AIMC Topic: Sequence Analysis, Protein

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Using Weighted Extreme Learning Machine Combined With Scale-Invariant Feature Transform to Predict Protein-Protein Interactions From Protein Evolutionary Information.

IEEE/ACM transactions on computational biology and bioinformatics Jan 10, 2020
Protein-Protein Interactions (PPIs) play an irreplaceable role in biological activities of organisms. Although many high-throughput methods are used to identify PPIs from different kinds of organisms, they have some shortcomings, such as high cost an...
Humans Computational Biology Machine Learning Evolution, Molecular Proteins Protein Interaction Mapping Saccharomyces cerevisiae Proteins Databases, Protein Sequence Analysis, Protein Helicobacter pylori Amino Acid Sequence Animals Protein Interaction Maps
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MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks.

BMC genomics Dec 24, 2019
BACKGROUND: Tandem mass spectrometry allows biologists to identify and quantify protein samples in the form of digested peptide sequences. When performing peptide identification, spectral library search is more sensitive than traditional database sea...
Neural Networks, Computer Sequence Analysis, Protein Proteins Humans Peptides Tandem Mass Spectrometry Deep Learning HeLa Cells
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NNTox: Gene Ontology-Based Protein Toxicity Prediction Using Neural Network.

Scientific reports Nov 29, 2019
With advancements in synthetic biology, the cost and the time needed for designing and synthesizing customized gene products have been steadily decreasing. Many research laboratories in academia as well as industry routinely create genetically engine...
Animals Proteins Sequence Analysis, Protein Gene Ontology Toxins, Biological Humans Neural Networks, Computer Software
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PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins.

BioMed research international Nov 19, 2019
Several computational approaches for predicting subcellular localization have been developed and proposed. These approaches provide diverse performance because of their different combinations of protein features, training datasets, training strategie...
Sequence Analysis, Protein Consensus Machine Learning Intracellular Space Computational Biology Bacterial Proteins Algorithms
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A step-by-step classification algorithm of protein secondary structures based on double-layer SVM model.

Genomics Nov 15, 2019
In this paper, a step-by-step classification algorithm based on double-layer SVM model is constructed to predict the secondary structure of proteins. The most important feature of this algorithm is to improve the prediction accuracy of α+β and α/β cl...
Sequence Analysis, Protein Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Humans Animals Support Vector Machine
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Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis.

Scientific reports Nov 15, 2019
Machine learning (ML) is ubiquitous in bioinformatics, due to its versatility. One of the most crucial aspects to consider while training a ML model is to carefully select the optimal feature encoding for the problem at hand. Biophysical propensity s...
Machine Learning Computational Biology Proteins Solvents Biophysical Phenomena Oxidation-Reduction Propensity Score Sequence Analysis, Protein Cysteine Amino Acids
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Novel Descriptors and Digital Signal Processing- Based Method for Protein Sequence Activity Relationship Study.

International journal of molecular sciences Nov 11, 2019
The work aiming to unravel the correlation between protein sequence and function in the absence of structural information can be highly rewarding. We present a new way of considering descriptors from the amino acids index database for modeling and pr...
Glucagon-Like Peptide-2 Receptor Quantitative Structure-Activity Relationship Sequence Analysis, Protein Epoxide Hydrolases Cytochrome P-450 Enzyme System Catalytic Domain Humans Machine Learning Tumor Necrosis Factor-alpha Animals
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Identifying Cancer-Specific circRNA-RBP Binding Sites Based on Deep Learning.

Molecules (Basel, Switzerland) Nov 7, 2019
Circular RNAs (circRNAs) are extensively expressed in cells and tissues, and play crucial roles in human diseases and biological processes. Recent studies have reported that circRNAs could function as RNA binding protein (RBP) sponges, meanwhile RBPs...
Sequence Analysis, Protein RNA, Neoplasm RNA, Circular Neoplasm Proteins Binding Sites Humans Neoplasms Deep Learning RNA-Binding Proteins Databases, Protein
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Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method.

Scientific reports Nov 7, 2019
Succinylation is a type of protein post-translational modification (PTM), which can play important roles in a variety of cellular processes. Due to an increasing number of site-specific succinylated peptides obtained from high-throughput mass spectro...
Databases, Protein Lysine Succinic Acid Deep Learning Protein Processing, Post-Translational Proteins Sequence Analysis, Protein
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Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid Peptide Sequencing Framework.

Molecular & cellular proteomics : MCP Oct 7, 2019
Typical analyses of mass spectrometry data only identify amino acid sequences that exist in reference databases. This restricts the possibility of discovering new peptides such as those that contain uncharacterized mutations or originate from unexpec...
Datasets as Topic Sequence Analysis, Protein Phosphopeptides Amino Acid Sequence Tandem Mass Spectrometry Peptides Humans Deep Learning HLA Antigens
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