DeepPep: Deep proteome inference from peptide profiles.
Protein inference, the identification of the protein set that is the origin of a given peptide profile, is a fundamental challenge in proteomics. We present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a proteomics mixture, given the sequence universe of...
Main Authors: | Minseung Kim, Ameen Eetemadi, Ilias Tagkopoulos |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2017-09-01
|
Series: | PLoS Computational Biology |
Online Access: | http://europepmc.org/articles/PMC5600403?pdf=render |
Similar Items
-
The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health
by: Ameen Eetemadi, et al.
Published: (2020-04-01) -
Reduced Graphene Oxide-Metalloporphyrin Sensors for Human Breath Screening
by: Bo Mi Lee, et al.
Published: (2021-11-01) -
Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles.
by: Minseung Kim, et al.
Published: (2015-03-01) -
Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning
by: Jielu Yan, et al.
Published: (2020-06-01) -
PepNN: a deep attention model for the identification of peptide binding sites
by: Osama Abdin, et al.
Published: (2022-05-01)