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Relevant bibliographies by topics / Computational biology

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Academic literature on the topic 'Computational biology'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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Journal articles on the topic "Computational biology"

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Sadiku, Matthew N. O., Yonghui Wang, Suxia Cui, and Sarhan M. Musa. "COMPUTATIONAL BIOLOGY." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 6 (June 30, 2018): 66. http://dx.doi.org/10.23956/ijarcsse.v8i6.616.

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Computation is an integral part of a larger revolution that will affect how science is conducted. Computational biology is an important emerging field of biology which is uniquely enabled by computation. It involves using computers to model biological problems and interpret data, especially problems in evolutionary and molecular biology. The application of computational tools to all areas of biology is producing excitements and insights into biological problems too complex for conventional approaches. This paper provides a brief introduction on computational biology.

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Wood, C. C. "The computational stance in biology." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1774 (April 22, 2019): 20180380. http://dx.doi.org/10.1098/rstb.2018.0380.

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The goal of this article is to call attention to, and to express caution about, the extensive use of computation as an explanatory concept in contemporary biology. Inspired by Dennett's ‘intentional stance’ in the philosophy of mind, I suggest that a ‘computational stance’ can be a productive approach to evaluating the value of computational concepts in biology. Such an approach allows the value of computational ideas to be assessed without being diverted by arguments about whether a particular biological system is ‘actually computing’ or not. Because there is sufficient difference of agreement among computer scientists about the essential elements that constitute computation, any doctrinaire position about the application of computational ideas seems misguided. Closely related to the concept of computation is the concept of information processing. Indeed, some influential computer scientists contend that there is no fundamental difference between the two concepts. I will argue that despite the lack of widely accepted, general definitions of information processing and computation: (1) information processing and computation are not fully equivalent and there is value in maintaining a distinction between them and (2) that such value is particularly evident in applications of information processing and computation to biology.This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.

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Lederman, Lynne. "Computational Biology." BioTechniques 40, no. 3 (March 2006): 263–65. http://dx.doi.org/10.2144/06403tn01.

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Mesirov, J. P., and D. K. Slonim. "Computational biology." Computing in Science & Engineering 1, no. 3 (May 1999): 16–17. http://dx.doi.org/10.1109/mcise.1999.764211.

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Surridge, Christopher. "Computational biology." Nature 420, no. 6912 (November 2002): 205. http://dx.doi.org/10.1038/nature01253x.

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Kingsbury, David T. "Computational biology." ACM Computing Surveys 28, no. 1 (March 1996): 101–3. http://dx.doi.org/10.1145/234313.234358.

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Ray, L. B., L. D. Chong, and N. R. Gough. "Computational Biology." Science Signaling 2002, no. 148 (September 3, 2002): eg10-eg10. http://dx.doi.org/10.1126/stke.2002.148.eg10.

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Schwarz, Karlheinz, Rainer Breitling, and Christian Allen. "Computation: A New Open Access Journal of Computational Chemistry, Computational Biology and Computational Engineering." Computation 1, no. 2 (September 4, 2013): 27–30. http://dx.doi.org/10.3390/computation1020027.

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Alt, Wolfgang, Andreas Deutsch, and Luigi Preziosi. "Computational Cell Biology: Second Theme Issue on “Computational Biology”." Journal of Mathematical Biology 58, no. 1-2 (August 27, 2008): 1–5. http://dx.doi.org/10.1007/s00285-008-0207-x.

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Markowetz, Florian. "All biology is computational biology." PLOS Biology 15, no. 3 (March 9, 2017): e2002050. http://dx.doi.org/10.1371/journal.pbio.2002050.

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Dissertations / Theses on the topic "Computational biology"

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Istrail, Sorin. "Computational molecular biology /." Amsterdam [u.a.] : Elsevier, 2003. http://www.loc.gov/catdir/toc/fy037/2003051360.html.

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Stegle, Oliver. "Probabilistic models in computational biology." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611560.

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Athanasakis, D. "Feature selection in computational biology." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1432346/.

