AIMC Topic: Binding Sites

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Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform.

International journal of molecular sciences Feb 21, 2019
It is significant for biological cells to predict self-interacting proteins (SIPs) in the field of bioinformatics. SIPs mean that two or more identical proteins can interact with each other by one gene expression. This plays a major role in the evolu...
Humans Binding Sites Fourier Analysis Saccharomyces cerevisiae Proteins Sequence Analysis, Protein Animals Protein Binding Support Vector Machine
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Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters.

Journal of chemical information and modeling Jan 25, 2019
Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially "badly behaving compounds...
Binding Sites Small Molecule Libraries Databases, Pharmaceutical Protein Binding Models, Molecular Machine Learning Proteins Pharmaceutical Preparations ROC Curve High-Throughput Screening Assays
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Prediction of TF-Binding Site by Inclusion of Higher Order Position Dependencies.

IEEE/ACM transactions on computational biology and bioinformatics Jan 10, 2019
Most proposed methods for TF-binding site (TFBS) predictions only use low order dependencies for predictions due to the lack of efficient methods to extract higher order dependencies. In this work, we first propose a novel method to extract higher or...
Humans Neural Networks, Computer Animals Transcription Factors Proteins Databases, Genetic Protein Binding DNA Models, Statistical Mice Binding Sites Computational Biology
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Convolutional neural network based on SMILES representation of compounds for detecting chemical motif.

BMC bioinformatics Dec 31, 2018
BACKGROUND: Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph ...
Humans Deep Learning Protein Binding Neural Networks, Computer Software Pharmaceutical Preparations Binding Sites Proteins DNA Models, Chemical
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PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine.

BMC bioinformatics Dec 31, 2018
BACKGROUND: Identifying specific residues for protein-DNA interactions are of considerable importance to better recognize the binding mechanism of protein-DNA complexes. Despite the fact that many computational DNA-binding residue prediction approach...
Binding Sites Algorithms Protein Binding Computational Biology DNA-Binding Proteins Protein Conformation Support Vector Machine Humans DNA Machine Learning
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Gene ontology improves template selection in comparative protein docking.

Proteins Dec 27, 2018
Structural characterization of protein-protein interactions is essential for our ability to study life processes at the molecular level. Computational modeling of protein complexes (protein docking) is important as the source of their structure and a...
Proteins Databases, Protein Models, Molecular Protein Conformation Protein Interaction Maps Protein Binding Computational Biology Structural Homology, Protein Gene Ontology Binding Sites Protein Interaction Mapping Software Molecular Docking Simulation Humans
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iRNA-PseKNC(2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components.

Journal of theoretical biology Dec 24, 2018
The 2'-O-methylation transferase is involved in the process of 2'-O-methylation. In catalytic processes, the 2-hydroxy group of the ribose moiety of a nucleotide accept a methyl group. This methylation process is a post-transcriptional modification, ...
Computational Biology RNA Methylation Binding Sites Machine Learning Algorithms Deep Learning Humans Neural Networks, Computer
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Deep learning identifies genome-wide DNA binding sites of long noncoding RNAs.

RNA biology Nov 28, 2018
Long noncoding RNAs (lncRNAs) can exert their function by interacting with the DNA via triplex structure formation. Even though this has been validated with a handful of experiments, a genome-wide analysis of lncRNA-DNA binding is needed. In this pap...
RNA, Long Noncoding Deep Learning DNA Genome-Wide Association Study Gene Expression Regulation Humans Binding Sites Mice Reproducibility of Results Animals
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A neural network based model effectively predicts enhancers from clinical ATAC-seq samples.

Scientific reports Oct 30, 2018
Enhancers are cis-acting sequences that regulate transcription rates of their target genes in a cell-specific manner and harbor disease-associated sequence variants in cognate cell types. Many complex diseases are associated with enhancer malfunction...
Computational Biology Neural Networks, Computer Sequence Analysis, DNA Sensitivity and Specificity High-Throughput Nucleotide Sequencing Transposases Gene Expression Profiling Humans Cell Line Binding Sites Transcriptome Enhancer Elements, Genetic ROC Curve
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Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC.

Journal of theoretical biology Oct 23, 2018
Lysine acetylation is one of the most important types of protein post-translational modifications (PTM) that are widely involved in cellular regulatory processes. To fully understand the regulatory mechanism of acetylation, identification of acetylat...
Binding Sites Prokaryotic Cells Species Specificity Acetylation Lysine Protein Processing, Post-Translational Bacterial Proteins Datasets as Topic Bacteria Computational Biology Support Vector Machine
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