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Protein Interaction Mapping

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Effectively Identifying Compound-Protein Interactions by Learning from Positive and Unlabeled Examples.

IEEE/ACM transactions on computational biology and bioinformatics
Prediction of compound-protein interactions (CPIs) is to find new compound-protein pairs where a protein is targeted by at least a compound, which is a crucial step in new drug design. Currently, a number of machine learning based methods have been d...

Protein-protein interaction inference based on semantic similarity of Gene Ontology terms.

Journal of theoretical biology
Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic si...

A New Feature Vector Based on Gene Ontology Terms for Protein-Protein Interaction Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Protein-protein interaction (PPI) plays a key role in understanding cellular mechanisms in different organisms. Many supervised classifiers like Random Forest (RF) and Support Vector Machine (SVM) have been used for intra or inter-species interaction...

Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules.

BMC bioinformatics
BACKGROUND: It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular n...

Detection of overlapping protein complexes in gene expression, phenotype and pathways of Saccharomyces cerevisiae using Prorank based Fuzzy algorithm.

Gene
Proteins show their functional activity by interacting with other proteins and forms protein complexes since it is playing an important role in cellular organization and function. To understand the higher order protein organization, overlapping is an...

Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords.

BioMed research international
For the automatic extraction of protein-protein interaction information from scientific articles, a machine learning approach is useful. The classifier is generated from training data represented using several features to decide whether a protein pai...

Towards Extracting Supporting Information About Predicted Protein-Protein Interactions.

IEEE/ACM transactions on computational biology and bioinformatics
One of the goals of relation extraction is to identify protein-protein interactions (PPIs) in biomedical literature. Current systems are capturing binary relations and also the direction and type of an interaction. Besides assisting in the curation P...

A Sequence-Based Dynamic Ensemble Learning System for Protein Ligand-Binding Site Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
BACKGROUND: Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands...

Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

The Journal of membrane biology
Accurately predicting protein-protein interaction sites (PPIs) is currently a hot topic because it has been demonstrated to be very useful for understanding disease mechanisms and designing drugs. Machine-learning-based computational approaches have ...

Prediction the Substrate Specificities of Membrane Transport Proteins Based on Support Vector Machine and Hybrid Features.

IEEE/ACM transactions on computational biology and bioinformatics
Membrane transport proteins and their substrate specificities play crucial roles in a variety of cellular functions. Identifying the substrate specificities of membrane transport proteins is closely related to the protein-target interaction predictio...