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This thesis concerns feature selection, with a particular emphasis on the computational biology domain and the possibility of non-linear interaction between features. Towards this it establishes a two-step approach, where the first step is feature selection, followed by the learning of a kernel machine in this reduced representation. Optimization of kernel target alignment is proposed as a model selection criterion and its properties are established for a number of feature selection algorithms, including some novel variants of stability selection. The thesis further studies greedy and stochastic approaches for optimizing alignment, propos- ing a fast stochastic method with substantial probabilistic guarantees. The proposed stochastic method compares favorably to its deterministic counterparts in terms of computational complexity and resulting accuracy. The characteristics of this stochastic proposal in terms of computational complexity and applicabil- ity to multi-class problems make it invaluable to a deep learning architecture which we propose. Very encouraging results of this architecture in a recent challenge dataset further justify this approach, with good further results on a signal peptide cleavage prediction task. These proposals are evaluated in terms of generalization accuracy, interpretability and numerical stability of the models, and speed on a number of real datasets arising from infectious disease bioinfor- matics, with encouraging results.

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Wu, Yichao Hurd Harry L. Ji Chuanshu. "Probability approximations with applications in computational finance and computational biology." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2006. http://dc.lib.unc.edu/u?/etd,247.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2006.
Title from electronic title page (viewed Oct. 10, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research." Discipline: Statistics and Operations Research; Department/School: Statistics and Operations Research.

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Ranjard, Louis. "Computational biology of bird song evolution." e-Thesis University of Auckland, 2010. http://hdl.handle.net/2292/5719.

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Individuals of a given population share more behavioural traits with each other than with members of other populations. For example, in humans, traditions are specific to regions or countries. These cultural relationships can tell us about the history of the populations, their origin and the amount of exchange between them. In birds, regional dialects have been described in many species. However, the mechanisms with which dialects form in populations is not fully understood because it is difficult to analyse experimentally. Translocated populations, with their known histories, offer an opportunity to study these mechanisms. From the study of bird vocalisations we can make inferences regarding population structure and relationships as well as their history, individual behavioural state, neuronal and physiological mechanisms or development of neuronal learning. Too achieve this, cross-disciplinary approaches are necessary, combining field work, bioacoustic methods, statistical tools such as machine learning, ecological knowledge and phylogenetic methods. Here, I will describe computational methods for the treatment and classification of bird vocalisations and will use them to depict the relationships between bird populations. First, I discretise the data in order to define the cultural traits. Then phylogenetic tree-building methods are used. Two approaches are possible, first to map these traits onto known phylogenies and, second, to directly build the phylogeny of these traits. I describe the application of these methods to test several hypothesis on bird songs evolution related to both their history and the mechanisms with which they evolve. Evidence for the presence of dialects in the Puget Sound white-crowned sparrow (Zonotrichia leucophrys pugetensis) is provided on the basis of the syllable content of the songs. The absence of vocal sexual dimorphism is reported in the Australasian gannet (or takapu, Morus serrator), a member of the Sulidae family for which extensive sexual dimorphism has been reported in other species. Subsequently, convergence between the begging calls of several cuckoo species and their respective hosts is suggested by various bioacoustic methods. In addition, the male calls of the hihi (or stitchbird, Notiomystis cincta) is analysed in an island population. The corresponding pattern of variation suggests a post-dispersal acquisition of calls via learning which is in agreement with the most related species in the revised phylogeny of the hihi. Finally, the mechanisms of song evolution are depicted in translocated populations of tieke (or saddleback, Philesturnus carunculatus rufusater), resulting in the development of island dialects.

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Lanctôt, J. Kevin. "Some string problems in computational biology." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0023/NQ51207.pdf.

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Miller, David J. Ghosh Avijit. "New methods in computational systems biology /." Philadelphia, Pa. : Drexel University, 2008. http://hdl.handle.net/1860/2810.

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Li, Limin, and 李丽敏. "Machine learning methods for computational biology." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44546749.

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Vialette, Stéphane. "Algorithmic Contributions to Computational Molecular Biology." Habilitation à diriger des recherches, Université Paris-Est, 2010. http://tel.archives-ouvertes.fr/tel-00862069.

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Selega, Alina. "Computational methods for RNA integrative biology." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/29630.

