AIMC Topic: Proteins

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Adaptive local learning in sampling based motion planning for protein folding.

BMC systems biology
BACKGROUND: Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods ...

A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces.

International journal of molecular sciences
Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous publ...

MetaPred2CS: a sequence-based meta-predictor for protein-protein interactions of prokaryotic two-component system proteins.

Bioinformatics (Oxford, England)
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
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)
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
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
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
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
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
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...