Using bioinformatics to predict the functional impact of SNVs
about
Annotate-it: a Swiss-knife approach to annotation, analysis and interpretation of single nucleotide variation in human diseaseBioinformatics for personal genome interpretationConformational dynamics of nonsynonymous variants at protein interfaces reveals disease association.Influence of V54M mutation in giant muscle protein titin: a computational screening and molecular dynamics approach.PinSnps: structural and functional analysis of SNPs in the context of protein interaction networksMSV3d: database of human MisSense Variants mapped to 3D protein structure.Interpretation of the consequences of mutations in protein kinases: combined use of bioinformatics and text mining.Structural dynamics flexibility informs function and evolution at a proteome scale.Predicting the impact of deleterious mutations in the protein kinase domain of FGFR2 in the context of function, structure, and pathogenesis--a bioinformatics approach.Common sequence variants affect molecular function more than rare variants?Integrating in silico prediction methods, molecular docking, and molecular dynamics simulation to predict the impact of ALK missense mutations in structural perspectiveGene-based tests of associationHuman germline and pan-cancer variomes and their distinct functional profilesPrioritization of pathogenic mutations in the protein kinase superfamilyFunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest modelVariation Interpretation Predictors: Principles, Types, Performance, and Choice.WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation.Bioinformatics challenges for personalized medicinewKinMut: an integrated tool for the analysis and interpretation of mutations in human protein kinases.Phenotype-optimized sequence ensembles substantially improve prediction of disease-causing mutation in cystic fibrosis.Missense variants in CFTR nucleotide-binding domains predict quantitative phenotypes associated with cystic fibrosis disease severity.Better prediction of functional effects for sequence variants.GenomeRunner: automating genome explorationIncorporating molecular and functional context into the analysis and prioritization of human variants associated with cancerExonic Splicing Mutations Are More Prevalent than Currently Estimated and Can Be Predicted by Using In Silico Tools.Towards Increasing the Clinical Relevance of In Silico Methods to Predict Pathogenic Missense VariantsHuman genomic disease variants: a neutral evolutionary explanation.Evaluating tools for transcription factor binding site prediction.Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties.An extended set of yeast-based functional assays accurately identifies human disease mutations.A Molecular Evolutionary Reference for the Human VariomeGenotype to phenotype via network analysis.Recent approaches to the prioritization of candidate disease genes.Interpretation of genetic variants.Locus-specific databases in cancer: what future in a post-genomic era? The TP53 LSDB paradigm.Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation.Protein function in precision medicine: deep understanding with machine learning.PON-P: integrated predictor for pathogenicity of missense variants.MICO: A meta-tool for prediction of the effects of non-synonymous mutations.IMHOTEP-a composite score integrating popular tools for predicting the functional consequences of non-synonymous sequence variants.
P2860
Q28706718-152C5748-E853-4B50-A9D2-715994BBB770Q28728835-D7150320-74E1-497E-9135-DE98D4F2509EQ30370239-C8EA6C4A-45E0-46D7-BE41-10C46A7C6BDEQ30387461-7D0AB68D-92F5-4854-88BF-31BD58A67FF5Q30387798-5A42BFDF-738A-4A93-9FBB-3669FF7B30FDQ30415233-4E199506-8991-4B80-A111-38B4B0CC6DD0Q30422192-518E14BE-D9AE-4ABF-8676-B26C3C0A2AB0Q30431577-D049E0B8-DDAE-4934-8152-4432F0D7DDC9Q30431723-3B4BF2CD-89EA-495A-89C2-DC9D7511CD28Q33686410-1800C03F-5AE5-436B-B35D-361B652AAB8CQ33903573-370EA65C-2DA4-44BD-8D3C-0AC70E24AE95Q33987507-309B607E-9276-4330-B44B-452229C8A4E3Q34312953-1C18ED84-61DD-4DDF-BB94-F058EB0E12E4Q34324941-4FFD46C4-B569-4C69-83D0-7B5211BA4A61Q34399466-73805DA2-345E-4ED0-9D47-03D783AB1651Q34518645-74F6714C-920E-44B4-B96F-A311AF4C8203Q34789974-2B7DCC99-9304-45C1-8E40-BAD1F8B7E908Q35051893-E805F744-42BB-4E9C-8C20-2E534A455CE6Q35056439-500A4818-A11D-4A2B-B5F1-ADC14ABE1BD5Q35190186-E1A48127-7720-47B8-AC11-130A8BA4DE19Q35197386-0216D25B-BD53-4F55-98DB-EADCB2FA9DD3Q35673957-7162108A-54D6-45B2-B76E-C1CB047170D0Q35708487-2B6234A8-80BE-4BB4-B83D-EB8CB62C4BC1Q35751267-95B887B3-3B92-43C5-A6FD-7AD0DE249846Q35892850-160B8FFA-23C8-4F9D-911E-D6C740481B29Q36015435-929F3318-4C5D-4C23-A7D4-D989094DD07DQ36130827-AD566FF9-C3E3-4548-A029-5152409EFC6BQ36180626-860D3571-C391-4F84-8BF0-FC0F048B6790Q36405487-2A65EB72-3A1E-4CFD-9E77-779AB85F39E7Q36893770-EBD829C6-A4E3-4E62-ACE5-F54C3FAA7459Q37229420-ED51C0A5-4CD7-49A6-A4F9-19EE6DD1BB0DQ37399103-341CCD81-BED5-4747-B4B6-CF515B96B22CQ38018005-125339F8-715F-416B-BB08-33BF9AB1C4DBQ38171755-3DEE66DF-52DC-47E6-9EDF-AA3A87B2FBA8Q38183344-08780D69-22EE-4982-949E-161BA69934A7Q38857623-40A5C025-B885-4FEF-8B92-7828E726C75DQ38898708-E59747C1-8287-45AD-A88E-8D7D8AB50D25Q39364474-97539434-A1F1-4E2D-B28B-E8B5A644A153Q42202597-488D729F-515D-443F-ACE6-C37DE9AD796FQ42292082-CC5DC338-95D4-4C0B-8F92-4A84D3F4C559
P2860
Using bioinformatics to predict the functional impact of SNVs
description
2011 nî lūn-bûn
@nan
2011 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի փետրվարին 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
Using bioinformatics to predict the functional impact of SNVs
@ast
Using bioinformatics to predict the functional impact of SNVs
@en
Using bioinformatics to predict the functional impact of SNVs
@nl
type
label
Using bioinformatics to predict the functional impact of SNVs
@ast
Using bioinformatics to predict the functional impact of SNVs
@en
Using bioinformatics to predict the functional impact of SNVs
@nl
prefLabel
Using bioinformatics to predict the functional impact of SNVs
@ast
Using bioinformatics to predict the functional impact of SNVs
@en
Using bioinformatics to predict the functional impact of SNVs
@nl
P2860
P3181
P356
P1433
P1476
Using bioinformatics to predict the functional impact of SNVs
@en
P2093
Melissa S Cline
Rachel Karchin
P2860
P3181
P356
10.1093/BIOINFORMATICS/BTQ695
P407
P577
2011-02-15T00:00:00Z