Revealing disease-associated pathways by network integration of untargeted metabolomics.
about
Metabox: A Toolbox for Metabolomic Data Analysis, Interpretation and Integrative Exploration.Defective Sphingosine-1-phosphate metabolism is a druggable target in Huntington's disease.Serum Metabolomics of Burkitt Lymphoma Mouse Models.Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data.Pharmacometabolomics Informs Viromics toward Precision Medicine.Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery.Review of emerging metabolomic tools and resources: 2015-2016.Systems approaches in osteoarthritis: Identifying routes to novel diagnostic and therapeutic strategiesMetabolomics: A Primer.Flux control through protein phosphorylation in yeast.Proteomics and metabolomics in ageing research: from biomarkers to systems biology.Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements.Metabolic profiling of presymptomatic Huntington's disease sheep reveals novel biomarkers.Meta-mass shift chemical profiling of metabolomes from coral reefs.Advances in Pharmacotherapy Development: Human Clinical Studies.De novo Synthesis of Sphingolipids Is Defective in Experimental Models of Huntington's Disease.Towards an Understanding of Energy Impairment in Huntington's Disease Brain.Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map.Sphingomyelin and GM1 Influence Huntingtin Binding to, Disruption of, and Aggregation on Lipid Membranes.Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online.A structural examination and collision cross section database for over 500 metabolites and xenobiotics using drift tube ion mobility spectrometry.From correlation to causation: analysis of metabolomics data using systems biology approaches.Sphingolipid Metabolism: A New Therapeutic Opportunity for Brain Degenerative Disorders.Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review.Multi-hierarchical profiling the structure-activity relationships of engineered nanomaterials at nano-bio interfacesUntargeted metabolomics profiles delineate metabolic alterations in mouse plasma during lung carcinoma development using UPLC-QTOF/MS in MSE mode
P2860
Q31158395-477AC176-3B23-4F0D-9F36-B5336994FA00Q33902931-FA09E84D-771D-4DDC-8943-F624402F9CC6Q36262561-97AA783D-F20C-42E9-9D46-65A5AF75D40FQ36396861-7662A1AD-A69B-4078-BF5E-77A794AB64E2Q37368628-DAA2FE10-2704-4A47-9045-AAAADF06A935Q38607534-5605D05D-364C-4EFE-96AA-B0B36C1080D7Q38722471-B9E9ABCB-FA9F-424B-A7CF-8B51A26769FEQ38743031-AF0B6055-A7C6-41B2-972B-65E08A380546Q38757603-EC3A1AD3-B1EB-47AA-8317-AA27CC8FAE79Q38802024-218534DB-87F0-4E9E-8511-5133D11F76E6Q39429374-AF3F63C4-7B5B-42B5-B251-C0973DAE89B2Q41701052-BE30F73D-1811-444C-90D6-9FA531BE5194Q42320470-1ABB082A-F565-4AAB-AE30-B4D2648515DEQ43675104-D206936F-DFA3-42C4-87C1-4BC2CCD414D6Q47182010-0DCA1697-FCF1-4762-BE09-975949DD717AQ47273613-25EEBC58-F2CA-4361-B133-C0DA28F91751Q47442924-F31C1D63-06D4-425B-92B9-8B807784C355Q48209699-40628228-33E3-40CE-B493-A2DCA31565FAQ49331880-54B2410D-DCA0-4DEC-9801-64F6B706FEE9Q52370989-9F463915-D34C-4090-88FC-E9C7F7F7EC12Q52641543-655B3268-79F3-4A86-8876-FF3AE2816F9EQ52808586-1A9CB194-4F6D-4C0F-B499-187B7EA0B83AQ55122433-C7A8612F-D43F-42AA-B47D-10A6CB5B3F66Q55259038-3D5B6B2B-33D1-4A93-A560-A022E9A8E59BQ57793576-5C3B0D16-5791-41A9-AEAC-AB9A80E0F22EQ58715280-07847AF2-9A76-4324-B012-7B0C4178EA0C
P2860
Revealing disease-associated pathways by network integration of untargeted metabolomics.
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
Revealing disease-associated pathways by network integration of untargeted metabolomics.
@en
Revealing disease-associated pathways by network integration of untargeted metabolomics.
@nl
type
label
Revealing disease-associated pathways by network integration of untargeted metabolomics.
@en
Revealing disease-associated pathways by network integration of untargeted metabolomics.
@nl
prefLabel
Revealing disease-associated pathways by network integration of untargeted metabolomics.
@en
Revealing disease-associated pathways by network integration of untargeted metabolomics.
@nl
P2093
P2860
P50
P356
P1433
P1476
Revealing disease-associated pathways by network integration of untargeted metabolomics
@en
P2093
Alan Saghatelian
Julian Avila-Pacheco
Leila Pirhaji
Mathias Leidl
Pamela Milani
Timothy Curran
P2860
P2888
P304
P356
10.1038/NMETH.3940
P577
2016-08-01T00:00:00Z