About: Architectures and accuracy of artificial neural network for disease classification from omics data

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scientific article published on 04 March 2019

Attributes	Values
rdf:type	Item
description	wetenschappelijk artikel (nl) наукова стаття, опублікована в березні 2019 (uk) im März 2019 veröffentlichter wissenschaftlicher Artikel (de) artículu científicu espublizáu en marzu de 2019 (ast) scientific article published on 04 March 2019 (en)
publication date	wds:Q62489706-AB967055-F143-4681-80E0-2A23AFCB2480
publication date	2019-03-04 00:00:00Z (xsd:dateTime)
language of work or name	wds:Q62489706-205A2CA8-6A9A-4306-8372-E64B061D6E15
language of work or name	English
exact match	wds:Q62489706-09AB11E8-F61D-49CA-A9F2-9E732C33D2E9
exact match	https://scigraph.springernature.com/pub.10.1186/s12864-019-5546-z
cites work	wds:Q62489706-20CF086E-E862-4FBC-B561-FF028EEE288D wds:Q62489706-23A6438E-FF11-49C2-8144-05E07B89ED70 wds:Q62489706-3D1B5796-B312-4647-9F44-F70944FA8C6C wds:Q62489706-48A57BE8-9C5E-41E8-87B8-E5EAD797FC58 wds:Q62489706-5163F7FF-3AEE-4D43-833E-7ED05EBCF3DA wds:Q62489706-5997E4C4-05D8-4FE9-A2B4-FDD7F6B875F9 wds:Q62489706-6001C576-FB17-487F-A437-5F336F915A36 wds:Q62489706-62A8CABC-F527-4E14-8126-FFAA1A4634C6 wds:Q62489706-6E5E8075-A974-4A93-AD22-678865ECD324 wds:Q62489706-95B042C9-0E55-47C4-B19D-1FC1EF120E3D wds:Q62489706-9AED07B1-4583-4230-BA30-16EF4791457D wds:Q62489706-B2C1CF2B-4539-4C05-A4AE-52DA7362FEB1 wds:Q62489706-B876C02D-413D-4EB6-BE9A-EE66CCD016D0 wds:Q62489706-C8C3EFD1-44CD-4A15-A02D-1E0A37C8FCF8 wds:Q62489706-D2B3C05F-5988-44BA-A076-89E22CD1B985 wds:Q62489706-E215F9C6-B050-4CB2-A46C-E1C9C137E759 wds:Q62489706-E83FA433-75EC-4CD7-8D2E-FB7FA1169B16
cites work	The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies Deep learning Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions TCGA2STAT: simple TCGA data access for integrated statistical analysis in R. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Sample normalization methods in quantitative metabolomics Deepr: A Convolutional Net for Medical Records Convolutional neural network architectures for predicting DNA-protein binding Deep learning in bioinformatics Multi-Layer and Recursive Neural Networks for Metagenomic Classification Phylogenetic convolutional neural networks in metagenomics. Using recurrent neural network models for early detection of heart failure onset. Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM). Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.
author name string	wds:Q62489706-8C2B5DBE-FFCA-4EBB-B853-07182D170323 wds:Q62489706-BDC40452-2F53-4E77-9EC5-C71FA5A3ADD5
author name string	Hui Yu David C Samuels
rdfs:label	Architectures and accuracy of artificial neural network for disease classification from omics data (en) Architectures and accuracy of artificial neural network for disease classification from omics data (nl)
skos:prefLabel	Architectures and accuracy of artificial neural network for disease classification from omics data (en) Architectures and accuracy of artificial neural network for disease classification from omics data (nl)
name	Architectures and accuracy of artificial neural network for disease classification from omics data (en) Architectures and accuracy of artificial neural network for disease classification from omics data (nl)
author	wds:Q62489706-96310D3A-C1E1-49A5-B3C2-07B9BA9BDD31 wds:Q62489706-DF744337-D4AE-4943-A8B7-C83478C53673
author	Ying-Yong Zhao Yan Guo
title	wds:Q62489706-5D0D3905-F793-42FE-8C91-4FE509EB70DB
title	Architectures and accuracy of artificial neural network for disease classification from omics data (en)
page(s)	wds:Q62489706-95243EF9-0937-40B5-9224-39F938E72476
page(s)	167
instance of	wds:Q62489706-615BB54D-6A4F-4C36-8A22-585054997F9F
instance of	scholarly article
main subject	wds:Q62489706-55802455-2F3A-4FA4-B32F-DCD1EC2C9F21 wds:Q62489706-7442B760-92BE-47F4-B599-275E8B5CD884 wds:Q62489706-D48EEBA7-7C6E-47FF-AB4D-D510F815BACE
main subject	artificial neural network omics biotechnology
PubMed ID	wds:Q62489706-DF229D17-C71C-4A91-9A74-D65CC820A061
PubMed ID	http://rdf.ncbi.nlm.nih.gov/pubchem/reference/30832569
PubMed ID	30832569
published in	wds:Q62489706-AE2F9308-298F-4029-A8E6-A45246F8EABE
published in	BMC Genomics
issue	wds:Q62489706-58514156-D0DB-4BAD-8E2E-D7AEC43762E1
volume	wds:Q62489706-0EAF92A6-B07C-4C21-81EA-F62618E079B0
issue	1
volume	20
DOI	wds:Q62489706-DEA55F16-F970-40DC-92DE-85BBD07E9136
DOI	http://dx.doi.org/10.1186/S12864-019-5546-Z
DOI	10.1186/S12864-019-5546-Z
copyright license	wds:Q62489706-e9c1f4b5-9218-4847-b2ac-30f2153a4349
copyright status	wds:Q62489706-f6ebb073-fcbd-44db-bd0c-608c390163ed
copyright license	Creative Commons Attribution 4.0 International
copyright status	copyrighted
describes a project that uses	wds:Q62489706-96E1ED37-42FA-48E8-A701-73257723E79F
describes a project that uses	Bayes' theorem
Dimensions Publication ID	wds:Q62489706-A95B8A4B-DA51-40EA-A888-29DD91309AC7
Dimensions Publication ID	1112527551
PMCID	wds:Q62489706-184FA530-656C-4850-921C-B176E8F5D968
PMCID	6399893
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q62489706
is cites work of	Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection Opportunities for Artificial Intelligence in Advancing Precision Medicine Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data
is cites work of	wds:Q95300224-EFA42C37-8723-4F93-AB12-5D06480A6E0D

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