AIMC Topic: Proteome
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Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning.
G3 (Bethesda, Md.)
May 5, 2017
High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a ta...
Unveiling antimicrobial peptide-generating human proteases using PROTEASIX.
Journal of proteomics
Feb 27, 2017
UNLABELLED: Extracting information from peptidomics data is a major current challenge, as endogenous peptides can result from the activity of multiple enzymes. Proteolytic enzymes can display overlapping or complementary specificity. The activity spe...
Systematic analysis of non-structural protein features for the prediction of PTM function potential by artificial neural networks.
PloS one
Feb 22, 2017
Post-translational modifications (PTMs) provide an extensible framework for regulation of protein behavior beyond the diversity represented within the genome alone. While the rate of identification of PTMs has rapidly increased in recent years, our k...
Diagnosis of major depressive disorder by combining multimodal information from heart rate dynamics and serum proteomics using machine-learning algorithm.
Progress in neuro-psychopharmacology & biological psychiatry
Feb 20, 2017
OBJECTIVE: Major depressive disorder (MDD) is a systemic and multifactorial disorder that involves abnormalities in multiple biochemical pathways and the autonomic nervous system. This study applied a machine-learning method to classify MDD and contr...
Identifying the missing proteins in human proteome by biological language model.
BMC systems biology
Dec 23, 2016
BACKGROUND: With the rapid development of high-throughput sequencing technology, the proteomics research becomes a trendy field in the post genomics era. It is necessary to identify all the native-encoding protein sequences for further function and p...
MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm.
BMC bioinformatics
Dec 15, 2016
BACKGROUND: This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by s...
Eliciting the Functional Taxonomy from protein annotations and taxa.
Scientific reports
Aug 18, 2016
The advances of omics technologies have triggered the production of an enormous volume of data coming from thousands of species. Meanwhile, joint international efforts like the Gene Ontology (GO) consortium have worked to provide functional informati...
CGBVS-DNN: Prediction of Compound-protein Interactions Based on Deep Learning.
Molecular informatics
Aug 12, 2016
Computational prediction of compound-protein interactions (CPIs) is of great importance for drug design as the first step in in-silico screening. We previously proposed chemical genomics-based virtual screening (CGBVS), which predicts CPIs by using a...
Exploiting non-linear relationships between retention time and molecular structure of peptides originating from proteomes and comparing three multivariate approaches.
Journal of pharmaceutical and biomedical analysis
Jan 27, 2016
Peptides' retention time prediction is gaining increasing popularity in liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics. This is a promising approach for improving successful proteome mapping, useful both in identification ...
UbiSite: incorporating two-layered machine learning method with substrate motifs to predict ubiquitin-conjugation site on lysines.
BMC systems biology
Jan 11, 2016
BACKGROUND: The conjugation of ubiquitin to a substrate protein (protein ubiquitylation), which involves a sequential process--E1 activation, E2 conjugation and E3 ligation, is crucial to the regulation of protein function and activity in eukaryotes....
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