Relevant bibliographies by topics / Proteins Bioinformatics. Computational biology

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Proteins Bioinformatics. Computational biology'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Proteins Bioinformatics. Computational biology"

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G. Hawley, Robert, Yuzhong Chen, Irene Riz, and Chen Zeng. "An Integrated Bioinformatics and Computational Biology Approach Identifies New BH3-Only Protein Candidates." Open Biology Journal 5, no. 1 (2012): 6–16. http://dx.doi.org/10.2174/1874196701205010006.

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In this study, we utilized an integrated bioinformatics and computational biology approach in search of new BH3-only proteins belonging to the BCL2 family of apoptotic regulators. The BH3 (BCL2 homology 3) domain mediates specific binding interactions among various BCL2 family members. It is composed of an amphipathic α-helical region of approximately 13 residues that has only a few amino acids that are highly conserved across all members. Using a generalized motif, we performed a genome-wide search for novel BH3-containing proteins in the NCBI Consensus Coding Sequence (CCDS) database. In addition to known pro-apoptotic BH3-only proteins, 197 proteins were recovered that satisfied the search criteria. These were categorized according to α-helical content and predictive binding to BCL-xL (encoded by BCL2L1) and MCL-1, two representative anti-apoptotic BCL2 family members, using position-specific scoring matrix models. Notably, the list is enriched for proteins associated with autophagy as well as a broad spectrum of cellular stress responses such as endoplasmic reticulum stress, oxidative stress, antiviral defense, and the DNA damage response. Several potential novel BH3-containing proteins are highlighted. In particular, the analysis strongly suggests that the apoptosis inhibitor and DNA damage response regulator, AVEN, which was originally isolated as a BCL-xLinteracting protein, is a functional BH3-only protein representing a distinct subclass of BCL2 family members.
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Mih, Nathan, Elizabeth Brunk, Ke Chen, et al. "ssbio: a Python framework for structural systems biology." Bioinformatics 34, no. 12 (2018): 2155–57. http://dx.doi.org/10.1093/bioinformatics/bty077.

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Abstract Summary Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows. Availability and implementation ssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb. Supplementary information Supplementary data are available at Bioinformatics online.
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Cameron, J. M., T. Hurd, and B. H. Robinson. "Computational identification of human mitochondrial proteins based on homology to yeast mitochondrially targeted proteins." Bioinformatics 21, no. 9 (2005): 1825–30. http://dx.doi.org/10.1093/bioinformatics/bti280.

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Gabaldón, Toni. "Computational approaches for the prediction of protein function in the mitochondrion." American Journal of Physiology-Cell Physiology 291, no. 6 (2006): C1121—C1128. http://dx.doi.org/10.1152/ajpcell.00225.2006.

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Understanding a complex biological system, such as the mitochondrion, requires the identification of the complete repertoire of proteins targeted to the organelle, the characterization of these, and finally, the elucidation of the functional and physical interactions that occur within the mitochondrion. In the last decade, significant developments have contributed to increase our understanding of the mitochondrion, and among these, computational research has played a significant role. Not only general bioinformatics tools have been applied in the context of the mitochondrion, but also some computational techniques have been specifically developed to address problems that arose from within the mitochondrial research field. In this review the contribution of bioinformatics to mitochondrial biology is addressed through a survey of current computational methods that can be applied to predict which proteins will be localized to the mitochondrion and to unravel their functional interactions.
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Likić, Vladimir A., Malcolm J. McConville, Trevor Lithgow, and Antony Bacic. "Systems Biology: The Next Frontier for Bioinformatics." Advances in Bioinformatics 2010 (February 9, 2010): 1–10. http://dx.doi.org/10.1155/2010/268925.

