AIMC Topic: Binding Sites

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AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification.

Nucleic acids research Jun 4, 2019
ChIP-seq is a technique to determine binding locations of transcription factors, which remains a central challenge in molecular biology. Current practice is to use a 'control' dataset to remove background signals from a immunoprecipitation (IP) 'targ...
Algorithms Reproducibility of Results Protein Binding Humans Machine Learning High-Throughput Nucleotide Sequencing Binding Sites Computational Biology Chromatin Immunoprecipitation Transcription Factors Sequence Analysis, DNA
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Identifying short disorder-to-order binding regions in disordered proteins with a deep convolutional neural network method.

Journal of bioinformatics and computational biology Feb 1, 2019
Molecular recognition features (MoRFs) are key functional regions of intrinsically disordered proteins (IDPs), which play important roles in the molecular interaction network of cells and are implicated in many serious human diseases. Identifying MoR...
Membrane Proteins Databases, Protein Amino Acid Sequence Intrinsically Disordered Proteins Binding Sites Deep Learning Protein Binding Computational Biology Humans Neural Networks, Computer
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Using two-dimensional convolutional neural networks for identifying GTP binding sites in Rab proteins.

Journal of bioinformatics and computational biology Feb 1, 2019
Deep learning has been increasingly and widely used to solve numerous problems in various fields with state-of-the-art performance. It can also be applied in bioinformatics to reduce the requirement for feature extraction and reach high performance. ...
Deep Learning Computational Biology Databases, Protein Humans Neural Networks, Computer rab GTP-Binding Proteins Guanosine Triphosphate Amino Acids Amino Acid Sequence Binding Sites
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BIPSPI: a method for the prediction of partner-specific protein-protein interfaces.

Bioinformatics (Oxford, England) Feb 1, 2019
MOTIVATION: Protein-Protein Interactions (PPI) are essentials for most cellular processes and thus, unveiling how proteins interact is a crucial question that can be better understood by identifying which residues are responsible for the interaction....
Machine Learning Protein Domains Binding Sites Sequence Analysis, Protein Protein Interaction Maps Proteins
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Automatic recognition of ligands in electron density by machine learning.

Bioinformatics (Oxford, England) Feb 1, 2019
MOTIVATION: The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure-guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without so...
Machine Learning Algorithms Protein Binding Electrons Ligands Binding Sites
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LigVoxel: inpainting binding pockets using 3D-convolutional neural networks.

Bioinformatics (Oxford, England) Jan 15, 2019
MOTIVATION: Structure-based drug discovery methods exploit protein structural information to design small molecules binding to given protein pockets. This work proposes a purely data driven, structure-based approach for imaging ligands as spatial fie...
Protein Binding Computational Biology Proteins Ligands Protein Conformation Neural Networks, Computer Binding Sites Software Drug Discovery
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Boosting Granular Support Vector Machines for the Accurate Prediction of Protein-Nucleotide Binding Sites.

Combinatorial chemistry & high throughput screening Jan 1, 2019
AIM AND OBJECTIVE: The accurate identification of protein-ligand binding sites helps elucidate protein function and facilitate the design of new drugs. Machine-learning-based methods have been widely used for the prediction of protein-ligand binding ...
Support Vector Machine Proteins Nucleotides High-Throughput Screening Assays Binding Sites
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Discovering epistatic feature interactions from neural network models of regulatory DNA sequences.

Bioinformatics (Oxford, England) Sep 1, 2018
MOTIVATION: Transcription factors bind regulatory DNA sequences in a combinatorial manner to modulate gene expression. Deep neural networks (DNNs) can learn the cis-regulatory grammars encoded in regulatory DNA sequences associated with transcription...
Chromatin High-Throughput Nucleotide Sequencing Binding Sites Transcription Factors Sequence Analysis, DNA DNA Neural Networks, Computer Protein Binding Genomics
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pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks.

Bioinformatics (Oxford, England) Sep 1, 2018
SUMMARY: Convolutional neural networks (CNNs) have been shown to perform exceptionally well in a variety of tasks, including biological sequence classification. Available implementations, however, are usually optimized for a particular task and diffi...
Neural Networks, Computer Software Machine Learning Binding Sites Sequence Analysis
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Convolutional neural networks for classification of alignments of non-coding RNA sequences.

Bioinformatics (Oxford, England) Jul 1, 2018
MOTIVATION: The convolutional neural network (CNN) has been applied to the classification problem of DNA sequences, with the additional purpose of motif discovery. The training of CNNs with distributed representations of four nucleotides has successf...
Binding Sites RNA, Untranslated RNA, Small Nucleolar Nucleic Acid Conformation RNA, Transfer Computational Biology Adenocarcinoma Cluster Analysis MicroRNAs Prostatic Neoplasms Software Male Protein Binding Humans Neural Networks, Computer
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