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Results 1 - 20 of 598  for All Library Resources

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1
Utilizing graph machine learning within drug discovery and development
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Utilizing graph machine learning within drug discovery and development

Briefings in bioinformatics, 2021-11, Vol.22 (6) [Peer Reviewed Journal]

The Author(s) 2021. Published by Oxford University Press. ;The Author(s) 2021. Published by Oxford University Press. 2021 ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbab159 ;PMID: 34013350

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2
Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools
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Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

Briefings in bioinformatics, 2020-03, Vol.21 (2), p.408-420 [Peer Reviewed Journal]

The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2019 ;The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. ;The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bby124 ;PMID: 30649170

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3
Beware of the generic machine learning-based scoring functions in structure-based virtual screening
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Beware of the generic machine learning-based scoring functions in structure-based virtual screening

Briefings in bioinformatics, 2021-05, Vol.22 (3) [Peer Reviewed Journal]

ISSN: 1477-4054 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbaa070

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4
MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes
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MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes

Briefings in bioinformatics, 2024-01, Vol.25 (2) [Peer Reviewed Journal]

The Author(s) 2024. Published by Oxford University Press. 2024 ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbae087

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5
Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions
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Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions

Briefings in bioinformatics, 2021-01, Vol.22 (1), p.497-514 [Peer Reviewed Journal]

The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2020 ;The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. ;The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com ;ISSN: 1477-4054 ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbz173 ;PMID: 31982914

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6
Auto-Kla: a novel web server to discriminate lysine lactylation sites using automated machine learning
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Article
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Auto-Kla: a novel web server to discriminate lysine lactylation sites using automated machine learning

Briefings in bioinformatics, 2023-03, Vol.24 (2) [Peer Reviewed Journal]

The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2023 ;The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbad070 ;PMID: 36869843

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7
Graph convolutional networks for computational drug development and discovery
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Article
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Graph convolutional networks for computational drug development and discovery

Briefings in bioinformatics, 2020-05, Vol.21 (3), p.919-935 [Peer Reviewed Journal]

The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2019 ;The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. ;The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbz042 ;PMID: 31155636

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8
A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction
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Article
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A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction

Briefings in bioinformatics, 2019-01, Vol.20 (1), p.330-346 [Peer Reviewed Journal]

The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2017 ;The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbx126 ;PMID: 30657889

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9
Deep learning for healthcare: review, opportunities and challenges
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Article
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Deep learning for healthcare: review, opportunities and challenges

Briefings in bioinformatics, 2018-11, Vol.19 (6), p.1236-1246 [Peer Reviewed Journal]

The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2017 ;The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbx044 ;PMID: 28481991

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10
A survey on adverse drug reaction studies: data, tasks and machine learning methods
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Article
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A survey on adverse drug reaction studies: data, tasks and machine learning methods

Briefings in bioinformatics, 2021-01, Vol.22 (1), p.164-177 [Peer Reviewed Journal]

The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2019 ;The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. ;The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbz140 ;PMID: 31838499

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11
Deep learning in bioinformatics
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Article
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Deep learning in bioinformatics

Briefings in bioinformatics, 2017-09, Vol.18 (5), p.851-869 [Peer Reviewed Journal]

The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2016 ;The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. ;The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbw068 ;PMID: 27473064

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12
Positive-unlabeled learning in bioinformatics and computational biology: a brief review
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Article
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Positive-unlabeled learning in bioinformatics and computational biology: a brief review

Briefings in bioinformatics, 2022-01, Vol.23 (1) [Peer Reviewed Journal]

The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2021 ;The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. ;The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbab461 ;PMID: 34729589

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13
Trends in the development of miRNA bioinformatics tools
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Article
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Trends in the development of miRNA bioinformatics tools

Briefings in bioinformatics, 2019-09, Vol.20 (5), p.1836-1852 [Peer Reviewed Journal]

The Author(s) 2018. Published by Oxford University Press. 2018 ;The Author(s) 2018. Published by Oxford University Press. ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bby054 ;PMID: 29982332

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14
Evaluation of variable selection methods for random forests and omics data sets
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Article
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Evaluation of variable selection methods for random forests and omics data sets

Briefings in bioinformatics, 2019-03, Vol.20 (2), p.492-503 [Peer Reviewed Journal]

The Author 2017. Published by Oxford University Press. 2017 ;The Author 2017. Published by Oxford University Press. ;ISSN: 1477-4054 ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbx124 ;PMID: 29045534

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15
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
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Article
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Machine learning approaches and databases for prediction of drug–target interaction: a survey paper

Briefings in bioinformatics, 2021-01, Vol.22 (1), p.247-269 [Peer Reviewed Journal]

The Author(s) 2020. Published by Oxford University Press. 2021 ;The Author(s) 2020. Published by Oxford University Press. ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbz157 ;PMID: 31950972

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16
AntiCP 2.0: an updated model for predicting anticancer peptides
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Article
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AntiCP 2.0: an updated model for predicting anticancer peptides

Briefings in bioinformatics, 2021-05, Vol.22 (3) [Peer Reviewed Journal]

ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbaa153

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17
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases
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Article
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Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

Briefings in bioinformatics, 2019-09, Vol.20 (5), p.1878-1912 [Peer Reviewed Journal]

The Author(s) 2018. Published by Oxford University Press. 2018 ;The Author(s) 2018. Published by Oxford University Press. ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bby061 ;PMID: 30084866

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18
iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data
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Article
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iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data

Briefings in bioinformatics, 2020-05, Vol.21 (3), p.1047-1057 [Peer Reviewed Journal]

The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2020 ;The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. ;The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbz041 ;PMID: 31067315

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19
A deep learning method for predicting metabolite–disease associations via graph neural network
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Article
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A deep learning method for predicting metabolite–disease associations via graph neural network

Briefings in bioinformatics, 2022-07, Vol.23 (4) [Peer Reviewed Journal]

The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2022 ;The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbac266

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20
ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides
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Article
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ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides

Briefings in bioinformatics, 2020-09, Vol.21 (5), p.1846-1855 [Peer Reviewed Journal]

The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2019 ;The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbz088

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