AIMC Topic: Molecular Sequence Data
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SnoReport 2.0: new features and a refined Support Vector Machine to improve snoRNA identification.
BMC bioinformatics
Dec 15, 2016
BACKGROUND: snoReport uses RNA secondary structure prediction combined with machine learning as the basis to identify the two main classes of small nucleolar RNAs, the box H/ACA snoRNAs and the box C/D snoRNAs. Here, we present snoReport 2.0, which s...
BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species.
BioMed research international
Aug 22, 2016
MicroRNAs (miRNAs) are a set of short (21-24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs' essential biologica...
Enhancing the Prediction of Transmembrane β-Barrel Segments with Chain Learning and Feature Sparse Representation.
IEEE/ACM transactions on computational biology and bioinformatics
Feb 11, 2016
Transmembrane β-barrels (TMBs) are one important class of membrane proteins that play crucial functions in the cell. Membrane proteins are difficult wet-lab targets of structural biology, which call for accurate computational prediction approaches. H...
The identification of cis-regulatory elements: A review from a machine learning perspective.
Bio Systems
Oct 21, 2015
The majority of the human genome consists of non-coding regions that have been called junk DNA. However, recent studies have unveiled that these regions contain cis-regulatory elements, such as promoters, enhancers, silencers, insulators, etc. These ...
Prediction Enhancement of Residue Real-Value Relative Accessible Surface Area in Transmembrane Helical Proteins by Solving the Output Preference Problem of Machine Learning-Based Predictors.
Journal of chemical information and modeling
Oct 20, 2015
The α-helical transmembrane proteins constitute 25% of the entire human proteome space and are difficult targets in high-resolution wet-lab structural studies, calling for accurate computational predictors. We present a novel sequence-based method ca...
Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.
Immunogenetics
Sep 29, 2015
A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented ...
Computational Biology
Cluster Analysis
Databases, Protein
Models, Molecular
Peptide Fragments
Algorithms
Humans
Epitopes
Animals
Neural Networks, Computer
Amino Acid Sequence
Protein Binding
Molecular Sequence Data
Binding Sites
Mice
Sequence Homology, Amino Acid
Histocompatibility Antigens Class II
Harnessing Computational Biology for Exact Linear B-Cell Epitope Prediction: A Novel Amino Acid Composition-Based Feature Descriptor.
Omics : a journal of integrative biology
Sep 25, 2015
Proteins embody epitopes that serve as their antigenic determinants. Epitopes occupy a central place in integrative biology, not to mention as targets for novel vaccine, pharmaceutical, and systems diagnostics development. The presence of T-cell and ...
Machine-Learning-Based Analysis in Genome-Edited Cells Reveals the Efficiency of Clathrin-Mediated Endocytosis.
Cell reports
Sep 17, 2015
Cells internalize various molecules through clathrin-mediated endocytosis (CME). Previous live-cell imaging studies suggested that CME is inefficient, with about half of the events terminated. These CME efficiency estimates may have been confounded b...
Knockdown of NADPH-cytochrome P450 reductase results in reduced resistance to buprofezin in the small brown planthopper, Laodelphax striatellus (fallén).
Pesticide biochemistry and physiology
Sep 5, 2015
NADPH-cytochrome P450 reductase (CPR) plays an important role in cytochrome P450 function, and CPR knockdown in several insects leads to increased susceptibility to insecticides. However, a putative CPR gene has not yet been fully characterized in th...
High-order neural networks and kernel methods for peptide-MHC binding prediction.
Bioinformatics (Oxford, England)
Jul 23, 2015
MOTIVATION: Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between diff...
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