AIMC Topic: Proteins

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Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC.

Journal of theoretical biology May 1, 2018
This study examines accurate and efficient computational method for identification of 5-methylcytosine sites in RNA modification. The occurrence of 5-methylcytosine (mC) plays a vital role in a number of biological processes. For better comprehension...
Support Vector Machine Proteins Amino Acids Algorithms Models, Theoretical Humans High-Throughput Nucleotide Sequencing Base Sequence 5-Methylcytosine Amino Acid Sequence RNA
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Measuring phenotype-phenotype similarity through the interactome.

BMC bioinformatics Apr 11, 2018
BACKGROUND: Recently, measuring phenotype similarity began to play an important role in disease diagnosis. Researchers have begun to pay attention to develop phenotype similarity measurement. However, existing methods ignore the interactions between ...
Gene Ontology Proteins Gene Expression Regulation Humans Algorithms Protein Interaction Maps Databases, Genetic Phenotype
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Prediction of lysine glutarylation sites by maximum relevance minimum redundancy feature selection.

Analytical biochemistry Apr 8, 2018
Lysine glutarylation is new type of protein acylation modification in both prokaryotes and eukaryotes. To better understand the molecular mechanism of glutarylation, it is important to identify glutarylated substrates and their corresponding glutaryl...
Software Support Vector Machine Lysine Glutarates Sequence Analysis, Protein Protein Processing, Post-Translational Proteins Acylation
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Automated Preparation of MS-Sensitive Fluorescently Labeled N-Glycans with a Commercial Pipetting Robot.

SLAS technology Apr 4, 2018
N-Glycan analysis is routinely performed for biotherapeutic protein characterization. A recently introduced N-glycan analysis kit using RapiFluor-MS (RFMS) labeling provides time savings over reductive amination labeling methods while also providing ...
Proteins Robotics Polysaccharides Fluorescent Dyes Reproducibility of Results Staining and Labeling Mass Spectrometry Automation, Laboratory
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AdaSampling for Positive-Unlabeled and Label Noise Learning With Bioinformatics Applications.

IEEE transactions on cybernetics Apr 2, 2018
Class labels are required for supervised learning but may be corrupted or missing in various applications. In binary classification, for example, when only a subset of positive instances is labeled whereas the remaining are unlabeled, positive-unlabe...
Phosphoproteins Phosphotransferases Models, Statistical Proteins Machine Learning Computational Biology Transcription Factors Algorithms
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Recognition of protein allosteric states and residues: Machine learning approaches.

Journal of computational chemistry Mar 31, 2018
Allostery is a process by which proteins transmit the effect of perturbation at one site to a distal functional site upon certain perturbation. As an intrinsically global effect of protein dynamics, it is difficult to associate protein allostery with...
Molecular Dynamics Simulation Machine Learning Allosteric Site Proteins Neural Networks, Computer
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PON-tstab: Protein Variant Stability Predictor. Importance of Training Data Quality.

International journal of molecular sciences Mar 28, 2018
Several methods have been developed to predict effects of amino acid substitutions on protein stability. Benchmark datasets are essential for method training and testing and have numerous requirements including that the data is representative for the...
Machine Learning Proteins Databases, Protein Models, Molecular Protein Stability
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HMMER Cut-off Threshold Tool (HMMERCTTER): Supervised classification of superfamily protein sequences with a reliable cut-off threshold.

PloS one Mar 26, 2018
BACKGROUND: Protein superfamilies can be divided into subfamilies of proteins with different functional characteristics. Their sequences can be classified hierarchically, which is part of sequence function assignation. Typically, there are no clear s...
Supervised Machine Learning Cluster Analysis Proteins Computational Biology Amino Acid Sequence Proteomics
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SPIN2: Predicting sequence profiles from protein structures using deep neural networks.

Proteins Mar 25, 2018
Designing protein sequences that can fold into a given structure is a well-known inverse protein-folding problem. One important characteristic to attain for a protein design program is the ability to recover wild-type sequences given their native bac...
Neural Networks, Computer Software Protein Folding Proteins Databases, Protein Models, Molecular Amino Acid Sequence Protein Structure, Secondary
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Completing sparse and disconnected protein-protein network by deep learning.

BMC bioinformatics Mar 22, 2018
BACKGROUND: Protein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of cellular and intracellular processes. Recently, an increasing number of computational methods have sh...
Area Under Curve Bayes Theorem Saccharomyces cerevisiae Protein Interaction Maps Proteins Algorithms ROC Curve Humans Neural Networks, Computer Machine Learning
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