Relevant bibliographies by topics / Computational Molecular Biology

Academic literature on the topic 'Computational Molecular Biology'

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Published: 11 March 2023

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Journal articles on the topic "Computational Molecular Biology"

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Wong, Wing Hung. "Computational Molecular Biology." Journal of the American Statistical Association 95, no. 449 (March 2000): 322–26. http://dx.doi.org/10.1080/01621459.2000.10473934.

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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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Lloyd, A. "Computational Methods in Molecular Biology." Briefings in Bioinformatics 1, no. 3 (January 1, 2000): 315–16. http://dx.doi.org/10.1093/bib/1.3.315.

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Martin, D. "Computational Molecular Biology: An Introduction." Briefings in Bioinformatics 2, no. 2 (January 1, 2001): 204–6. http://dx.doi.org/10.1093/bib/2.2.204.

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Brutlag, Douglas L. "Genomics and computational molecular biology." Current Opinion in Microbiology 1, no. 3 (June 1998): 340–45. http://dx.doi.org/10.1016/s1369-5274(98)80039-8.

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Hunter, Lawrence. "Progress in computational molecular biology." ACM SIGBIO Newsletter 19, no. 3 (December 1999): 9–12. http://dx.doi.org/10.1145/340358.340374.

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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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Sarpeshkar, R. "Analog synthetic biology." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, no. 2012 (March 28, 2014): 20130110. http://dx.doi.org/10.1098/rsta.2013.0110.

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We analyse the pros and cons of analog versus digital computation in living cells. Our analysis is based on fundamental laws of noise in gene and protein expression, which set limits on the energy, time, space, molecular count and part-count resources needed to compute at a given level of precision. We conclude that analog computation is significantly more efficient in its use of resources than deterministic digital computation even at relatively high levels of precision in the cell. Based on this analysis, we conclude that synthetic biology must use analog, collective analog, probabilistic and hybrid analog–digital computational approaches; otherwise, even relatively simple synthetic computations in cells such as addition will exceed energy and molecular-count budgets. We present schematics for efficiently representing analog DNA–protein computation in cells. Analog electronic flow in subthreshold transistors and analog molecular flux in chemical reactions obey Boltzmann exponential laws of thermodynamics and are described by astoundingly similar logarithmic electrochemical potentials. Therefore, cytomorphic circuits can help to map circuit designs between electronic and biochemical domains. We review recent work that uses positive-feedback linearization circuits to architect wide-dynamic-range logarithmic analog computation in Escherichia coli using three transcription factors, nearly two orders of magnitude more efficient in parts than prior digital implementations.
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Casadio, Rita, Boris Lenhard, and Michael J. E. Sternberg. "Computational Resources for Molecular Biology 2021." Journal of Molecular Biology 433, no. 11 (May 2021): 166962. http://dx.doi.org/10.1016/j.jmb.2021.166962.

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Gentleman, Robert. "Current Topics in Computational Molecular Biology." Journal of the American Statistical Association 99, no. 466 (June 2004): 560. http://dx.doi.org/10.1198/jasa.2004.s328.

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Dissertations / Theses on the topic "Computational Molecular 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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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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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.

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Dinescu, Adriana. "Metals in Chemistry and Biology: Computational Chemistry Studies." Thesis, University of North Texas, 2007. https://digital.library.unt.edu/ark:/67531/metadc3678/.

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Numerous enzymatic reactions are controlled by the chemistry of metallic ions. This dissertation investigates the electronic properties of three transition metal (copper, chromium, and nickel) complexes and describes modeling studies performed on glutathione synthetase. (1) Copper nitrene complexes were computationally characterized, as these complexes have yet to be experimentally isolated. (2) Multireference calculations were carried out on a symmetric C2v chromium dimer derived from the crystal structure of the [(tBu3SiO)Cr(µ-OSitBu3)]2 complex. (3) The T-shaped geometry of a three-coordinate β-diketiminate nickel(I) complex with a CO ligand was compared and contrasted with isoelectronic and isosteric copper(II) complexes. (4) Glutathione synthetase (GS), an enzyme that belongs to the ATP-grasp superfamily, catalyzes the (Mg, ATP)-dependent biosynthesis of glutathione (GSH) from γ-glutamylcysteine and glycine. The free and reactant forms of human GS (wild-type and glycine mutants) were modeled computationally by employing molecular dynamics simulations, as these currently have not been structurally characterized.
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Karathia, Hiren Mahendrabhai. "Development and application of computational methdologies for Integrated Molecular Systems Biology." Doctoral thesis, Universitat de Lleida, 2012. http://hdl.handle.net/10803/110518.

