AIMC Topic: Proteins

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Extracting chemical-protein relations with ensembles of SVM and deep learning models.

Database : the journal of biological databases and curation
Mining relations between chemicals and proteins from the biomedical literature is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemica...

Integrative Analysis of Proteomics Data to Obtain Clinically Relevant Markers.

Methods in molecular biology (Clifton, N.J.)
The analysis of proteomics data can be significantly challenging. Beyond the technical challenges of accurately identifying and quantifying peptides, identifying the most biologically coherent set of biomarkers can be a particularly daunting step. In...

MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. ...

SVM-dependent pairwise HMM: an application to protein pairwise alignments.

Bioinformatics (Oxford, England)
MOTIVATION: Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondar...

ProtDec-LTR2.0: an improved method for protein remote homology detection by combining pseudo protein and supervised Learning to Rank.

Bioinformatics (Oxford, England)
SUMMARY: As one of the most important tasks in protein sequence analysis, protein remote homology detection is critical for both basic research and practical applications. Here, we present an effective web server for protein remote homology detection...

DeepSite: protein-binding site predictor using 3D-convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: An important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years b...

Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.

Bioinformatics (Oxford, England)
MOTIVATION: The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions b...

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...

iMulti-HumPhos: a multi-label classifier for identifying human phosphorylated proteins using multiple kernel learning based support vector machines.

Molecular bioSystems
Protein phosphorylation plays a potential role in regulating protein conformation and functions. As a result, identifying an uncharacterized protein sequence as a phosphorylated protein is a very meaningful problem and an urgent issue for both basic ...

When loss-of-function is loss of function: assessing mutational signatures and impact of loss-of-function genetic variants.

Bioinformatics (Oxford, England)
MOTIVATION: Loss-of-function genetic variants are frequently associated with severe clinical phenotypes, yet many are present in the genomes of healthy individuals. The available methods to assess the impact of these variants rely primarily upon evol...