Genome-wide chromatin remodeling identified at GC-rich long nucleosome-free regionsFABIA: factor analysis for bicluster acquisitionComplex networks govern coiled-coil oligomerization--predicting and profiling by means of a machine learning approachLong short-term memoryFast model-based protein homology detection without alignmentSelf-Normalizing Neural NetworksHapFABIA: identification of very short segments of identity by descent characterized by rare variants in large sequencing data.A new summarization method for Affymetrix probe level data.Support vector machines for dyadic data.I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data.cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate.APCluster: an R package for affinity propagation clustering.cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rateDEXUS: identifying differential expression in RNA-Seq studies with unknown conditionsAssessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project.An SMO algorithm for the potential support vector machine.Furby: fuzzy force-directed bicluster visualizationpanelcn.MOPS: Copy-number detection in targeted NGS panel data for clinical diagnostics.IBD Sharing between Africans, Neandertals, and Denisovans.Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map.Filtering data from high-throughput experiments based on measurement reliability.Rectified factor networks for biclustering of omics data.GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash EquilibriumRepurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.Informative or noninformative calls for gene expression: a latent variable approach.Machine Learning in Drug DiscoveryLarge-scale comparison of machine learning methods for drug target prediction on ChEMBLMachine Learning in Drug DiscoveryUntersuchungen zu dynamischen neuronalen NetzenSource Separation as a By-Product of RegularizationFréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug DiscoveryAccurate Prediction of Biological Assays with High-Throughput Microscopy Images and Convolutional NetworksDeepRC: Immune repertoire classification with attention-based deep massive multiple instance learning
P50
Q21133925-6AFC264E-9824-4290-B4B8-759D5FDB2000Q24599474-7A521E92-27B8-4131-A3A4-ED34B89D11D6Q24632790-19A2677B-C95C-498C-9B34-B94E651EA10FQ24805158-dad61be8-4334-7dc8-76d8-50999d4e75e0Q28301372-BC3870AA-3631-4DFD-AB38-C6562716E376Q30208689-5c99ecf3-46eb-bf40-b266-38e763dcb0dbQ30686308-F1819BAE-B4AF-4699-8762-5F2458BDA604Q31032288-7C0725ED-4EB3-4991-B674-F115F1A92B0FQ31043807-42E9439D-2915-4366-928F-1F1AB95F69A5Q31132460-78D9E866-069F-4FEF-B85C-4D7ECF7D875BQ33870768-0C896D66-B708-4325-A261-CC49D01045BCQ33954147-AAF57FBC-98FD-43F0-BF64-743625D355E6Q34030518-85A144C4-6F83-4B01-B424-C37670BA76D0Q34372165-CEF6F969-A3EF-4246-81E7-B95AC1798B53Q34440417-2B637A04-2501-441F-B997-549697331388Q34457873-04DECDB4-1A0D-4D94-8FE8-29A52429EA4EQ34719224-35702B7C-219D-4E8C-A64C-75F56A2111C7Q35216264-FC598590-029A-450B-883E-B7439C9C362BQ36356887-D64E2AA8-539B-40DD-9332-63DC279D8DF2Q37738504-49DAB403-0C05-492D-9A74-57D8AC853FBEQ40822032-4342082E-F96B-42B2-BB81-8BDD07C2F904Q42341568-CF3EB136-E2E2-4771-980B-FEAEC6014B34Q42697291-DA170F7D-4E41-418B-9BB3-6455C2074DA9Q44653388-05C7FC3F-10E4-44E9-9E6F-BA74D3182161Q50420935-B948ABEC-5A6E-4D52-A5E1-385F4961B9BDQ51717117-CB27298E-33ED-4AA1-9E68-D06A4463D627Q57009076-6B8B5778-3F7C-4107-AED0-828F9E16D776Q59651982-4DCDE786-03ED-470E-84F0-C18411D46861Q62669363-422D5B23-0557-432E-9CD5-FDE22E3EDB49Q71414518-45d1bfcb-4d47-5107-8cbb-de9b56191eacQ77696593-8ce1cc89-4abe-c119-d9bb-33a8e90eb42eQ91003762-424AB7B3-A161-4A9E-81A3-7E718FC282A5Q92165929-3F73812B-DEF6-4105-98B6-81CE678DBC56Q95610839-3D1E2BCD-0327-43F1-B85E-7BD3B97DEE43
P50
description
Duits bioinformaticus
@nl
German computer scientist
@en
deutscher Informatiker
@de
ríomheolaí Gearmánach
@ga
Німецький кібернетик
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name
Sepp Hochreiter
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Sepp Hochreiter
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Sepp Hochreiter
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Sepp Hochreiter
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Sepp Hochreiter
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Sepp Hochreiter
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Sepp Hochreiter
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Sepp Hochreiter
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塞普·霍赫賴特
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塞普·霍赫赖特
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赛普·霍克赖特
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Sepp Hochreiter
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