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L'objectiu del treball presentat en aquesta tesi va ser el desenvolupament i l'aplicació de metodologies computacionals que integren l’anàlisis de informació sobre seqüències proteiques, informació funcional i genòmica per a la reconstrucció, anotació i organització de proteomes complets, de manera que els resultats es poden comparar entre qualsevol nombre d'organismes amb genomes completament seqüenciats. Metodològicament, m'he centrat en la identificació de l'organització molecular dins d'un proteoma complet d'un organisme de referència i comparació amb proteomes d'altres organismes, en espacial, estructural i funcional, el teixit cel • lular de desenvolupament, o els nivells de la fisiologia. La metodologia es va aplicar per abordar la qüestió de la identificació de organismes model adequats per a estudiar diferents fenòmens biològics. Això es va fer mitjançant la comparació d’un conjunt de proteines involucrades en diferents fenòmens biològics en Saccharomyces cerevisiae i Homo sapiens amb els conjunts corresponents d'altres organismes amb genomes. La tesi conclou amb la presentació d'un servidor web, Homol-MetReS, en què s'implementa la metodologia. Homol-MetReS proporciona un entorn de codi obert a la comunitat científica en què es poden realitzar múltiples nivells de comparació i anàlisi de proteomes.
El objetivo del trabajo presentado en esta tesis fue el desarrollo y la aplicación de metodologías computacionales que integran el análisis de la secuencia y de la información funcional y genómica, con el objetivo de reconstruir, anotar y organizar proteomas completos, de tal manera que estos proteomas se puedan comparar entre cualquier número de organismos con genomas completamente secuenciados. Metodológicamente, I centrado en la identificación de organización molecular dentro de un proteoma completo de un organismo de referencia, vinculando cada proteína en que proteoma a las proteínas de otros organismos, de tal manera que cualquiera puede comparar los dos proteomas en espacial, estructural, funcional tejido, celular, el desarrollo o los niveles de la fisiología. La metodología se aplicó para abordar la cuestión de la identificación de organismos modelo adecuados para estudiar diferentes fenómenos biológicos. Esto se hizo comparando conjuntos de proteínas involucradas en diferentes fenómenos biológicos en Saccharomyces cerevisiae y Homo sapiens con los conjuntos correspondientes de otros organismos con genomas completamente secuenciados. La tesis concluye con la presentación de un servidor web, Homol-MetReS, en el que se implementa la metodología. Homol-MetReS proporciona un entorno de código abierto a la comunidad científica en la que se pueden realizar múltiples niveles de comparación y análisis de proteomas.
The aim of the work presented in this thesis was the development and application of computational methodologies that integrate sequence, functional, and genomic information to provide tools for the reconstruction, annotation and organization of complete proteomes in such a way that the results can be compared between any number of organisms with fully sequenced genomes. Methodologically, I focused on identifying molecular organization within a complete proteome of a reference organism and comparing with proteomes of other organisms at spatial, structural, functional, cellular tissue, development or physiology levels. The methodology was applied to address the issue of identifying appropriate model organisms to study different biological phenomena. This was done by comparing the protein sets involved in different biological phenomena in Saccharomyces cerevisiae and Homo sapiens. This thesis concludes by presenting a web server, Homol-MetReS, on which the methodology is implemented. It provides an open source environment to the scientific community on which they can perform multi-level comparison and analysis of proteomes.
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Donaldson, Eric F. Baric Ralph S. "Computational and molecular biology approaches to viral replication and pathogenesis." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2008. http://dc.lib.unc.edu/u?/etd,1731.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2008.
Title from electronic title page (viewed Sep. 16, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Microbiology and Immunology Virology." Discipline: Microbiology and Immunology; Department/School: Medicine.
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Cao, Dan. "Computational and experimental analysis of mRNA degradationin Saccharomyces cerevisiae." Diss., The University of Arizona, 2002. http://hdl.handle.net/10150/280160.

