji, and Oyekanmi Nashiru. "Computational Biology and Bioinformatics in Nigeria." PLoS Computational Biology 10, no. 4 (April 24, 2014): e1003516. http://dx.doi.org/10.1371/journal.pcbi.1003516.
Full textPearson, W. R. "Training for bioinformatics and computational biology." Bioinformatics 17, no. 9 (September 1, 2001): 761–62. http://dx.doi.org/10.1093/bioinformatics/17.9.761.
Full textCho, Sung-Bae, Jonathan H. Chan, and Kyu-Baek Hwang. "Preface: Computational Systems-Biology and Bioinformatics." Procedia Computer Science 23 (2013): 1–4. http://dx.doi.org/10.1016/j.procs.2013.10.002.
Full textPegg, S. "Dictionary of Bioinformatics and Computational Biology." Briefings in Bioinformatics 6, no. 2 (January 1, 2005): 211–12. http://dx.doi.org/10.1093/bib/6.2.211.
Full textChan, Jonathan H., Asawin Meechai, and Chee Keong Kwoh. "Preface: Computational Systems-Biology and Bioinformatics." Procedia Computer Science 11 (2012): 1–3. http://dx.doi.org/10.1016/j.procs.2012.09.001.
Full textTalbi, El-Ghazali, and Albert Zomaya. "Grids in bioinformatics and computational biology." Journal of Parallel and Distributed Computing 66, no. 12 (December 2006): 1481. http://dx.doi.org/10.1016/j.jpdc.2006.09.001.
Full textBujnicki, Janusz M., and Jerzy Tiuryn. "Bioinformatics and Computational Biology in Poland." PLoS Computational Biology 9, no. 5 (May 2, 2013): e1003048. http://dx.doi.org/10.1371/journal.pcbi.1003048.
Full textEldəniz qızı Əhmədova, Gülnarə. "Inclusion of bioinformatics in biological sciences." NATURE AND SCIENCE 22, no. 7 (July 17, 2022): 82–86. http://dx.doi.org/10.36719/2707-1146/22/82-86.
Full textGomathy, Dr CK, Mr D. Surya Manohar, and Vasavi Rajesh. "PROTEIN DATABASE IN COMPUTATIONAL BIOLOGY." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem26770.
Full textDissertations / Theses on the topic "Computational biology, bioinformatics"
Rajarathinam, Kayathri. "Nutraceuticals based computational medicinal chemistry." Licentiate thesis, KTH, Teoretisk kemi och biologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-122681.
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Pettersson, Fredrik. "A multivariate approach to computational molecular biology." Doctoral thesis, Umeå : Univ, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-609.
Full textPeng, Zeshan. "Structure comparison in bioinformatics." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36271299.
Full textPeng, Zeshan, and 彭澤山. "Structure comparison in bioinformatics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36271299.
Full textBjörkholm, Patrik. "Method for recognizing local descriptors of protein structures using Hidden Markov Models." Thesis, Linköping University, The Department of Physics, Chemistry and Biology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11408.
Full textBeing able to predict the sequence-structure relationship in proteins will extend the scope of many bioinformatics tools relying on structure information. Here we use Hidden Markov models (HMM) to recognize and pinpoint the location in target sequences of local structural motifs (local descriptors of protein structure, LDPS) These substructures are composed of three or more segments of amino acid backbone structures that are in proximity with each other in space but not necessarily along the amino acid sequence. We were able to align descriptors to their proper locations in 41.1% of the cases when using models solely built from amino acid information. Using models that also incorporated secondary structure information, we were able to assign 57.8% of the local descriptors to their proper location. Further enhancements in performance was yielded when threading a profile through the Hidden Markov models together with the secondary structure, with this material we were able assign 58,5% of the descriptors to their proper locations. Hidden Markov models were shown to be able to locate LDPS in target sequences, the performance accuracy increases when secondary structure and the profile for the target sequence were used in the models.