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Ribonucleic acid (RNA) is an essential molecule, which carries out a wide variety of functions within the cell, from its crucial involvement in protein synthesis to catalysing biochemical reactions and regulating gene expression. Such diverse functional repertoire is indebted to complex structures that RNA can adopt and its flexibility as an interacting molecule. It has become possible to experimentally measure these two crucial aspects of RNA regulatory role with such technological advancements as next-generation sequencing (NGS). NGS methods can rapidly obtain the nucleotide sequence of many molecules in parallel. Designing experiments, where only the desired parts of the molecule (or specific parts of the transcriptome) are sequenced, allows to study various aspects of RNA biology. Analysis of NGS data is insurmountable without computational methods. One such experimental method is RNA structure probing, which aims to infer RNA structure from sequencing chemically altered transcripts. RNA structure probing data is inherently noisy, affected both by technological biases and the stochasticity of the underlying process. Most existing methods do not adequately address the issue of noise, resorting to heuristics and limiting the informativeness of their output. In this thesis, a statistical pipeline was developed for modelling RNA structure probing data, which explicitly captures biological variability, provides automated bias-correcting strategies, and generates a probabilistic output based on experimental measurements. The output of our method agrees with known RNA structures, can be used to constrain structure prediction algorithms, and remains robust to reduced sequence coverage, thereby increasing sensitivity of the technology. Another recent experimental innovation maps RNA-protein interactions at very high temporal resolution, making it possible to study rapid binding events happening on a minute time scale. In this thesis, a non-parametric algorithm was developed for identifying significant changes in RNA-protein binding time-series between different conditions. The method was applied to novel yeast RNA-protein binding time-course data to study the role of RNA degradation in stress response. It revealed pervasive changes in the binding to the transcriptome of the yeast transcription termination factor Nab3 and the cytoplasmic exoribonuclease Xrn1 under nutrient stress. This challenged the common assumption of viewing transcriptional changes as the major driver of changes in RNA expression during stress and highlighted the importance of degradation. These findings inspired a dynamical model for RNA expression, where transcription and degradation rates are modelled using RNA-protein binding time-series data.

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Books on the topic "Computational biology"

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Wünschiers, Röbbe. Computational Biology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-662-45799-3.

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Pham, Tuan, ed. Computational Biology. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-0811-7.

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Gascuel, Olivier, and Marie-France Sagot, eds. Computational Biology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45727-5.

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Fenyö, David, ed. Computational Biology. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-842-3.

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Wünschiers, Röbbe. Computational Biology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34749-8.

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Wünschiers, Röbbe. Computational Biology —. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18552-6.

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Vidyasagar, Mathukumalli. Computational Cancer Biology. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4751-0.

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Ireton, Reneé, Kristina Montgomery, Roger Bumgarner, Ram Samudrala, and Jason McDermott, eds. Computational Systems Biology. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-243-4.

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Fall, Christopher P., Eric S. Marland, John M. Wagner, and John J. Tyson, eds. Computational Cell Biology. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/b97701.

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Huang, Tao, ed. Computational Systems Biology. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7717-8.

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Book chapters on the topic "Computational biology"

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Turk, Irfan. "Computational Biology." In Practical MATLAB, 147–83. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5281-9_8.

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Greenberg, Harvey J., and Allen G. Holder. "Computational Biology." In Encyclopedia of Operations Research and Management Science, 225–38. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_1146.

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Klostermeier, Dagmar, and Markus G. Rudolph. "Computational Biology." In Biophysical Chemistry, 341–61. Names: Klostermeier, Dagmar, author. | Rudolph, Markus G., author. Title: Biophysical chemistry / Dagmar Klostermeier and Markus G. Rudolph. Description: Boca Raton, FL : CRC Press, Taylor & Francis Group, [2017]: CRC Press, 2018. http://dx.doi.org/10.1201/9781315156910-21.

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Steele, Guy L., Xiaowei Shen, Josep Torrellas, Mark Tuckerman, Eric J. Bohm, Laxmikant V. Kalé, Glenn Martyna, et al. "Computational Biology." In Encyclopedia of Parallel Computing, 352. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2204.

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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.

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Vera, Julio, and Ulf Schmitz. "Computational microRNA Biology." In Encyclopedia of Systems Biology, 473–80. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1534.