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Biochemical systems biology augments more traditional disciplines, such as genomics, biochemistry and molecular biology, by championing (i) mathematical and computational modeling; (ii) the application of traditional engineering practices in the analysis of biochemical systems; and in the past decade increasingly (iii) the use of near-comprehensive data sets derived from ‘omics platform technologies, in particular “downstream” technologies relative to genome sequencing, including transcriptomics, proteomics and metabolomics. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful were strategies involving (a) quantitative measurements of cellular components at the mRNA, protein and metabolite levels, as well as in vivo metabolic reaction rates, (b) development of mathematical models that integrate biochemical knowledge with the information generated by high-throughput experiments, and (c) applications to microbial organisms. The inevitable role bioinformatics plays in modern systems biology puts mathematical and computational sciences as an equal partner to analytical and experimental biology. Furthermore, mathematical and computational models are expected to become increasingly prevalent representations of our knowledge about specific biochemical systems.
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PERES LOPES, GRAZIELA MIÊ, and SANDRO JOSÉ DE SOUZA. "DISSECTING THE HUMAN SPLICEOSOME THROUGH BIOINFORMATICS AND PROTEOMICS APPROACHES." Journal of Bioinformatics and Computational Biology 01, no. 04 (2004): 743–50. http://dx.doi.org/10.1142/s0219720004000405.

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The precise excision of introns from mRNAs is executed by the spliceosome, a cellular machinery composed by five small nuclear RNAs and hundreds of proteins. In the last few years, several groups have used proteomics and computational biology tools to characterize the components of the human spliceosome. These reports have identified basically all known splicing factors and several new proteins. The composition of the human spliceosome confirms the link between splicing and other steps in gene expression. Here we comment on these reports and discuss the perspectives for the coming years.
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Segura, Joan, Ruben Sanchez-Garcia, C. O. S. Sorzano, and J. M. Carazo. "3DBIONOTES v3.0: crossing molecular and structural biology data with genomic variations." Bioinformatics 35, no. 18 (2019): 3512–13. http://dx.doi.org/10.1093/bioinformatics/btz118.

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Abstract Motivation Many diseases are associated to single nucleotide polymorphisms that affect critical regions of proteins as binding sites or post translational modifications. Therefore, analysing genomic variants with structural and molecular biology data is a powerful framework in order to elucidate the potential causes of such diseases. Results A new version of our web framework 3DBIONOTES is presented. This version offers new tools to analyse and visualize protein annotations and genomic variants, including a contingency analysis of variants and amino acid features by means of a Fisher exact test, the integration of a gene annotation viewer to highlight protein features on gene sequences and a protein–protein interaction viewer to display protein annotations at network level. Availability and implementation The web server is available at https://3dbionotes.cnb.csic.es Supplementary information Supplementary data are available at Bioinformatics online. Contact Spanish National Institute for Bioinformatics (INB ELIXIR-ES) and Biocomputing Unit, National Centre of Biotechnology (CSIC)/Instruct Image Processing Centre, C/ Darwin nº 3, Campus of Cantoblanco, 28049 Madrid, Spain.
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Simoncini, David, Kam Y. J. Zhang, Thomas Schiex, and Sophie Barbe. "A structural homology approach for computational protein design with flexible backbone." Bioinformatics 35, no. 14 (2018): 2418–26. http://dx.doi.org/10.1093/bioinformatics/bty975.

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Abstract Motivation Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs. Results We introduce Shades, a data-driven CPD method that exploits local structural environments in known protein structures together with energy to guide sequence design, while sampling side-chain and backbone conformations to accommodate mutations. Shades (Structural Homology Algorithm for protein DESign), is based on customized libraries of non-contiguous in-contact amino acid residue motifs. We have tested Shades on a public benchmark of 40 proteins selected from different protein families. When excluding homologous proteins, Shades achieved a protein sequence recovery of 30% and a protein sequence similarity of 46% on average, compared with the PFAM protein family of the target protein. When homologous structures were added, the wild-type sequence recovery rate achieved 93%. Availability and implementation Shades source code is available at https://bitbucket.org/satsumaimo/shades as a patch for Rosetta 3.8 with a curated protein structure database and ITEM library creation software. Supplementary information Supplementary data are available at Bioinformatics online.
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Bensmail, Halima, and Abdelali Haoudi. "Postgenomics: Proteomics and Bioinformatics in Cancer Research." Journal of Biomedicine and Biotechnology 2003, no. 4 (2003): 217–30. http://dx.doi.org/10.1155/s1110724303209207.

