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Investigating systematic bias in brain age estimation with application to post‐traumatic stress disordersHuman brain mapping, 2019-08, Vol.40 (11), p.3143-3152 [Peer Reviewed Journal]2019 Wiley Periodicals, Inc. ;COPYRIGHT 2019 John Wiley & Sons, Inc. ;ISSN: 1065-9471 ;EISSN: 1097-0193 ;DOI: 10.1002/hbm.24588 ;PMID: 30924225Full text available |
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Correction to: From data to diagnosis: how machine learning is revolutionizing biomarker discovery in idiopathic inflammatory myopathiesBriefings 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/bbae100Digital Resources/Online E-Resources |
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3 |
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Utilizing graph machine learning within drug discovery and developmentBriefings 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: 34013350Full text available |
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4 |
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Neutral processes related to regional bee commonness and dispersal distances are important predictors of plant–pollinator networks along gradients of climate and landscape conditionsNavngivelse 4.0 Internasjonal © 2022 The Authors ;ISSN: 0906-7590 ;EISSN: 1600-0587 ;DOI: 10.1111/ecog.06379Full text available |
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Material Type: Article
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Neutral processes related to regional bee commonness and dispersal distances are important predictors of plant–pollinator networks along gradients of climate and landscape conditionsISSN: 0906-7590 ;EISSN: 1600-0587 ;DOI: 10.1111/ecog.06379Full text available |
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6 |
Material Type: Article
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Neutral processes related to regional bee commonness and dispersal distances are important predictors of plant–pollinator networks along gradients of climate and landscape conditionsISSN: 0906-7590 ;EISSN: 1600-0587 ;DOI: 10.1111/ecog.06379Full text available |
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Material Type: Article
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Integrative analysis of gene expression and DNA methylation through one‐class logistic regression machine learning identifies stemness features in medulloblastomaMolecular oncology, 2019-10, Vol.13 (10), p.2227-2245 [Peer Reviewed Journal]2019 The Authors. Published by FEBS Press and John Wiley & Sons Ltd. ;COPYRIGHT 2019 John Wiley & Sons, Inc. ;2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1574-7891 ;EISSN: 1878-0261 ;DOI: 10.1002/1878-0261.12557 ;PMID: 31385424Full text available |
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Material Type: Article
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A Surgeon’s Guide to Machine LearningAnnals of surgery open, 2021-09, Vol.2 (3), p.e091-e091 [Peer Reviewed Journal]Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. 2021 ;ISSN: 2691-3593 ;EISSN: 2691-3593 ;DOI: 10.1097/AS9.0000000000000091Full text available |
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Explainable AI for Bioinformatics:Methods, Tools and ApplicationsISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbad236Digital Resources/Online E-Resources |
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10 |
Material Type: Article
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Explainable AI for Bioinformatics:Methods, Tools and ApplicationsISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbad236Digital Resources/Online E-Resources |
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Material Type: Article
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Empirical comparison and analysis of web-based cell-penetrating peptide prediction toolsBriefings 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: 30649170Full text available |
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Material Type: Article
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Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigensBioinformatics, 2020-05, Vol.36 (10), p.3185-3191 [Peer Reviewed Journal]The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2020 ;The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. ;ISSN: 1367-4803 ;EISSN: 1460-2059 ;EISSN: 1367-4811 ;DOI: 10.1093/bioinformatics/btaa119 ;PMID: 32096826Full text available |
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Material Type: Article
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Explainable artificial intelligence enhances the ecological interpretability of black‐box species distribution modelsEcography (Copenhagen), 2021-02, Vol.44 (2), p.199-205 [Peer Reviewed Journal]2020 The Authors. Ecography published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos ;Copyright Wiley Subscription Services, Inc. Feb 2021 ;ISSN: 0906-7590 ;EISSN: 1600-0587 ;DOI: 10.1111/ecog.05360Full text available |
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Material Type: Article
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Generative models for protein sequence modeling: recent advances and future directionsBriefings in bioinformatics, 2023-09, Vol.24 (6) [Peer Reviewed Journal]The Author(s) 2023. Published by Oxford University Press. 2023 ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbad358Digital Resources/Online E-Resources |
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15 |
Material Type: Article
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Clustering single-cell multi-omics data via graph regularized multi-view ensemble learningBioinformatics (Oxford, England), 2024-03, Vol.40 (4) [Peer Reviewed Journal]The Author(s) 2024. Published by Oxford University Press. ;EISSN: 1367-4811 ;DOI: 10.1093/bioinformatics/btae169 ;PMID: 38547401Full text available |
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Material Type: Article
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Predicting ventilator-associated pneumonia with machine learningMedicine (Baltimore), 2021-06, Vol.100 (23), p.e26246-e26246 [Peer Reviewed Journal]Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc. ;Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc. 2021 ;ISSN: 0025-7974 ;EISSN: 1536-5964 ;DOI: 10.1097/MD.0000000000026246 ;PMID: 34115013Full text available |
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17 |
Material Type: Article
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Individual prediction of long‐term outcome in adolescents at ultra‐high risk for psychosis: Applying machine learning techniques to brain imaging dataHuman brain mapping, 2017-02, Vol.38 (2), p.704-714 [Peer Reviewed Journal]2016 Wiley Periodicals, Inc. ;2017 Wiley Periodicals, Inc. ;ISSN: 1065-9471 ;EISSN: 1097-0193 ;DOI: 10.1002/hbm.23410 ;PMID: 27699911Full text available |
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18 |
Material Type: Article
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A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohortAlzheimer's & dementia : translational research & clinical interventions, 2019, Vol.5 (1), p.933-938 [Peer Reviewed Journal]2019 The Authors ;2019 The Authors. ;2019 The Authors 2019 ;ISSN: 2352-8737 ;EISSN: 2352-8737 ;DOI: 10.1016/j.trci.2019.11.001 ;PMID: 31890857Full text available |
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Prediction of outcome in internet‐delivered cognitive behaviour therapy for paediatric obsessive‐compulsive disorder: A machine learning approachInternational journal of methods in psychiatric research, 2018-03, Vol.27 (1), p.n/a [Peer Reviewed Journal]Copyright © 2017 John Wiley & Sons, Ltd. ;Copyright © 2018 John Wiley & Sons, Ltd. ;ISSN: 1049-8931 ;ISSN: 1557-0657 ;EISSN: 1557-0657 ;DOI: 10.1002/mpr.1576 ;PMID: 28752937Full text available |
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20 |
Material Type: Article
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Identification of key biomarkers for predicting CAD progression in inflammatory bowel disease via machine‐learning and bioinformatics strategiesJournal of cellular and molecular medicine, 2024-03, Vol.28 (6), p.e18175-n/a [Peer Reviewed Journal]2024 The Authors. published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd. ;2024 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd. ;2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1582-1838 ;EISSN: 1582-4934 ;DOI: 10.1111/jcmm.18175 ;PMID: 38451044Full text available |