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Lancia, Giuseppe, and Paolo Serafini. "Computational Biology Problems." In EURO Advanced Tutorials on Operational Research, 183–95. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63976-5_15.

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Boukerche, Azzedine, and Alba Cristina Magalhães Alves de Melo. "Computational Molecular Biology." In Parallel Computing for Bioinformatics and Computational Biology, 147–66. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471756504.ch6.

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Jurisica, Igor. "Integrative Computational Biology." In Cancer Informatics in the Post Genomic Era, 129–45. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-69321-7_8.

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Banerjee, 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.

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Conference papers on the topic "Computational biology"

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Carruthers, Sarah, Kat Gunion, and Ulrike Stege. "Computational biology unplugged!" In the 14th Western Canadian Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1536274.1536314.

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Lesk, Arthur M. "COMPUTATIONAL MOLECULAR BIOLOGY." In Data For Discovery. Connecticut: Begellhouse, 2023. http://dx.doi.org/10.1615/1-56700-002-9.410.

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Krasnogor, Natalio. "(Computational) synthetic biology." In the 13th annual conference companion. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001858.2002131.

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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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Chervenak, Ann L. "Session details: Computational biology." In SC '07: International Conference for High Performance Computing, Networking, Storage and Analysis. New York, NY, USA: ACM, 2007. http://dx.doi.org/10.1145/3246893.

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"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.

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Wooley, John. "Trends in computational biology (abstract)." In the third annual international conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/299432.299511.

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Toga, 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.

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IDEKER, T., E. NEUMANN, and V. SCHACHTER. "COMPUTATIONAL AND SYMBOLIC SYSTEMS BIOLOGY." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704856_0044.

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Chanda, Pritam. "Information Theory in Computational Biology." In 5th International Electronic Conference on Entropy and Its Applications. Basel, Switzerland: MDPI, 2019. http://dx.doi.org/10.3390/ecea-5-06668.

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Reports on the topic "Computational biology"

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Barksy, D., and M. Colvin. Computational Biology: A Strategic Initiative LDRD. Office of Scientific and Technical Information (OSTI), February 2002. http://dx.doi.org/10.2172/15006108.

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Lightstone, F., and B. Bennion. Computational Biology for Drug Discovery and Characterization. Office of Scientific and Technical Information (OSTI), February 2009. http://dx.doi.org/10.2172/948962.

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Colvin, Michael, and Masakatsu Watanabe. UC Merced Center for Computational Biology Final Report. Office of Scientific and Technical Information (OSTI), November 2010. http://dx.doi.org/10.2172/993507.

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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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Bower, James M., and Christof Koch. Methods in Computational Neuroscience: Marine Biology Laboratory Student Projects. Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada201434.

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Kuo, I., and C. Mundy. Heterogeneous processes at the intersection of chemistry and biology: A computational approach. Office of Scientific and Technical Information (OSTI), February 2008. http://dx.doi.org/10.2172/926054.

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Ghosh, Debashree, and Arpan Choudhury. Photo-processes in biology: A glimpse from computational chemistry and machine learning. The Israel Chemical Society, March 2023. http://dx.doi.org/10.51167/acm00043.

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Hansmann, Ulrich H. E. Final report for Conference Support Grant "From Computational Biophysics to Systems Biology - CBSB12". Office of Scientific and Technical Information (OSTI), July 2012. http://dx.doi.org/10.2172/1059268.

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Herrador Rodríguez, Alicia, and Francisco José Esteban Ruiz. Integrating STXBP1 syndrome phenotypes. Fundación Avanza, May 2024. http://dx.doi.org/10.60096/fundacionavanza/1602024.

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In this study, we explored the phenotype-genotype spectrum of STXBP1 syndrome from a systems biology approach. We carried out a comparative analysis between the Human Phenotype Ontology and recent scientific findings using computational techniques.

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ALBA ROBLES, ANTONIO, and Francisco José Esteban Ruiz. Integrating STXBP1 syndrome phenotypes. Fundación Avanza, May 2024. http://dx.doi.org/10.60096/fundacionavanza/8432024.

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In this study, we explored the phenotype-genotype spectrum of STXBP1 syndrome from a systems biology approach. We carried out a comparative analysis between the Human Phenotype Ontology and recent scientific findings using computational techniques.

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