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Sequence Analysis, Protein

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Structure-based prediction of protein- peptide binding regions using Random Forest.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-peptide interactions are one of the most important biological interactions and play crucial role in many diseases including cancer. Therefore, knowledge of these interactions provides invaluable insights into all cellular processe...

Survey of Computational Approaches for Prediction of DNA-Binding Residues on Protein Surfaces.

Methods in molecular biology (Clifton, N.J.)
The increasing number of protein structures with uncharacterized function necessitates the development of in silico prediction methods for functional annotations on proteins. In this chapter, different kinds of computational approaches are briefly 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...

Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning.

Bioinformatics (Oxford, England)
MOTIVATION: The hydrophobicity of a monoclonal antibody is an important biophysical property relevant for its developability into a therapeutic. In addition to characterizing heterogeneity, Hydrophobic Interaction Chromatography (HIC) is an assay tha...

An introduction to deep learning on biological sequence data: examples and solutions.

Bioinformatics (Oxford, England)
MOTIVATION: Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data...

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...

DeepLoc: prediction of protein subcellular localization using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of...

POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles.

Bioinformatics (Oxford, England)
SUMMARY: Evolutionary information in the form of a Position-Specific Scoring Matrix (PSSM) is a widely used and highly informative representation of protein sequences. Accordingly, PSSM-based feature descriptors have been successfully applied to impr...

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...