Performance analysis of novel methods for detecting epistasis. - Wikidata - wikimedia - TriplyDB

Performance analysis of novel methods for detecting epistasis.

Performance analysis of novel methods for detecting epistasis.

http://www.wikidata.org/entity/Q34102036

about

GWIS--model-free, fast and exhaustive search for epistatic interactions in case-control GWAS.FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm Gene, pathway and network frameworks to identify epistatic interactions of single nucleotide polymorphisms derived from GWAS data.Functional regression method for whole genome eQTL epistasis analysis with sequencing data An Exploration of Gene-Gene Interactions and Their Effects on Hypertension Detecting epistatic interactions in metagenome-wide association studies by metaBOOST.A comparison of internal model validation methods for multifactor dimensionality reduction in the case of genetic heterogeneity.eQTL Epistasis - Challenges and Computational Approaches.EINVis: a visualization tool for analyzing and exploring genetic interactions in large-scale association studies.Stability SCAD: a powerful approach to detect interactions in large-scale genomic study.Genome-wide association studies identifies seven major regions responsible for iron deficiency chlorosis in soybean (Glycine max).An efficiency analysis of high-order combinations of gene-gene interactions using multifactor-dimensionality reduction.CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions.KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies.Additive, epistatic, and environmental effects through the lens of expression variability QTL in a twin cohort epiACO - a method for identifying epistasis based on ant Colony optimization algorithm Key Considerations and Methods in the Study of Gene-Environment Interactions.A comment on two-locus epistatic interaction models for genome-wide association studies.An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions.Confronting the missing epistasis problem: on the reproducibility of gene-gene interactions.MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies.The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation.

P2860

Q27496611-1C0462BE-C11C-4057-8E26-3A9902E6D893 Q28601894-E0A5B1D2-A2EF-4DFD-A256-F9B1E434D789 Q30584016-28543FC9-3B65-494E-946C-3F703EB716B7 Q33702925-085FC747-5718-4098-B34D-AC7DB9AF08F9 Q33795037-9BBED316-04DD-4F14-8293-578F0E4ACCFD Q34039995-02536AB6-87B0-4777-9D5C-DB8F6D6E08C8 Q34309985-E2AC3AE3-0623-4DBB-A383-EA95736B1265 Q34765476-7674832E-1D80-493A-ACE7-9A580A3A3EA3 Q34915927-74FC0491-FF93-469D-9DE6-F0D1EB40AF4B Q35104708-FF536451-837B-42E2-BA78-EEFFA81B62F5 Q35254940-9485E6C7-2743-410C-B3F0-4E253AF4CE90 Q35679069-EAFCD798-0915-4A6F-8B76-4E41375127DA Q36019253-D2461BBB-15E3-41AE-925F-031E2A67C118 Q36317343-51621C35-16C2-4C50-89FD-136EB72FF251 Q37557436-20B0780A-B18C-45A9-9E26-0E67EA96207D Q38685141-B9F5C6EE-1753-4B74-B144-A863A34F3BD7 Q38796166-E4EDC353-9749-43CC-97F9-EC6A2B406198 Q38975564-B11DA922-4A34-4BB6-A1C4-B75748A9E51F Q40666720-55F97C96-D03A-4E9E-9735-FCA06D5B3973 Q41723597-24615A88-7520-4B02-A76F-32210DD63C7A Q45191035-18323D43-DD6F-45A5-BCC8-8C5A813778B8 Q55015170-2BC4DBB8-91C2-4A58-BE56-DBB84B68FA86

P2860

Performance analysis of novel methods for detecting epistasis.

http://www.wikidata.org/entity/Q34102036

description

2011 nî lūn-bûn

@nan

2011 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած

@hyw

2011 թվականի դեկտեմբերին հրատարակված գիտական հոդված

@hy

2011年の論文

@ja

2011年論文

@yue

2011年論文

@zh-hant

2011年論文

@zh-hk

2011年論文

@zh-mo

2011年論文

@zh-tw

2011年论文

@wuu

name

Performance analysis of novel methods for detecting epistasis.

