about
Protein-protein interactions essentials: key concepts to building and analyzing interactome networksEvolution of phosphoregulation: comparison of phosphorylation patterns across yeast speciesEpistasis--the essential role of gene interactions in the structure and evolution of genetic systemsMethods for biological data integration: perspectives and challengesFrom systems to structure: bridging networks and mechanismGenetic interaction networks: better understand to better predictSearching for signaling balance through the identification of genetic interactors of the Rab guanine-nucleotide dissociation inhibitor gdi-1A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis.SCS3 and YFT2 link transcription of phospholipid biosynthetic genes to ER stress and the UPR.Prediction of Genetic Interactions Using Machine Learning and Network PropertiesThe genetic landscape of a cellThe BioGRID interaction database: 2015 updateEpistatic interaction maps relative to multiple metabolic phenotypesQuantitative epistasis analysis and pathway inference from genetic interaction dataTarget inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathwaysEnvironmental Interactions and Epistasis Are Revealed in the Proteomic Responses to Complex StimuliMapping the Hsp90 Genetic Network Reveals Ergosterol Biosynthesis and Phosphatidylinositol-4-Kinase Signaling as Core Circuitry Governing Cellular StressA novel role for transcription factor Lmo4 in thymus development through genetic interaction with Cited2Preservation of genetic and regulatory robustness in ancient gene duplicates of Saccharomyces cerevisiaeSystematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergismInteractome networks and human diseaseRecursive expectation-maximization clustering: a method for identifying buffering mechanisms composed of phenomic modules.An integrated approach to characterize genetic interaction networks in yeast metabolism.Low-Rank and Sparse Matrix Decomposition for Genetic Interaction DataMapping haplotype-haplotype interactions with adaptive LASSOIntegrative methods for analyzing big data in precision medicine.Genetic evidence for a phosphorylation-independent signal transduction mechanism within the Bacillus subtilis stressosomeA comprehensive strategy enabling high-resolution functional analysis of the yeast genome.Developmentally distinct activities of the exocyst enable rapid cell elongation and determine meristem size during primary root growth in ArabidopsisPredicting quantitative genetic interactions by means of sequential matrix approximationCombination chemical geneticsPredicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide dataA developmental systems perspective on epistasis: computational exploration of mutational interactions in model developmental regulatory networks.Genome-wide scoring of positive and negative epistasis through decomposition of quantitative genetic interaction fitness matricesTheCellMap.org: A Web-Accessible Database for Visualizing and Mining the Global Yeast Genetic Interaction Network.Identification of response-modulated genetic interactions by sensitivity-based epistatic analysis.A strategy to apply quantitative epistasis analysis on developmental traitsSuppression of statin effectiveness by copper and zinc in yeast and human cellsOn the classification of epistatic interactions.Synthetic cytotoxicity: digenic interactions with TEL1/ATM mutations reveal sensitivity to low doses of camptothecin
P2860
Q21145336-7C0D5365-B1FB-46BC-94EE-137797FB768AQ21145819-0C851BC6-DD24-4671-A1AE-045471DD0BC9Q22122008-47FF38B2-1B1D-4063-BB06-8E95C6F57B1FQ26778582-D7387520-E690-4905-8904-6A7F2668C906Q26830798-89818335-90DB-4B0E-AF18-94B38FEC293BQ27023184-CCAC9A44-374A-4242-AA39-FCB9D36BB20AQ27329446-468C82B4-B805-4331-B405-69FECE424159Q27931694-8382F9D8-22DC-4770-89D0-40DEBBEBF7BBQ27932090-11E8092F-4E6A-47CB-B105-E81368D9CED5Q28084302-01D46AA0-0C82-4AE3-9166-2CA0196CBB23Q28131628-AD7D97E4-B6C6-4EAC-8AA3-FB91C8EC1504Q28252221-9FDADEC8-D6BF-486A-95D0-C0251CA4AC02Q28477135-6544FAB3-62AC-4C00-8021-D9EAF3820C97Q28478118-DFE5376E-9A32-44DD-8E31-3935E5B699EEQ28533401-ABE40551-F46D-4284-9A88-E84BDCACF5DCQ28547070-17CFA435-3FED-4966-BBFF-CF433120A276Q28552131-03081F64-7210-4A2B-B8E9-3BA43A72BB05Q28587320-E7B1357A-E37D-4AA8-B0D5-6EBADDF03043Q28652840-F68B2846-5B34-467C-930B-07CD1DC38DFBQ28818344-8B3E5F7F-46FC-4023-B591-32F8DF0E19F7Q29615773-3246B8F5-21AC-4872-AE7A-9E98343A03B9Q30495653-99A4F1C8-EB16-4C72-B8C4-F0581BF2B2E1Q30501688-8B8B30FB-131B-4B65-B15B-517A0406C645Q30987321-40341248-7DA5-450E-AAF6-0AD36D8DA0A5Q30988293-5EBA4449-03FE-44F7-B333-CC412214A471Q31032576-57C3A1D4-881A-42CF-882E-DF842E9CBEA3Q31153061-349665FD-DA6C-403E-A4C3-E3DDD0CCCEE2Q33351293-B988C171-96AB-4975-9207-179BFAF32081Q33359799-3E3846F2-AA01-4B2E-8D5A-9137521E0390Q33372120-9130F4C2-FA69-4238-B420-C2188053FB0FQ33378070-E7479B82-9A57-422C-A626-EC0464FFF536Q33491395-A909FF95-6A7F-42E0-8805-642BA72CCE71Q33501199-C8DB3E0C-EFA1-484A-8217-06611125DA30Q33640073-99743947-0E84-436E-8008-EBBD1FA3AC48Q33667298-63188485-E76D-4159-A13F-0DDE1BDB52EAQ33690033-E382C076-7A78-45F6-9913-5C46CA2991DAQ33692095-0C7B7F3F-477A-4911-9462-5ABC7855FE62Q33750330-E000C011-4F20-4E65-84E6-DFA2B3734134Q33752657-25FD199A-652A-4B1D-B316-36C9BC992E5DQ33779587-24D1BB4C-FFFD-463D-9E75-48AEE1AC597E
P2860
description
2008 nî lūn-bûn
@nan
2008年の論文
@ja
2008年学术文章
@wuu
2008年学术文章
@zh-cn
2008年学术文章
@zh-hans
2008年学术文章
@zh-my
2008年学术文章
@zh-sg
2008年學術文章
@yue
2008年學術文章
@zh
2008年學術文章
@zh-hant
name
Defining genetic interaction.
@ast
Defining genetic interaction.
@en
type
label
Defining genetic interaction.
@ast
Defining genetic interaction.
@en
prefLabel
Defining genetic interaction.
@ast
Defining genetic interaction.
@en
P2093
P2860
P356
P1476
Defining genetic interaction.
@en
P2093
Guri Giaever
John L Hartman
Ramamurthy Mani
Robert P St Onge
P2860
P304
P356
10.1073/PNAS.0712255105
P407
P577
2008-02-27T00:00:00Z