Combining intrinsic disorder prediction and augmented training of hidden Markov models improves discriminative motif discovery
暂无分享,去创建一个
[1] Jörg Schultz,et al. HMM Logos for visualization of protein families , 2004, BMC Bioinformatics.
[2] Ziv Bar-Joseph,et al. Ieee/acm Transactions on Computational Biology and Bioinformatics Discriminative Motif Finding for Predicting Protein Subcellular Localization , 2022 .
[3] Mikael Bodén,et al. MEME Suite: tools for motif discovery and searching , 2009, Nucleic Acids Res..
[4] Ryan M. Rifkin,et al. In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..
[5] Sean R. Eddy,et al. Profile hidden Markov models , 1998, Bioinform..
[6] Robert B. Russell,et al. DILIMOT: discovery of linear motifs in proteins , 2006, Nucleic Acids Res..
[7] William Stafford Noble,et al. Assessing computational tools for the discovery of transcription factor binding sites , 2005, Nature Biotechnology.
[8] István Simon,et al. BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm035 Structural bioinformatics Local structural disorder imparts plasticity on linear motifs , 2022 .
[9] A. Tramontano,et al. Exploiting Publicly Available Biological and Biochemical Information for the Discovery of Novel Short Linear Motifs , 2011, PloS one.
[10] Timothy L. Bailey,et al. Discriminative motif discovery in DNA and protein sequences using the DEME algorithm , 2007, BMC Bioinformatics.
[11] Hong Gu,et al. Discriminative Motif Discovery via Simulated Evolution and Random Under-Sampling , 2014, PloS one.
[12] Alan M. Moses,et al. Proteome-Wide Discovery of Evolutionary Conserved Sequences in Disordered Regions , 2012, Science Signaling.
[13] Norman E. Davey,et al. Attributes of short linear motifs. , 2012, Molecular bioSystems.
[14] Ignacio E. Sánchez,et al. The eukaryotic linear motif resource ELM: 10 years and counting , 2013, Nucleic Acids Res..
[15] Zsuzsanna Dosztányi,et al. IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content , 2005, Bioinform..
[16] Anna Tramontano,et al. Assessment of protein disorder region predictions in CASP10 , 2014, Proteins.
[17] Seungjin Choi,et al. Probabilistic Models for Semisupervised Discriminative Motif Discovery in DNA Sequences , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[18] T. Gibson,et al. A careful disorderliness in the proteome: Sites for interaction and targets for future therapies , 2008, FEBS letters.
[19] Tom Minka,et al. Principled Hybrids of Generative and Discriminative Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[20] Lenore Cowen,et al. Augmented training of hidden Markov models to recognize remote homologs via simulated evolution , 2009, Bioinform..
[21] Hong Gu,et al. Discovering short linear protein motif based on selective training of profile hidden Markov models. , 2015, Journal of theoretical biology.
[22] Richard J. Edwards,et al. Computational identification and analysis of protein short linear motifs. , 2010, Frontiers in bioscience.
[23] Emi Tanaka,et al. Improving MEME via a two-tiered significance analysis , 2014, Bioinform..
[24] Richard J. Edwards,et al. Masking residues using context-specific evolutionary conservation significantly improves short linear motif discovery , 2009, Bioinform..
[25] Denis C. Shields,et al. Profile-based short linear protein motif discovery , 2012, BMC Bioinformatics.