AIMC Topic: Proteome
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Artificially intelligent proteomics improves cardiovascular risk assessment.
EBioMedicine
Jan 14, 2019
Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome.
BMC bioinformatics
Jan 5, 2019
BACKGROUND: To further our understanding of immunopeptidomics, improved tools are needed to identify peptides presented by major histocompatibility complex class I (MHC-I). Many existing tools are limited by their reliance upon chemical affinity data...
Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.
Nature communications
Dec 7, 2018
Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machi...
Structure and Protein Interaction-Based Gene Ontology Annotations Reveal Likely Functions of Uncharacterized Proteins on Human Chromosome 17.
Journal of proteome research
Oct 16, 2018
Understanding the function of human proteins is essential to decipher the molecular mechanisms of human diseases and phenotypes. Of the 17 470 human protein coding genes in the neXtProt 2018-01-17 database with unequivocal protein existence evidence ...
Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning.
Scientific reports
Oct 15, 2018
RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow fo...
A rank weighted classification for plasma proteomic profiles based on case-based reasoning.
BMC medical informatics and decision making
May 31, 2018
BACKGROUND: It is a challenge to precisely classify plasma proteomic profiles into their clinical status based solely on their patterns even though distinct patterns of plasma proteomic profiles are regarded as potential to be a biomarker because the...
Mapping Cellular Polarity Networks Using Mass Spectrometry-based Strategies.
Journal of molecular biology
May 18, 2018
Cell polarity is a vital biological process involved in the building, maintenance and normal functioning of tissues in invertebrates and vertebrates. Unsurprisingly, molecular defects affecting polarity organization and functions have a strong impact...
Assisting document triage for human kinome curation via machine learning.
Database : the journal of biological databases and curation
Jan 1, 2018
In the era of data explosion, the increasing frequency of published articles presents unorthodox challenges to fulfill specific curation requirements for bio-literature databases. Recognizing these demands, we designed a document triage system with a...
pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning.
Analytical chemistry
Nov 21, 2017
In tandem mass spectrometry (MS/MS)-based proteomics, search engines rely on comparison between an experimental MS/MS spectrum and the theoretical spectra of the candidate peptides. Hence, accurate prediction of the theoretical spectra of peptides ap...
A knowledge-based T2-statistic to perform pathway analysis for quantitative proteomic data.
PLoS computational biology
Jun 16, 2017
Approaches to identify significant pathways from high-throughput quantitative data have been developed in recent years. Still, the analysis of proteomic data stays difficult because of limited sample size. This limitation also leads to the practice o...
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