Chawade, Aakash. "Inferring Gene Regulatory Networks in Cold-Acclimated Plants by Combinatorial Analysis of mRNA Expression Levels and Promoter Regions." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20.
Full textUnderstanding the cold acclimation process in plants may help us develop genetically engineered plants that are resistant to cold. The key factor in understanding this process is to study the genes and thus the gene regulatory network that is involved in the cold acclimation process. Most of the existing approaches1-8 in deriving regulatory networks rely only on the gene expression data. Since the expression data is usually noisy and sparse the networks generated by these approaches are usually incoherent and incomplete. Hence a new approach is proposed here that analyzes the promoter regions along with the expression data in inferring the regulatory networks. In this approach genes are grouped into sets if they contain similar over-represented motifs or motif pairs in their promoter regions and if their expression pattern follows the expression pattern of the regulating gene. The network thus derived is evaluated using known literature evidence, functional annotations and from statistical tests.
Muhammad, Ashfaq. "Design and Development of a Database for the Classification of Corynebacterium glutamicum Genes, Proteins, Mutants and Experimental Protocols." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-23.
Full textCoryneform bacteria are largely distributed in nature and are rod like, aerobic soil bacteria capable of growing on a variety of sugars and organic acids. Corynebacterium glutamicum is a nonpathogenic species of Coryneform bacteria used for industrial production of amino acids. There are three main publicly available genome annotations, Cg, Cgl and NCgl for C. glutamicum. All these three annotations have different numbers of protein coding genes and varying numbers of overlaps of similar genes. The original data is only available in text files. In this format of genome data, it was not easy to search and compare the data among different annotations and it was impossible to make an extensive multidimensional customized formal search against different protein parameters. Comparison of all genome annotations for construction deletion, over-expression mutants, graphical representation of genome information, such as gene locations, neighboring genes, orientation (direct or complementary strand), overlapping genes, gene lengths, graphical output for structure function relation by comparison of predicted trans-membrane domains (TMD) and functional protein domains protein motifs was not possible when data is inconsistent and redundant on various publicly available biological database servers. There was therefore a need for a system of managing the data for mutants and experimental setups. In spite of the fact that the genome sequence is known, until now no databank providing such a complete set of information has been available. We solved these problems by developing a standalone relational database software application covering data processing, protein-DNA sequence extraction and
management of lab data. The result of the study is an application named, CORYNEBASE, which is a software that meets our aims and objectives.
Chen, Lei. "Construction of Evolutionary Tree Models for Oncogenesis of Endometrial Adenocarcinoma." Thesis, University of Skövde, School of Humanities and Informatics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-25.
Full textEndometrial adenocarcinoma (EAC) is the fourth leading cause of carcinoma in woman worldwide, but not much is known about genetic factors involved in this complex disease. During the EAC process, it is well known that losses and gains of chromosomal regions do not occur completely at random, but partly through some flow of causality. In this work, we used three different algorithms based on frequency of genomic alterations to construct 27 tree models of oncogenesis. So far, no study about applying pathway models to microsatellite marker data had been reported. Data from genome–wide scans with microsatellite markers were classified into 9 data sets, according to two biological approaches (solid tumor cell and corresponding tissue culture) and three different genetic backgrounds provided by intercrossing the susceptible rat BDII strain and two normal rat strains. Compared to previous study, similar conclusions were drawn from tree models that three main important regions (I, II and III) and two subordinate regions (IV and V) are likely to be involved in EAC development. Further information about these regions such as their likely order and relationships was produced by the tree models. A high consistency in tree models and the relationship among p19, Tp53 and Tp53 inducible
protein genes provided supportive evidence for the reliability of results.
Dodda, Srinivasa Rao. "Improvements and extensions of a web-tool for finding candidate genes associated with rheumatoid arthritis." Thesis, University of Skövde, School of Humanities and Informatics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-26.
