Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method.

about

RefBool: a reference-based algorithm for discretizing gene expression data.Expert curation for building network-based dynamical models: a case study on atherosclerotic plaque formation.Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge.

P2860

Q36320248-CA26DD59-5104-4079-BA3D-6D47ECDA99A6 Q55014759-2729A6DF-C8DC-489B-8C86-EC92A181B7B3 Q55267790-5579B29E-BA00-4E26-8423-4FEEAA7C9037

P2860

Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method.

Item

http://www.wikidata.org/entity/Q36156371

description

2016 nî lūn-bûn

@nan

2016年の論文

@ja

2016年論文

@yue

2016年論文

@zh-hant

2016年論文

@zh-hk

2016年論文

@zh-mo

2016年論文

@zh-tw

2016年论文

@wuu

2016年论文

@zh

2016年论文

@zh-cn

name

Boolean regulatory network rec ...... algorithm optimization method.

@ast

Boolean regulatory network rec ...... algorithm optimization method.

@en

type

Item

label

Boolean regulatory network rec ...... algorithm optimization method.

@ast

Boolean regulatory network rec ...... algorithm optimization method.

@en

prefLabel

Boolean regulatory network rec ...... algorithm optimization method.

@ast

Boolean regulatory network rec ...... algorithm optimization method.

@en

P1476

Boolean regulatory network rec ...... algorithm optimization method.

@en

P2093

Martin Ebeling

http://www.w3.org/2001/XMLSchema#string

P2860

Mathematical modelling of cell-fate decision in response to death receptor engagement

CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms.

An algebra-based method for inferring gene regulatory networks.

Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction.

ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data.

Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming.

Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.

Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET.

From biological pathways to regulatory networks.

Predicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided by network stable states.

P2888

s12859-016-1287-z

P304

410

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1186/S12859-016-1287-Z

http://www.w3.org/2001/XMLSchema#string

P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

P577

2016-10-06T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P6179

1039083936

http://www.w3.org/2001/XMLSchema#string

P698

27716031

http://www.w3.org/2001/XMLSchema#string

P932

5053080

http://www.w3.org/2001/XMLSchema#string

Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method.

about

P2860

P2860

Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method.

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

P2888

P304

P31

P356

P433

P478

P50

P577

P6179

P698

P932

P356

P1433

P1476

P2093

P2860

P2888

P304

P31

P356

P433

P478

P50

P577

P6179

P698

P932