AIMC Topic: Escherichia coli Proteins

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Deep representation learning improves prediction of LacI-mediated transcriptional repression.

Proceedings of the National Academy of Sciences of the United States of America Jul 6, 2021
Recent progress in DNA synthesis and sequencing technology has enabled systematic studies of protein function at a massive scale. We explore a deep mutational scanning study that measured the transcriptional repression function of 43,669 variants of ...
Reproducibility of Results Deep Learning Mutation Protein Domains Escherichia coli Proteins Transcription, Genetic Epistasis, Genetic Lac Repressors
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Pathogenic potential assessment of the Shiga toxin-producing by a source attribution-considered machine learning model.

Proceedings of the National Academy of Sciences of the United States of America May 18, 2021
Instead of conventional serotyping and virulence gene combination methods, methods have been developed to evaluate the pathogenic potential of newly emerging pathogens. Among them, the machine learning (ML)-based method using whole-genome sequencing ...
Humans Machine Learning Reproducibility of Results ROC Curve Support Vector Machine Escherichia coli Infections Whole Genome Sequencing Virulence Shiga-Toxigenic Escherichia coli Escherichia coli Proteins Shiga Toxin 2
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iML1515, a knowledgebase that computes Escherichia coli traits.

Nature biotechnology Oct 11, 2017
Escherichia coli Knowledge Bases Escherichia coli Proteins Database Management Systems
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Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.

Journal of biosciences Oct 1, 2015
Protein-protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown tha...
Humans Sensitivity and Specificity Support Vector Machine Escherichia coli Fuzzy Logic Databases, Protein Area Under Curve Gene Expression Escherichia coli Proteins Protein Interaction Mapping Protein Interaction Domains and Motifs
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Semi-supervised prediction of gene regulatory networks using machine learning algorithms.

Journal of biosciences Oct 1, 2015
Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low predicti...
Gene Regulatory Networks Computational Biology Transcription Factors Support Vector Machine Escherichia coli Datasets as Topic Escherichia coli Proteins Saccharomyces cerevisiae Gene Expression Regulation, Bacterial Gene Expression Regulation, Fungal Saccharomyces cerevisiae Proteins
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