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Because of its integration power, quantifying power, explanatory power and predictive power, mathematical and computational modeling is becoming an important tool to test and advance our understanding about cellular process in the post-genomic era. Iterative approach between modeling, making prediction and experimental testing might increase the rate of forming and testing hypotheses in Biology. mRNA decay is an ideal system to start knowledge based modeling. In the second chapter, I applied the computational modeling approach to test our understanding about normal mRNA turnover processes in yeast. The computational modeling reproduces experimental observations for the unstable MFA2 and stable PGK1 transcripts, suggesting we have a relatively robust understanding for the mRNA decay process in yeast. Subsequent analysis and a series of in silico experiments led to several important insights about this process, which are presented in the second chapter. In the last chapter, I extended this kind of computational analysis to nonsense mediated mRNA decay (NMD), which is a surveillance system all eukaryotic cells have to recognize and degrade mRNAs containing premature translation termination codons. Initial in silico analysis suggests the popular leaky surveillance model about NMD is inconsistent with previous observations. Further experimental analysis using PGK1 mRNA with a nonsense codon in four different positions revealed several new properties of NMD. First, regardless of the position of the nonsense codon, the entire observable population of transcripts is recognized as aberrant, which is different from the leaky surveillance model. Second, the rate of decapping is accelerated in a position dependent manner, although at all positions the dependence of decapping on deadenylation is removed. This provides a mechanistic explanation for the polarity in NMD wherein 5' nonsense codons exert larger effects than 3' nonsense codons. Third, NMD leads to enhanced deadenylation independent of the position of the nonsense codon. This multitude of changes in the metabolism of nonsense containing mRNAs suggests that these transcripts contain multiple alterations in mRNP structure and/or transcript localization. Based on these observations, I constructed a robust computational model that accurately describes the process of NMD and can serve as a predictive model for future work.
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Weis, Michael Christian. "Computational Models of the Mammalian Cell Cycle." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1323278159.

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Ensterö, Mats. "The multi-faceted RNA molecule : Characterization and Function in the regulation of Gene Expression." Doctoral thesis, Stockholm University, Department of Molecular Biology and Functional Genomics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-7729.

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In this thesis I have studied the RNA molecule and its function and characteristics in the regulation of gene expression. I have focused on two events that are important for the regulation of the transcriptome: Translational regulation through micro RNAs; and RNA editing through adenosine deaminations.

Micro RNAs (miRNAs) are ~22 nucleotides long RNA molecules that by semi complementarity bind to untranslated regions of a target messenger RNA (mRNA). The interaction manifests through an RNA/protein complex and act mainly by repressing translation of the target mRNA. I have shown that a pre-cursor miRNA molecule have significantly different information content of sequential composition of the two arms of the pre-cursor hairpin. I have also shown that sequential composition differs between species.

Selective adenosine to inosine (A-to-I) RNA editing is a post-transcriptional process whereby highly specific adenosines in a (pre-)messenger transcript are deaminated to inosines. The deamination is carried out by the ADAR family of proteins and require a specific sequential and structural landscape for target recognition. Only a handful of messenger substrates have been found to be site selectively edited in mammals. Still, most of these editing events have an impact on neurotransmission in the brain.

In order to find novel substrates for A-to-I editing, an experimental setup was made to extract RNA targets of the ADAR2 enzyme. In concert with this experimental approach, I have constructed a computational screen to predict specific positions prone to A-to-I editing.

Further, I have analyzed editing in the mouse brain at four different developmental stages by 454 amplicon sequencing. With high resolution, I present data supporting a general developmental regulation of A-to-I editing. I also present data of coupled editing events on single RNA transcripts suggesting an A-to-I editing mechanism that involve ADAR dimers to act in concert. A different editing pattern is seen for the serotonin receptor 5-ht2c.

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Zwolak, Jason Walter. "Computational Tools for Molecular Networks in Biological Systems." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/30274.

