Predicting gene function in a hierarchical context with an ensemble of classifiers
about
Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq dataSystematic planning of genome-scale experiments in poorly studied speciesThe Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A ReviewA new system for comparative functional genomics of Saccharomyces yeastsGenes Caught In Flagranti: Integrating Renal Transcriptional Profiles With Genotypes and PhenotypesComputationally driven, quantitative experiments discover genes required for mitochondrial biogenesisThe emerging era of genomic data integration for analyzing splice isoform functionSimultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway componentsMining chemical patents with an ensemble of open systemsBeyond accuracy: creating interoperable and scalable text-mining web servicesCommunity challenges in biomedical text mining over 10 years: success, failure and the future.Combining heterogeneous data sources for accurate functional annotation of proteins.MIsoMine: a genome-scale high-resolution data portal of expression, function and networks at the splice isoform level in the mouseUsing interpolation to estimate system uncertainty in gene expression experiments.Incremental learning with SVM for multimodal classification of prostatic adenocarcinomaPredicting gene function using hierarchical multi-label decision tree ensemblesBayesian Markov Random Field analysis for protein function prediction based on network dataPredicting gene function using few positive examples and unlabeled onesFunctional genomics complements quantitative genetics in identifying disease-gene associations.SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells.Enriching for correct prediction of biological processes using a combination of diverse classifiers.A gene-phenotype network for the laboratory mouse and its implications for systematic phenotyping.Small sets of interacting proteins suggest functional linkage mechanisms via Bayesian analogical reasoningSupport vector machine prediction of enzyme function with conjoint triad feature and hierarchical context.Predicting the lethal phenotype of the knockout mouse by integrating comprehensive genomic dataCombFunc: predicting protein function using heterogeneous data sourcesCell-type-specific predictive network yields novel insights into mouse embryonic stem cell self-renewal and cell fateFunctional knowledge transfer for high-accuracy prediction of under-studied biological processesFunctional annotations for the Saccharomyces cerevisiae genome: the knowns and the known unknowns.Predicting gene function using similarity learning.Negative example selection for protein function prediction: the NoGO database.SIFTER search: a web server for accurate phylogeny-based protein function predictionA Factor Graph Approach to Automated GO AnnotationExploiting ontology graph for predicting sparsely annotated gene function.TaggerOne: joint named entity recognition and normalization with semi-Markov ModelsSVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challengesParametric Bayesian priors and better choice of negative examples improve protein function prediction.Using rule-based natural language processing to improve disease normalization in biomedical text.A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC.
P2860
Q21145302-FC7FD854-6F1B-4281-8CFB-DA20B3C95180Q21145344-71F6E0AA-93C3-4098-BD2A-6ABBE4DB84FBQ21285159-F671EDC7-AC41-493C-925E-528428E6282FQ24628747-714999FD-2D97-4B76-8DD5-A48C2BE10742Q26799187-320795B8-00E4-46D7-AD5A-3ED2F4B4AA85Q27939191-2D4936C4-DD6B-4D98-9D51-924FD8A06B73Q28242493-B23DB803-1480-4022-88EA-770A9F504236Q28476223-2582DBB0-ECE0-4D34-B2CA-3E1CF25E08ABQ28834343-678C9B64-8AF3-4891-BF35-CF31007AB8AFQ28971434-9B7FCFB8-0CD8-4BFD-96B8-5BE0FACBE4C3Q30374327-23393C99-AB31-4D1E-ABA9-9F8D8FD676E9Q30604355-DC57D3D0-3F6B-463C-B908-ADC070C13AA6Q30946471-DEE3597B-2225-4E6F-832C-3B4DA8C5ABD1Q31026787-07578E6D-ECC7-4AF2-ADD7-5AE13D74C959Q31157201-C886A50D-D825-438B-B392-A710A185AA48Q33521571-EC58F352-A08C-4DBC-ABA8-6893B1E5A3D8Q33535782-A7698E3C-FFCE-4AC5-A5E7-A2443294D0C2Q33737005-D0E8D488-F494-40BA-A126-5EE62F6EC245Q33750124-A5A9DF91-268C-4D06-9EB1-3D3D5C904303Q33777439-B58C906C-27D1-45B1-857D-23EC3FE36C36Q33909416-75E6C69E-C306-4447-BF03-06765AABA010Q33916576-BC6D5EAB-FF7E-44DC-A6E8-46049FA5CF04Q33936105-56B14584-0E89-4002-B20B-0A6EFA171FF3Q33937535-4B41E46B-2D57-4E44-BB23-B5AE65AD61A3Q34196685-8B3C781D-2CF4-4058-8328-294810E64C07Q34284958-29875136-C8DF-4BAA-8986-D39DCFC63E82Q34611576-6580A325-B63E-4FED-B9C6-310E5641B06EQ34629093-39FD8D82-3BC2-4393-A81A-78F6FC736447Q34670249-A08C380E-5ABB-407C-89D2-6792548661C6Q35050912-4A9201AD-C564-4D6A-9DE9-5417A1F32518Q35186620-50188212-91B2-43BB-A225-29882B6F2FC4Q35810252-0D6FD841-C4AD-4F59-9CBD-0B983E47479FQ35895121-913ED241-AADC-408A-94C1-8F8CE0398151Q35975074-188B0C8C-8126-4848-8BBC-A990BC716692Q36047738-E747FDF3-AF57-4971-ACE3-B9A283EE3D60Q36104137-80A0C7AC-04F6-4751-8B1B-F8C016366831Q36233671-D694D038-0967-42A5-B057-12A556FF57C7Q36789551-A21C61C4-E3E8-4902-9F4C-67E0389B3C62Q37129481-35C4C872-0B84-4CFB-8F65-1DC7B93B8009Q37179379-EC6236B5-B844-4525-B167-0100CEF5CDAB
P2860
Predicting gene function in a hierarchical context with an ensemble of classifiers
description
2008 nî lūn-bûn
@nan
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
2008年论文
@zh
2008年论文
@zh-cn
name
Predicting gene function in a hierarchical context with an ensemble of classifiers
@en
type
label
Predicting gene function in a hierarchical context with an ensemble of classifiers
@en
prefLabel
Predicting gene function in a hierarchical context with an ensemble of classifiers
@en
P2093
P2860
P356
P1433
P1476
Predicting gene function in a hierarchical context with an ensemble of classifiers
@en
P2093
Chad L Myers
David C Hess
Yuanfang Guan
Zafer Barutcuoglu
P2860
P2888
P356
10.1186/GB-2008-9-S1-S3
P478
P577
2008-06-27T00:00:00Z
P5875
P6179
1006959314