AIMC Topic: Proteins
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MetaPred2CS: a sequence-based meta-predictor for protein-protein interactions of prokaryotic two-component system proteins.
Bioinformatics (Oxford, England)
Jul 4, 2016
MOTIVATION: Two-component systems (TCS) are the main signalling pathways of prokaryotes, and control a wide range of biological phenomena. Their functioning depends on interactions between TCS proteins, the specificity of which is poorly understood.
Machine learning approaches in MALDI-MSI: clinical applications.
Expert review of proteomics
Jun 23, 2016
INTRODUCTION: Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is co...
NegGOA: negative GO annotations selection using ontology structure.
Bioinformatics (Oxford, England)
Jun 17, 2016
MOTIVATION: Predicting the biological functions of proteins is one of the key challenges in the post-genomic era. Computational models have demonstrated the utility of applying machine learning methods to predict protein function. Most prediction met...
A machine-learning approach for predicting palmitoylation sites from integrated sequence-based features.
Journal of bioinformatics and computational biology
Jun 14, 2016
Palmitoylation is the covalent attachment of lipids to amino acid residues in proteins. As an important form of protein posttranslational modification, it increases the hydrophobicity of proteins, which contributes to the protein transportation, orga...
FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation.
Journal of biomedical semantics
Jun 13, 2016
BACKGROUND: Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that descr...
Effectively Identifying Compound-Protein Interactions by Learning from Positive and Unlabeled Examples.
IEEE/ACM transactions on computational biology and bioinformatics
May 18, 2016
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...
Publication of nuclear magnetic resonance experimental data with semantic web technology and the application thereof to biomedical research of proteins.
Journal of biomedical semantics
May 5, 2016
BACKGROUND: The nuclear magnetic resonance (NMR) spectroscopic data for biological macromolecules archived at the BioMagResBank (BMRB) provide a rich resource of biophysical information at atomic resolution. The NMR data archived in NMR-STAR ASCII fo...
A New Feature Vector Based on Gene Ontology Terms for Protein-Protein Interaction Prediction.
IEEE/ACM transactions on computational biology and bioinformatics
Apr 20, 2016
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...
Drug target identification using network analysis: Taking active components in Sini decoction as an example.
Scientific reports
Apr 20, 2016
Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmac...
Exploring information from the topology beneath the Gene Ontology terms to improve semantic similarity measures.
Gene
Apr 12, 2016
Measuring the similarity between pairs of biological entities is important in molecular biology. The introduction of Gene Ontology (GO) provides us with a promising approach to quantifying the semantic similarity between two genes or gene products. T...
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