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Databases, Protein

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

International journal of molecular sciences
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...

AntiVPP 1.0: A portable tool for prediction of antiviral peptides.

Computers in biology and medicine
Viruses are worldwide pathogens with a high impact on the human population. Despite the constant efforts to fight viral infections, there is a need to discover and design new drug candidates. Antiviral peptides are molecules with confirmed activity a...

PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions.

Journal of computational chemistry
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...

Application of adaptive-network-based fuzzy inference systems to the parameter optimization of a biochemical rule-based model.

Computers in biology and medicine
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...

Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling.

Scientific reports
PPAR-δ agonists are known to enhance fatty acid metabolism, preserving glucose and physical endurance and are suggested as candidates for treating metabolic diseases. None have reached the clinic yet. Our Machine Learning algorithm called "Iterative ...

TargetDBP: Accurate DNA-Binding Protein Prediction Via Sequence-Based Multi-View Feature Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Accurately identifying DNA-binding proteins (DBPs) from protein sequence information is an important but challenging task for protein function annotations. In this paper, we establish a novel computational method, named TargetDBP, for accurately targ...

DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure.

PloS one
Rapid, accurate prediction of protein structure from amino acid sequence would accelerate fields as diverse as drug discovery, synthetic biology and disease diagnosis. Massively improved prediction of protein structures has been driven by improving t...

Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome.

BMC bioinformatics
BACKGROUND: To further our understanding of immunopeptidomics, improved tools are needed to identify peptides presented by major histocompatibility complex class I (MHC-I). Many existing tools are limited by their reliance upon chemical affinity data...

Extracting chemical-protein interactions from literature using sentence structure analysis and feature engineering.

Database : the journal of biological databases and curation
Information about the interactions between chemical compounds and proteins is indispensable for understanding the regulation of biological processes and the development of therapeutic drugs. Manually extracting such information from biomedical litera...

Prediction of DNA-Binding Residues in Local Segments of Protein Sequences with Fuzzy Cognitive Maps.

IEEE/ACM transactions on computational biology and bioinformatics
While protein-DNA interactions are crucial for a wide range of cellular functions, only a small fraction of these interactions was annotated to date. One solution to close this annotation gap is to employ computational methods that accurately predict...