@ast

Performance analysis of novel methods for detecting epistasis.

@en

Performance analysis of novel methods for detecting epistasis.

@nl

type

label

Performance analysis of novel methods for detecting epistasis.

@ast

Performance analysis of novel methods for detecting epistasis.

@en

Performance analysis of novel methods for detecting epistasis.

@nl

prefLabel

Performance analysis of novel methods for detecting epistasis.

@ast

Performance analysis of novel methods for detecting epistasis.

@en

Performance analysis of novel methods for detecting epistasis.

@nl

P1433

Q34102036-FE2DD900-B9DE-460E-B61F-C36175395BD0

P1476

Q34102036-065016C3-0309-44CC-BE90-8F7339C4CD89

P2093

Q34102036-02E4646A-FFD7-4E97-8BE8-D64ADA934E67

Q34102036-15BAD094-0B53-4C84-9898-16CAA78837A2

Q34102036-2173F31B-F488-47E9-9AC8-D6B7D6AB6E32

Q34102036-49B816A3-F71C-421D-818E-F4CDA2EBEACE

Q34102036-DE819A35-BF0D-436E-B315-D8010BCC84DD

Q34102036-EABA7146-10FD-43E7-AC99-086B83145E03

P2860

Q34102036-01346245-618D-4658-8B4A-BF4D84883089

Q34102036-01FD5E26-EACA-4241-BD66-0E0759CF3B0E

Q34102036-035D3119-A915-422C-B40B-F28E75CF807C

Q34102036-0EC55517-200A-46B9-981F-1C4751EAE965

Q34102036-0EC6E4DB-B212-45A7-9EB6-C4C376D17351

Q34102036-144F9C22-6103-468C-8F8C-BD6B116FB373

Q34102036-1469CB27-2468-4CD8-AB8E-411687130F17

Q34102036-1B5A0BCB-12BC-4E25-92E4-ED73943693EB

Q34102036-1C8A4019-B400-482B-80F1-17BD90F6B8A2

Q34102036-2B1A6C15-E481-468E-BD3E-6C66401BC5D1

P2888

Q34102036-18F2EAF3-6ACB-48C4-A3A5-DBF86F825F7F

P304

Q34102036-5629057C-8063-47C6-8B83-8DAAA0968B2B

P31

Q34102036-6E418BA7-DC84-495C-9CA3-F55158757A23

P356

Q34102036-C4F54736-ACA0-46A0-98CE-9413E41F38C8

P478

Q34102036-45F2C5D0-EC59-4F39-935F-1966726188E2

P577

Q34102036-79ECAD1F-B206-49D8-A26E-4A6F0C277FD0

P5875

Q34102036-063E2CEE-366B-4B90-B972-6C5F77478368

P6179

Q34102036-6DE19BF8-A48E-4129-A894-DF3EAEC55E33

P698

Q34102036-352AFC36-0500-429B-A4CE-DD373334FAB8

P932

Q34102036-A12F36B1-7BBC-4FF0-AE40-83B884382EB2

P356

1471-2105-12-475

P1433

BMC Bioinformatics

P1476

Performance analysis of novel methods for detecting epistasis.

@en

P2093

Dan Liu

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

Daojun Ye

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

Junliang Shang

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

Junying Zhang

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

Yaling Yin

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

Yan Sun

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

P2860

Learning genetic epistasis using Bayesian network scoring criteria

A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility

BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies

The future of genetic studies of complex human diseases

Personal genomes: The case of the missing heritability

FastANOVA: an Efficient Algorithm for Genome-Wide Association Study.

Power of multifactor dimensionality reduction and penalized logistic regression for detecting gene-gene interaction in a case-control study.

Detecting epistatic interactions contributing to human gene expression using the CEPH family data

Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity.

Power of grammatical evolution neural networks to detect gene-gene interactions in the presence of error.

P2888

1471-2105-12-475

P304

475

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

P31

scholarly article

P356

10.1186/1471-2105-12-475

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

P478

12

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

P577

2011-12-15T00:00:00Z

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

P5875

51882068

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

P6179

1043765068

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

P698

22172045

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

P932

3259123

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