Full textQuantitativeTraitLocus (QTL) is a statistical method used to restrict genomic regions contributing to specific phenotypes. To further localize genes in such regions a web tool called “Candidate Gene Capture” (CGC) was developed by Andersson et al. (2005). The CGC tool was based on the textual description of genes defined in the human phenotype database OMIM. Even though the CGC tool works well, the tool was limited by a number of inconsistencies in the underlying database structure, static web pages and some gene descriptions without properly defined function in the OMIM database. Hence, in this work the CGC tool was improved by redesigning its database structure, adding dynamic web pages and improving the prediction of unknown gene function by using exon analysis. The changes in database structure diminished the number of tables considerably, eliminated redundancies and made data retrieval more efficient. A new method for prediction of gene function was proposed, based on the assumption that similarity between exon sequences is associated with biochemical function. Using Blast with 20380 exon protein sequences and a threshold E-value of 0.01, 639 exon groups were obtained with an average of 11 exons per group. When estimating the functional similarity, it was found that on the average 72% of the exons in a group had at least one Gene Ontology (GO) term in common.
Huque, Enamul. "Shape Analysis and Measurement for the HeLa cell classification of cultured cells in high throughput screening." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-27.
Full textFeature extraction by digital image analysis and cell classification is an important task for cell culture automation. In High Throughput Screening (HTS) where thousands of data points are generated and processed at once, features will be extracted and cells will be classified to make a decision whether the cell-culture is going on smoothly or not. The culture is restarted if a problem is detected. In this thesis project HeLa cells, which are human epithelial cancer cells, are selected for the experiment. The purpose is to classify two types of HeLa cells in culture: Cells in cleavage that are round floating cells (stressed or dead cells are also round and floating) and another is, normal growing cells that are attached to the substrate. As the number of cells in cleavage will always be smaller than the number of cells which are growing normally and attached to the substrate, the cell-count of attached cells should be higher than the round cells. There are five different HeLa cell images that are used. For each image, every single cell is obtained by image segmentation and isolation. Different mathematical features are found for each cell. The feature set for this experiment is chosen in such a way that features are robust, discriminative and have good generalisation quality for classification. Almost all the features presented in this thesis are rotation, translation and scale invariant so that they are expected to perform well in discriminating objects or cells by any classification algorithm. There are some new features added which are believed to improve the classification result. The feature set is considerably broad rather than in contrast with the restricted sets which have been used in previous work. These features are used based on a common interface so that the library can be extended and integrated into other applications. These features are fed into a machine learning algorithm called Linear Discriminant Analysis (LDA) for classification. Cells are then classified as ‘Cells attached to the substrate’ or Cell Class A and ‘Cells in cleavage’ or Cell Class B. LDA considers features by leaving and adding shape features for increased performance. On average there is higher than ninety five percent accuracy obtained in the classification result which is validated by visual classification.
Books on the topic "Computational biology, bioinformatics"
Tiwary, Basant K. Bioinformatics and Computational Biology. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4241-8.
Full textRajasekaran, Sanguthevar, ed. Bioinformatics and Computational Biology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00727-9.
Full textSingh, Tiratha Raj, Hemraj Saini, and Moacyr Comar Junior. Bioinformatics and Computational Biology. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003331247.
Full textChan, Jonathan H., Yew-Soon Ong, and Sung-Bae Cho, eds. Computational Systems-Biology and Bioinformatics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16750-8.
Full textGibas, Cynthia. Developing bioinformatics computer skills. Beijing: O'Reilly, 2001.
Find full textIgnacimuthu, S. Basic bioinformatics. Harrow, U.K: Alpha Science International, 2005.
Find full textV, Yan Peter, ed. Bioinformatics: New research. New York: Nova Biomedical Books, 2004.
Find full textStadler, Peter F., Maria Emilia M. T. Walter, Maribel Hernandez-Rosales, and Marcelo M. Brigido, eds. Advances in Bioinformatics and Computational Biology. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91814-9.
Full textGuimarães, Katia S., Anna Panchenko, and Teresa M. Przytycka, eds. Advances in Bioinformatics and Computational Biology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03223-3.
Full textSetubal, João Carlos, and Sergio Verjovski-Almeida, eds. Advances in Bioinformatics and Computational Biology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11532323.