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Now that the human genome is completed, the characterization of the proteins encoded by the sequence remains a challenging task. The study of the complete protein complement of the genome, the “proteome,” referred to as proteomics, will be essential if new therapeutic drugs and new disease biomarkers for early diagnosis are to be developed. Research efforts are already underway to develop the technology necessary to compare the specific protein profiles of diseased versus nondiseased states. These technologies provide a wealth of information and rapidly generate large quantities of data. Processing the large amounts of data will lead to useful predictive mathematical descriptions of biological systems which will permit rapid identification of novel therapeutic targets and identification of metabolic disorders. Here, we present an overview of the current status and future research approaches in defining the cancer cell's proteome in combination with different bioinformatics and computational biology tools toward a better understanding of health and disease.
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Orlando, Gabriele, Daniele Raimondi, Francesco Tabaro, Francesco Codicè, Yves Moreau, and Wim F. Vranken. "Computational identification of prion-like RNA-binding proteins that form liquid phase-separated condensates." Bioinformatics 35, no. 22 (2019): 4617–23. http://dx.doi.org/10.1093/bioinformatics/btz274.

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Abstract Motivation Eukaryotic cells contain different membrane-delimited compartments, which are crucial for the biochemical reactions necessary to sustain cell life. Recent studies showed that cells can also trigger the formation of membraneless organelles composed by phase-separated proteins to respond to various stimuli. These condensates provide new ways to control the reactions and phase-separation proteins (PSPs) are thus revolutionizing how cellular organization is conceived. The small number of experimentally validated proteins, and the difficulty in discovering them, remain bottlenecks in PSPs research. Results Here we present PSPer, the first in-silico screening tool for prion-like RNA-binding PSPs. We show that it can prioritize PSPs among proteins containing similar RNA-binding domains, intrinsically disordered regions and prions. PSPer is thus suitable to screen proteomes, identifying the most likely PSPs for further experimental investigation. Moreover, its predictions are fully interpretable in the sense that it assigns specific functional regions to the predicted proteins, providing valuable information for experimental investigation of targeted mutations on these regions. Finally, we show that it can estimate the ability of artificially designed proteins to form condensates (r=−0.87), thus providing an in-silico screening tool for protein design experiments. Availability and implementation PSPer is available at bio2byte.com/psp. Supplementary information Supplementary data are available at Bioinformatics online.
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Dissertations / Theses on the topic "Proteins Bioinformatics. Computational biology"

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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.

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<p>Coryneform 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</p><p>management of lab data. The result of the study is an application named, CORYNEBASE, which is a software that meets our aims and objectives.</p>
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Siu, Wing-yan. "Multiple structural alignment for proteins." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B4068748X.

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Siu, Wing-yan, and 蕭穎欣. "Multiple structural alignment for proteins." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B4068748X.

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Simu, Tiberiu. "A method for extracting pathways from Scansite-predicted protein-protein interactions." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-34.

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<p>Protein interaction is an important mechanism for cellular functionality. Predicting protein interactions is available in many cases as computational methods in publicly available resources (for example Scansite). These predictions can be further combined with other information sources to generate hypothetical pathways. However, when using computational methods for building pathways, the process may become time consuming, as it requires multiple iterations and consolidating data from different sources. We have tested whether it is possible to generate graphs of protein-protein interaction by using only domain-motif interaction data and the degree to which it is possible to automate this process by developing a program that is able to aggregate, under user guidance, query results from different information sources. The data sources used are Scansite and SwissProt. Visualisation of the graphs is done with an external program freely available for academic purposes, Osprey. The graphs obtained by running the software show that although it is possible to combine publicly available data and theoretical protein-protein interaction predictions from Scansite, further efforts are needed to increase the biological plausibility of these collections of data. It is possible, however, to reduce the dimensionality of the obtained graphs by focusing the searches on a certain tissue of interest.</p>
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Björkholm, 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.

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<p>Being 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.</p>
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Yao, Jianchao, and 姚劍超. "Predicting the 3D structure of human aquaporin-0 protein in eye lens using computational tools." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B2948540X.

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Podowski, Raf M. "Applied bioinformatics for gene characterization /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-818-5/.