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Theoretical molecular biologists try to understand the workings of cells through mathematics. Some theoreticians use systems of ordinary differential equations (ODEs) as the basis for mathematical modelling of molecular networks. This thesis develops algorithms for estimating molecular reaction rate constants within those mathematical models by fitting the models to experimental data. An additional step is taken to fit non-timecourse experimental data (e.g., transformations must be performed on the ODE solutions before the experimental and simulation data are similar, and therefore, comparable). VTDIRECT is used to perform (a deterministic direct search) global estimation and ODRPACK is used to perform (a trust region Levenberg-Marquardt based) local estimation of rate constants. One such transformation performed on the ODE solutions determines the value of the steady state of the ODE solutions. A new algorithm was developed that finds all steady state solutions of the ODE system given that the system has a special structure (e.g., the right hand sides of the ODEs are rational functions). Also, since the rate constants in the models cannot be negative and may have other restrictions on the values, ODRPACK was modified to address this problem of bound constraints. The new Fortran 95 version of ODRPACK is named ODRPACK95.
Ph. D.
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Books on the topic "Computational Molecular Biology"

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1949-, Leszczynski Jerzy, ed. Computational molecular biology. Amsterdam: Elsevier, 1999.

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1960-, Salzberg Steven L., Searls David B, and Kasif Simon, eds. Computational methods in molecular biology. Amsterdam: Elsevier, 1998.

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Srinivas, Aluru, ed. Handbook of computational molecular biology. Boca Raton, FL: Chapman & Hall/CRC, 2005.

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João, Meidanis, ed. Introduction to computational molecular biology. Boston: PWS Pub., 1997.

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Pe'er, Itsik, ed. Research in Computational Molecular Biology. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04749-7.

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Singh, Mona, ed. Research in Computational Molecular Biology. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31957-5.

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Chor, Benny, ed. Research in Computational Molecular Biology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29627-7.

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Batzoglou, Serafim, ed. Research in Computational Molecular Biology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02008-7.

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Berger, Bonnie, ed. Research in Computational Molecular Biology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12683-3.

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Speed, Terry, and Haiyan Huang, eds. Research in Computational Molecular Biology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71681-5.

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Book chapters on the topic "Computational Molecular Biology"

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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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Erciyes, K. "Introduction to Molecular Biology." In Computational Biology, 11–25. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24966-7_2.

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Waterman, Michael S. "Some Molecular Biology." In Introduction to Computational Biology, 5–27. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-6846-3_2.

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Oprea, Tudor I., Elebeoba E. May, Andrei Leitão, and Alexander Tropsha. "Computational Systems Chemical Biology." In Methods in Molecular Biology, 459–88. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-839-3_18.

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Lodola, Alessio, and Adrian J. Mulholland. "Computational Enzymology." In Methods in Molecular Biology, 67–89. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-017-5_4.

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Mahon, Annette S. "A Molecular Supertree of the Artiodactyla." In Computational Biology, 411–37. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-2330-9_20.

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Tiwary, Basant K. "Molecular Evolution." In Bioinformatics and Computational Biology, 87–116. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4241-8_6.

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Vallabhajosyula, Ravishankar R., and Alpan Raval. "Computational Modeling in Systems Biology." In Methods in Molecular Biology, 97–120. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-800-3_5.

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Ko, Jason M., Reza Mousavi, and Daniel Lobo. "Computational Systems Biology of Morphogenesis." In Methods in Molecular Biology, 343–65. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1831-8_14.

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Söllner, Johannes. "Computational Peptide Vaccinology." In Methods in Molecular Biology, 291–312. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2285-7_13.

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Conference papers on the topic "Computational Molecular Biology"

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Donald, Bruce R. "Computational and physical modeling challenges in structural molecular biology and proteomics." In the 2005 ACM symposium. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1060244.1060245.

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Wei, Guopeng, Connor Walsh, Irina Cazan, and Radu Marculescu. "Molecular tweeting." In BCB '15: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808719.2808757.

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Yang, J. Y., A. Niemierko, M. Q. Yang, Zuojie Luo, and Jianling Li. "Predicting Tumor Malignancies using Combined Computational Intelligence, Bioinformatics and Laboratory Molecular Biology Approaches." In 2007 4th Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2007. http://dx.doi.org/10.1109/cibcb.2007.4221203.

