AI Medical Compendium Topic:
Databases, Protein

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Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms.

BioMed research international
Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of...

Bioinformatics approaches for the functional interpretation of protein lists: from ontology term enrichment to network analysis.

Proteomics
The main result of a great deal of the published proteomics studies is a list of identified proteins, which then needs to be interpreted in relation to the research question and existing knowledge. In the early days of proteomics this interpretation ...

Machine learning in computational docking.

Artificial intelligence in medicine
OBJECTIVE: The objective of this paper is to highlight the state-of-the-art machine learning (ML) techniques in computational docking. The use of smart computational methods in the life cycle of drug design is relatively a recent development that has...

Computationally predicting protein-RNA interactions using only positive and unlabeled examples.

Journal of bioinformatics and computational biology
Protein-RNA interactions (PRIs) are considerably important in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulations of gene expression to the active defense of host against virus. With the development...

BagReg: Protein inference through machine learning.

Computational biology and chemistry
Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have ...

Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study.

Database : the journal of biological databases and curation
The evidence that two molecules interact in a living cell is often inferred from multiple different experiments. Experimental data is captured in multiple repositories, but there is no simple way to assess the evidence of an interaction occurring in ...

Types and effects of protein variations.

Human genetics
Variations in proteins have very large number of diverse effects affecting sequence, structure, stability, interactions, activity, abundance and other properties. Although protein-coding exons cover just over 1 % of the human genome they harbor an di...

A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data.

BMC genomics
BACKGROUND: Investigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machin...

Using support vector machines to identify protein phosphorylation sites in viruses.

Journal of molecular graphics & modelling
Phosphorylation of viral proteins plays important roles in enhancing replication and inhibition of normal host-cell functions. Given its importance in biology, a unique opportunity has arisen to identify viral protein phosphorylation sites. However, ...

A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach.

Journal of computer-aided molecular design
The assessment of binding affinity between ligands and the target proteins plays an essential role in drug discovery and design process. As an alternative to widely used scoring approaches, machine learning methods have also been proposed for fast pr...