AI Medical Compendium Topic:
Databases, Protein

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HPSLPred: An Ensemble Multi-Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source.

Proteomics
Predicting the subcellular localization of proteins is an important and challenging problem. Traditional experimental approaches are often expensive and time-consuming. Consequently, a growing number of research efforts employ a series of machine lea...

A deep learning framework for improving long-range residue-residue contact prediction using a hierarchical strategy.

Bioinformatics (Oxford, England)
MOTIVATION: Residue-residue contacts are of great value for protein structure prediction, since contact information, especially from those long-range residue pairs, can significantly reduce the complexity of conformational sampling for protein struct...

Sequence-based predictive modeling to identify cancerlectins.

Oncotarget
Lectins are a diverse type of glycoproteins or carbohydrate-binding proteins that have a wide distribution to various species. They can specially identify and exclusively bind to a certain kind of saccharide groups. Cancerlectins are a group of lecti...

Biocuration in the structure-function linkage database: the anatomy of a superfamily.

Database : the journal of biological databases and curation
UNLABELLED: With ever-increasing amounts of sequence data available in both the primary literature and sequence repositories, there is a bottleneck in annotating molecular function to a sequence. This article describes the biocuration process and met...

Recognizing and Predicting Thioether Bridges Formed by Lanthionine and β-Methyllanthionine in Lantibiotics Using a Random Forest Approach with Feature Selection.

Combinatorial chemistry & high throughput screening
BACKGROUND: Lantibiotics, which are usually produced from Gram-positive bacteria, are regarded as one type of special bacteriocins. Lantibiotics have unsaturated amino acid residues formed by lanthionine (Lan) and β-methyllanthionine (MeLan) residues...

Prediction and Identification of Krüppel-Like Transcription Factors by Machine Learning Method.

Combinatorial chemistry & high throughput screening
AIM AND OBJECTIVE: The Krüppel-like factors (KLFs) are a family of containing Zn finger(ZF) motif transcription factors with 18 members in human genome, among them, KLF18 is predicted by bioinformatics. KLFs possess various physiological function inv...

An Efficient Semi-supervised Learning Approach to Predict SH2 Domain Mediated Interactions.

Methods in molecular biology (Clifton, N.J.)
Src homology 2 (SH2) domain is an important subclass of modular protein domains that plays an indispensable role in several biological processes in eukaryotes. SH2 domains specifically bind to the phosphotyrosine residue of their binding peptides to ...

Evaluating Functional Annotations of Enzymes Using the Gene Ontology.

Methods in molecular biology (Clifton, N.J.)
The Gene Ontology (GO) (Ashburner et al., Nat Genet 25(1):25-29, 2000) is a powerful tool in the informatics arsenal of methods for evaluating annotations in a protein dataset. From identifying the nearest well annotated homologue of a protein of int...