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Bahena, Silvia. "Computational Methods for the structural and dynamical understanding of GPCR-RAMP interactions." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-416790.

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Protein-protein interaction dominates all major biology processes in living cells. Recent studies suggestthat the surface expression and activity of G protein-coupled receptors (GPCRs), which are the largestfamily of receptors in human cells, can be modulated by receptor activity–modifying proteins (RAMPs). Computational tools are essential to complement experimental approaches for the understanding ofmolecular activity of living cells and molecular dynamics simulations are well suited to providemolecular details of proteins function and structure. The classical atom-level molecular modeling ofbiological systems is limited to small systems and short time scales. Therefore, its application iscomplicated for systems such as protein-protein interaction in cell-surface membrane. For this reason, coarse-grained (CG) models have become widely used and they represent an importantstep in the study of large biomolecular systems. CG models are computationally more effective becausethey simplify the complexity of the protein structure allowing simulations to have longer timescales. The aim of this degree project was to determine if the applications of coarse-grained molecularsimulations were suitable for the understanding of the dynamics and structural basis of the GPCRRAMP interactions in a membrane environment. Results indicate that the study of protein-proteininteractions using CG needs further improvement with a more accurate parameterization that will allowthe study of complex systems.
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Kemmer, Danielle. "Genomics and bioinformatics approaches to functional gene annotation /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-636-0/.

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Söderquist, Fredrik. "Proteus : A new predictor for protean segments." Thesis, Linköpings universitet, Teknisk biologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121260.

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The discovery of intrinsically disordered proteins has led to a paradigm shift in protein science. Many disordered proteins have regions that can transform from a disordered state to an ordered. Those regions are called protean segments. Many intrinsically disordered proteins are involved in diseases, including Alzheimer's disease, Parkinson's disease and Down's syndrome, which makes them prime targets for medical research. As protean segments often are the functional part of the proteins, it is of great importance to identify those regions. This report presents Proteus, a new predictor for protean segments. The predictor uses Random Forest (a decision tree ensemble classifier) and is trained on features derived from amino acid sequence and conservation data. Proteus compares favourably to state of the art predictors and performs better than the competition on all four metrics: precision, recall, F1 and MCC. The report also looks at the differences between protean and non-protean regions and how they differ between the two datasets that were used to train the predictor.
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Books on the topic "Proteins Bioinformatics. Computational biology"

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Frishman, Dmitrij. Structural bioinformatics of membrane proteins. Springer, 2010.

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Rigden, Daniel John. From protein structure to function with bioinformatics. Springer, 2009.

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Campbell, A. Malcolm. Discovering genomics, proteomics, and bioinformatics. 2nd ed. CSHL Press, 2007.

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J, Heyer Laurie, ed. Discovering genomics, proteomics, and bioinformatics. 2nd ed. CSHL Press, 2007.

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Russell, David James. Multiple sequence alignment methods. Humana Press, 2014.

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Bioinformatics: Genome bioinformatics and computational biology. Nova Science, 2011.

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Rajasekaran, Sanguthevar, ed. Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00727-9.

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Essential bioinformatics. Cambridge University Press, 2006.

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Chan, Jonathan H., Yew-Soon Ong, and Sung-Bae Cho, eds. Computational Systems-Biology and Bioinformatics. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16750-8.

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Gibas, Cynthia. Developing bioinformatics computer skills. O'Reilly, 2001.

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Book chapters on the topic "Proteins Bioinformatics. Computational biology"

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Cao, Wei, Kazuya Sumikoshi, Tohru Terada, Shugo Nakamura, Katsuhiko Kitamoto, and Kentaro Shimizu. "Computational Protocol for Screening GPI-anchored Proteins." In Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00727-9_17.

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Nihitha, Y., G. Lavanya Devi, and V. Jaya Vani. "A Study on Proteins Associated with MODY Using Computational Biology." In Cognitive Science and Health Bioinformatics. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6653-5_3.

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González, Alvaro J., and Li Liao. "Constrained Fisher Scores Derived from Interaction Profile Hidden Markov Models Improve Protein to Protein Interaction Prediction." In Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00727-9_23.

