Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

PLoS Computational Biology

Volumn 13, Issue 2, 2017, Pages

  Henriques, David ^a   Villaverde, Alejandro F ^a,b   Rocha, Miguel ^b   Saez Rodriguez, Julio ^c,d   Banga, Julio R ^a  

^a INSTITUTO DE INVESTIGACIONES MARINAS   (Spain)

^b UNIVERSITY OF MINHO   (Portugal)

^c RWTH AACHEN UNIVERSITY   (Germany)

^d WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; ORDINARY DIFFERENTIAL EQUATIONS; REVERSE ENGINEERING; SIGNAL TRANSDUCTION; SIGNALING;

DATA DRIVEN; DYNAMICS MODELS; ENSEMBLE PREDICTION; ILL POSED; INTERACTION NETWORKS; MODEL TRAINING; NONLINEAR NATURE; POST-TRANSLATIONAL MODIFICATIONS; SIGNALING NETWORKS; SIGNALLING PATHWAYS;

DYNAMIC MODELS;

MITOGEN ACTIVATED PROTEIN KINASE; PROTEOME;

ARTICLE; BENCHMARKING; BIOENGINEERING; BREAST CANCER; COMPUTER MODEL; COMPUTER PREDICTION; DATA ANALYSIS SOFTWARE; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; PRACTICE GUIDELINE; PREDICTION; SIGNAL TRANSDUCTION; UNCERTAINTY; ALGORITHM; ANIMAL; BIOLOGICAL MODEL; COMPUTER SIMULATION; HUMAN; METABOLIC ENGINEERING; METABOLIC FLUX ANALYSIS; METABOLISM; PHYSIOLOGY; PROCEDURES;

ALGORITHMS; ANIMALS; COMPUTER SIMULATION; HUMANS; METABOLIC ENGINEERING; METABOLIC FLUX ANALYSIS; MODELS, BIOLOGICAL; PROTEOME; SIGNAL TRANSDUCTION;

EID: 85014202761     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1005379     Document Type: Article

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