AI Medical Compendium Topic:
Protein Binding

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BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information.

International journal of molecular sciences Feb 23, 2019
The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental m...
ROC Curve Protein Binding Computational Biology Software Deep Learning Position-Specific Scoring Matrices Databases, Protein Amino Acid Sequence RNA, Untranslated
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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 Animals Binding Sites Sequence Analysis, Protein Protein Binding Fourier Analysis Support Vector Machine Saccharomyces cerevisiae Proteins
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PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions.

Journal of computational chemistry Feb 15, 2019
The prediction of peptide-protein or protein-protein interactions (PPI) is a challenging task, especially if amino acid sequences are the only information available. Machine learning methods allow us to exploit the information content in PPI datasets...
Models, Chemical Amino Acid Sequence Protein Binding Support Vector Machine Computational Biology Proteins Databases, Protein
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Application of adaptive-network-based fuzzy inference systems to the parameter optimization of a biochemical rule-based model.

Computers in biology and medicine Feb 14, 2019
In this study, the binding of allergens to antibody-receptor complexes was investigated. This process is important in understanding the allergic response. A BioNetGen model that simulates this process, combined with a novel method for encoding steric...
Models, Biological Algorithms Protein Binding Computational Biology Fuzzy Logic Cluster Analysis Databases, Protein
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Machine Learning Consensus To Predict the Binding to the Androgen Receptor within the CoMPARA Project.

Journal of chemical information and modeling Feb 11, 2019
The nuclear androgen receptor (AR) is one of the most relevant biological targets of Endocrine Disrupting Chemicals (EDCs), which produce adverse effects by interfering with hormonal regulation and endocrine system functioning. This paper describes n...
Machine Learning Software Molecular Docking Simulation Humans Endocrine Disruptors Drug Discovery Androgens Protein Binding Androgen Receptor Antagonists Receptors, Androgen
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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 Pharmaceutical Preparations High-Throughput Screening Assays Machine Learning ROC Curve Protein Binding Proteins Models, Molecular
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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...
Binding Sites Humans Neural Networks, Computer Models, Statistical Animals Proteins Protein Binding Computational Biology DNA Mice Transcription Factors Databases, Genetic
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Machine learning models for predicting endocrine disruption potential of environmental chemicals.

Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews Jan 10, 2019
We introduce here ML4Tox, a framework offering Deep Learning and Support Vector Machine models to predict agonist, antagonist, and binding activities of chemical compounds, in this case for the estrogen receptor ligand-binding domain. The ML4Tox mode...
Quantitative Structure-Activity Relationship Receptors, Estrogen Protein Binding Toxicity Tests Machine Learning Support Vector Machine Endocrine Disruptors Computer Simulation Environmental Pollutants
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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 ...
Proteins DNA Pharmaceutical Preparations Software Humans Models, Chemical Binding Sites Deep Learning Neural Networks, Computer Protein Binding
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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 Machine Learning Humans DNA Algorithms Computational Biology DNA-Binding Proteins Protein Binding Support Vector Machine Protein Conformation
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