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Proteomics

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A rank weighted classification for plasma proteomic profiles based on case-based reasoning.

BMC medical informatics and decision making
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
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...

Consistent prediction of GO protein localization.

Scientific reports
The GO-Cellular Component (GO-CC) ontology provides a controlled vocabulary for the consistent description of the subcellular compartments or macromolecular complexes where proteins may act. Current machine learning-based methods used for the automat...

A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.

Journal of proteome research
Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary b...

HMMER Cut-off Threshold Tool (HMMERCTTER): Supervised classification of superfamily protein sequences with a reliable cut-off threshold.

PloS one
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...

Predicting protein complexes using a supervised learning method combined with local structural information.

PloS one
The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein i...

ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning.

The New phytologist
The plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate betwee...

Large-scale automated function prediction of protein sequences and an experimental case study validation on PTEN transcript variants.

Proteins
Recent advances in computing power and machine learning empower functional annotation of protein sequences and their transcript variations. Here, we present an automated prediction system UniGOPred, for GO annotations and a database of GO term predic...

Characterization and Identification of Cryptic Biopeptides in (Wangenh K. Koch) Storage Proteins.

BioMed research international
The objective of this research was to identify and characterize the encoded peptides present in nut storage proteins of . It was found, through in silico prediction, proteomic analysis, and MS spectrometry, that bioactive peptides were mainly found i...

pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning.

Analytical chemistry
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...