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Yalamanchili, Hari Krishna, and Nita Parekh. "Graph Spectral Approach for Identifying Protein Domains." In Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00727-9_40.

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Souza, Daniel S., Waldeyr M. C. Silva, Célia G. Ralha, and Maria Emília M. T. Walter. "An Argumentation Theory-Based Multiagent Model to Annotate Proteins." In Advances in Bioinformatics and Computational Biology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01722-4_7.

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Rivera, Corban G., and T. M. Murali. "Identifying Evolutionarily Conserved Protein Interaction Modules Using GraphHopper." In Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00727-9_9.

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Borsuk, Sibele, Fabiana Kommling Seixas, Daniela Fernandes Ramos, Caroline Rizzi, and Odir Antonio Dellagostin. "Identification of Proteins from Tuberculin Purified Protein Derivative (PPD) with Potential for TB Diagnosis Using Bioinformatics Analysis." In Advances in Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03223-3_15.

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Hu, Jianjun, and Fan Zhang. "Improving Protein Localization Prediction Using Amino Acid Group Based Physichemical Encoding." In Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00727-9_24.

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Cerri, Ricardo, Renato R. O. da Silva, and André C. P. L. F. de Carvalho. "Comparing Methods for Multilabel Classification of Proteins Using Machine Learning Techniques." In Advances in Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03223-3_10.

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Gao, Jianjiong, Ganesh Kumar Agrawal, Jay J. Thelen, Zoran Obradovic, A. Keith Dunker, and Dong Xu. "A New Machine Learning Approach for Protein Phosphorylation Site Prediction in Plants." In Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00727-9_4.

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Conference papers on the topic "Proteins Bioinformatics. Computational biology"

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Hawkins, J., and M. Boden. "Predicting Peroxisomal Proteins." In 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2005. http://dx.doi.org/10.1109/cibcb.2005.1594956.

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Wang, Penghao, and Susan R. Wilson. "A new hybrid probability-based method for identifying proteins and protein modifications." In 2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2013. http://dx.doi.org/10.1109/cibcb.2013.6595381.

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Bauer, D. C., M. Boden, R. Thier, and Zheng Yuan. "Predicting Structural Disruption of Proteins Caused by Crossover." In 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2005. http://dx.doi.org/10.1109/cibcb.2005.1594962.

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Watanabe, Ryosuke, Edgar E. Vallejo, and Enrique Morett. "Inferring functional coupling of proteins using the Evolutionary Bond Energy Algorithm." In 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology. IEEE, 2006. http://dx.doi.org/10.1109/cibcb.2006.330964.

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Kedarisetti, Kanaka Durga, Ke Chen, Aashima Kapoor, and Lukasz Kurgan. "Prediction of the Number of Helices for the Twilight Zone Proteins." In 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology. IEEE, 2006. http://dx.doi.org/10.1109/cibcb.2006.330972.

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Maetschke, S., M. Gallagher, and M. Boden. "A Comparison of Sequence Kernels for Localization Prediction of Transmembrane Proteins." In 2007 4th Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2007. http://dx.doi.org/10.1109/cibcb.2007.4221246.

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Alvarez, Marco A., and Changhui Yan. "Exploring structural modeling of proteins for kernel-based enzyme discrimination." In 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2010. http://dx.doi.org/10.1109/cibcb.2010.5510588.

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Hu, Jing. "BLKnn: A K-nearest neighbors method for predicting bioluminescent proteins." In 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2014. http://dx.doi.org/10.1109/cibcb.2014.6845503.

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Fernandez, Juan Carlos, Edgar E. Vallejo, and Enrique Morett. "Fuzzy C-means for inferring functional coupling of proteins from their phylogenetic profiles." In 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology. IEEE, 2006. http://dx.doi.org/10.1109/cibcb.2006.331007.

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10

Melhem, Hind, Xiang Jia Min, and Greg Butler. "The impact of SignalP 4.0 on the prediction of secreted proteins." In 2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2013. http://dx.doi.org/10.1109/cibcb.2013.6595383.

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Reports on the topic "Proteins Bioinformatics. Computational biology"

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Wallace, Susan S. DOE EPSCoR Initiative in Structural and computational Biology/Bioinformatics. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/924036.

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