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Khudyakov, Yury, Ion Mandoiu, Pavel Skums, and Alexander Zelikovsky. "Workshop on Computational Advances in Molecular Epidemiology." In BCB '19: 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3307339.3343859.

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Roychowdhury, Jaijeet. "Session details: Repurposing I.C. CAD computational techniques for molecular and cell biology." In DAC '11: The 48th Annual Design Automation Conference 2011. New York, NY, USA: ACM, 2011. http://dx.doi.org/10.1145/3256179.

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"“Dynamic molecular portraits” of biomembranes: a computational insight." In Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) :. Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022. http://dx.doi.org/10.18699/sbb-2022-159.

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Ma, Hehuan, Feng Jiang, Yu Rong, Yuzhi Guo, and Junzhou Huang. "Robust self-training strategy for various molecular biology prediction tasks." In BCB '22: 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3535508.3545998.

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"Computational design of molecular probes targeting CD95 signaling pathway." In Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) :. Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022. http://dx.doi.org/10.18699/sbb-2022-581.

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Muhamedyev, Ravil I., Y. Daineko, D. Bari, and Alma T. Mansharipova. "Using computational models for development of the three-dimensional visualization in molecular biology." In 2014 IEEE 8th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2014. http://dx.doi.org/10.1109/icaict.2014.7036006.

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Rasheed, Muhibur, Nathan Clement, Abhishek Bhowmick, and Chandrajit Bajaj. "Statistical Framework for Uncertainty Quantification in Computational Molecular Modeling." In BCB '16: ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2975167.2975182.

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Reports on the topic "Computational Molecular Biology"

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Taylor, Ronald C. Automated insertion of sequences into a ribosomal RNA alignment: An application of computational linguistics in molecular biology. Office of Scientific and Technical Information (OSTI), November 1991. http://dx.doi.org/10.2172/10108317.

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Taylor, R. C. Automated insertion of sequences into a ribosomal RNA alignment: An application of computational linguistics in molecular biology. Office of Scientific and Technical Information (OSTI), November 1991. http://dx.doi.org/10.2172/6057182.

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Hawkins, Brian T., and Sonia Grego. A Better, Faster Road From Biological Data to Human Health: A Systems Biology Approach for Engineered Cell Cultures. RTI Press, June 2017. http://dx.doi.org/10.3768/rtipress.2017.rb.0015.1706.

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Traditionally, the interactions of drugs and toxicants with human tissue have been investigated in a reductionist way—for example, by focusing on specific molecular targets and using single-cell-type cultures before testing compounds in whole organisms. More recently, “systems biology” approaches attempt to enhance the predictive value of in vitro biological data by adopting a comprehensive description of biological systems and using computational tools that are sophisticated enough to handle the complexity of these systems. However, the utility of computational models resulting from these efforts completely relies on the quality of the data used to construct them. Here, we propose that recent advances in the development of bioengineered, three-dimensional, multicellular constructs provide in vitro data of sufficient complexity and physiological relevance to be used in predictive systems biology models of human responses. Such predictive models are essential to maximally leveraging these emerging bioengineering technologies to improve both therapeutic development and toxicity risk assessment. This brief outlines the opportunities presented by emerging technologies and approaches for the acceleration of drug development and toxicity testing, as well as the challenges lying ahead for the field.
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Sheinerman, Felix. Report on the research conducted under the funding of the Sloan foundation postdoctoral fellowship in Computational Molecular Biology [Systematic study of protein-protein complexes] Final report. Office of Scientific and Technical Information (OSTI), June 2001. http://dx.doi.org/10.2172/810580.

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Agresar, Grenmarie, and Michael A. Savageau. Final Report, December, 1999. Sloan - US Department of Energy joint postdoctoral fellowship in computational molecular biology [Canonical nonlinear methods for modeling and analyzing gene circuits and spatial variations during pattern formation in embryonic development]. Office of Scientific and Technical Information (OSTI), December 1999. http://dx.doi.org/10.2172/811376.

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You might also be interested in the extended bibliographies on the topic 'Computational Molecular Biology' for particular source types:
Journal articles Dissertations / Theses Books
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