Full textBook chapters on the topic "Computational biology, bioinformatics"
Mittal, Mamta, Shailendra Singh, and Dolly Sharma. "Computational Biology." In Bioinformatics and RNA, 17–38. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003107736-2-2.
Full textBanerjee, Subhamoy. "Computational Evolutionary Biology." In Advances in Bioinformatics, 83–100. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6191-1_5.
Full textMaulik, Ujjwal, Sanghamitra Bandyopadhyay, and Anirban Mukhopadhyay. "Computational Biology and Bioinformatics." In Multiobjective Genetic Algorithms for Clustering, 71–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16615-0_4.
Full textAli, Muhammad Amjad, Adil Zahoor, Zeenat Niaz, Muhammad Jabran, Muhammad Anas, Ikhlas Shafique, Hafiz Muhammad Ahmad, Muhammad Usama, and Amjad Abbas. "Bioinformatics and Computational Biology." In Trends in Plant Biotechnology, 281–334. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0814-7_10.
Full textJiang, Tao, and Jianxing Feng. "Algorithms in Computational Biology." In Basics of Bioinformatics, 151–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38951-1_5.
Full textTiwary, Basant K. "Structural Bioinformatics." In Bioinformatics and Computational Biology, 65–86. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4241-8_5.
Full textTiwary, Basant K. "Agricultural Bioinformatics." In Bioinformatics and Computational Biology, 183–201. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4241-8_10.
Full textTiwary, Basant K. "Clinical Bioinformatics." In Bioinformatics and Computational Biology, 163–82. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4241-8_9.
Full textBhanu, Bir, and Prue Talbot. "Live Imaging and Video Bioinformatics." In Computational Biology, 3–12. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23724-4_1.
Full textThakoor, Ninad S., Alberto C. Cruz, and Bir Bhanu. "Video Bioinformatics Databases and Software." In Computational Biology, 313–28. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23724-4_17.
Full textConference papers on the topic "Computational biology, bioinformatics"
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.
Full text"Bioinformatics and computational biology, systems biology and modeling." In 2014 Cairo International Biomedical Engineering Conference (CIBEC). IEEE, 2014. http://dx.doi.org/10.1109/cibec.2014.7020933.
Full textBush, William S. "Introduction to bioinformatics and computational biology." In the fourteenth international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2330935.
Full textSEN, PRANAB K. "WHITHER BIOSTOCHASTICS IN COMPUTATIONAL BIOLOGY AND BIOINFORMATICS." In Proceedings of the 2008 Conference on FACM'08. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812835291_0002.
Full textQin, Hong. "Teaching computational thinking through bioinformatics to biology students." In the 40th ACM technical symposium. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1508865.1508932.
Full text"Session details: BIO - computational biology and bioinformatics track." In SAC 2017: Symposium on Applied Computing, edited by Paola Lecca, Dan Tulpan, and Juan Manuel Corchado Rodriguez. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3243941.
Full textCongdon, Clare Bates, and Martin Middendorf. "Session details: Track 3: bioinformatics and computational biology." In GECCO09: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2009. http://dx.doi.org/10.1145/3257497.
Full textCongdon, Clare Bates, and Martin Middendorf. "Session details: Track 3: bioinformatics and computational biology." In GECCO09: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2009. http://dx.doi.org/10.1145/3257482.
Full textToga, A. W. "The Center for Computational Biology." In 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05). IEEE, 2005. http://dx.doi.org/10.1109/csb.2005.50.
Full textValverde, Jose, and Allan Orozco. "Bioinformatics and Computational Biology Systems design applied to Nanobiotechnology." In 2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI). IEEE, 2016. http://dx.doi.org/10.1109/concapan.2016.7942374.
Full textReports on the topic "Computational biology, bioinformatics"
Wallace, Susan S. DOE EPSCoR Initiative in Structural and computational Biology/Bioinformatics. Office of Scientific and Technical Information (OSTI), February 2008. http://dx.doi.org/10.2172/924036.
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