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Can machine-learning improve cardiovascular risk prediction using routine clinical data?
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
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Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PloS one, 2017-04, Vol.12 (4), p.e0174944-e0174944 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Weng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Weng et al 2017 Weng et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0174944 ;PMID: 28376093

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2
Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways
Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways
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Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways

PloS one, 2017-09, Vol.12 (9), p.e0184129-e0184129 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Chen et al 2017 Chen et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0184129 ;PMID: 28873455

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3
Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database
Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database
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Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

PloS one, 2017-11, Vol.12 (11), p.e0187336-e0187336 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Choi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Choi et al 2017 Choi et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0187336 ;PMID: 29095872

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4
Development of machine learning models for diagnosis of glaucoma
Development of machine learning models for diagnosis of glaucoma
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Development of machine learning models for diagnosis of glaucoma

PloS one, 2017-05, Vol.12 (5), p.e0177726 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Kim et al 2017 Kim et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0177726 ;PMID: 28542342

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5
Deep learning approach to bacterial colony classification
Deep learning approach to bacterial colony classification
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Deep learning approach to bacterial colony classification

PloS one, 2017-09, Vol.12 (9), p.e0184554-e0184554 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Zieliński et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Zieliński et al 2017 Zieliński et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0184554 ;PMID: 28910352

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6
SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity
SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity
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SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity

PloS one, 2016-08, Vol.11 (8), p.e0155290-e0155290 [Peer Reviewed Journal]

COPYRIGHT 2016 Public Library of Science ;COPYRIGHT 2016 Public Library of Science ;2016 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2016 Li et al 2016 Li et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0155290 ;PMID: 27525735

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7
Open source machine-learning algorithms for the prediction of optimal cancer drug therapies
Open source machine-learning algorithms for the prediction of optimal cancer drug therapies
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Open source machine-learning algorithms for the prediction of optimal cancer drug therapies

PloS one, 2017-10, Vol.12 (10), p.e0186906-e0186906 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Huang et al 2017 Huang et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0186906 ;PMID: 29073279

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8
Prediction of N-linked glycosylation sites using position relative features and statistical moments
Prediction of N-linked glycosylation sites using position relative features and statistical moments
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Prediction of N-linked glycosylation sites using position relative features and statistical moments

PloS one, 2017-08, Vol.12 (8), p.e0181966-e0181966 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Akmal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Akmal et al 2017 Akmal et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0181966 ;PMID: 28797096

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9
Gastric precancerous diseases classification using CNN with a concise model
Gastric precancerous diseases classification using CNN with a concise model
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Gastric precancerous diseases classification using CNN with a concise model

PloS one, 2017-09, Vol.12 (9), p.e0185508-e0185508 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Zhang et al 2017 Zhang et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0185508 ;PMID: 28950010

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10
Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree
Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree
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Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree

PloS one, 2017-08, Vol.12 (8), p.e0181426-e0181426 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Zhou et al 2017 Zhou et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0181426 ;PMID: 28792503

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11
PRmePRed: A protein arginine methylation prediction tool
PRmePRed: A protein arginine methylation prediction tool
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PRmePRed: A protein arginine methylation prediction tool

PloS one, 2017-08, Vol.12 (8), p.e0183318-e0183318 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Kumar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Kumar et al 2017 Kumar et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0183318 ;PMID: 28813517

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12
Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features
Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features
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Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features

PloS one, 2016-04, Vol.11 (4), p.e0153268-e0153268 [Peer Reviewed Journal]

COPYRIGHT 2016 Public Library of Science ;COPYRIGHT 2016 Public Library of Science ;2016 Luo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2016 Luo et al 2016 Luo et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0153268 ;PMID: 27074043

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13
Application of unsupervised analysis techniques to lung cancer patient data
Application of unsupervised analysis techniques to lung cancer patient data
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Application of unsupervised analysis techniques to lung cancer patient data

PloS one, 2017-09, Vol.12 (9), p.e0184370-e0184370 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Lynch et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Lynch et al 2017 Lynch et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0184370 ;PMID: 28910336

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14
Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO-SVM algorithm
Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO-SVM algorithm
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Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO-SVM algorithm

PloS one, 2017-11, Vol.12 (11), p.e0187371-e0187371 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Hameed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Hameed et al 2017 Hameed et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0187371 ;PMID: 29095904

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15
BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches
BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches
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BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches

Briefings in bioinformatics, 2019-07, Vol.20 (4), p.1280-1294 [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. ;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/bbx165 ;PMID: 29272359

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16
Ensemble learning method for the prediction of new bioactive molecules
Ensemble learning method for the prediction of new bioactive molecules
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Ensemble learning method for the prediction of new bioactive molecules

PloS one, 2018-01, Vol.13 (1), p.e0189538-e0189538 [Peer Reviewed Journal]

COPYRIGHT 2018 Public Library of Science ;COPYRIGHT 2018 Public Library of Science ;This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication: https://creativecommons.org/publicdomain/zero/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0189538 ;PMID: 29329334

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17
BioVis Explorer: A visual guide for biological data visualization techniques
BioVis Explorer: A visual guide for biological data visualization techniques
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BioVis Explorer: A visual guide for biological data visualization techniques

PloS one, 2017-11, Vol.12 (11), p.e0187341-e0187341 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Kerren et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Kerren et al 2017 Kerren et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0187341 ;PMID: 29091942

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18
Ensemble Feature Learning of Genomic Data Using Support Vector Machine
Ensemble Feature Learning of Genomic Data Using Support Vector Machine
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Ensemble Feature Learning of Genomic Data Using Support Vector Machine

PloS one, 2016-06, Vol.11 (6), p.e0157330-e0157330 [Peer Reviewed Journal]

2016 Anaissi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2016 Anaissi et al 2016 Anaissi et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0157330 ;PMID: 27304923

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19
DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues
DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues
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DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues

PloS one, 2016-12, Vol.11 (12), p.e0167345-e0167345 [Peer Reviewed Journal]

COPYRIGHT 2016 Public Library of Science ;COPYRIGHT 2016 Public Library of Science ;2016 Ma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2016 Ma et al 2016 Ma et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0167345 ;PMID: 27907159

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20
Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature
Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature
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Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature

PloS one, 2017-11, Vol.12 (11), p.e0187379-e0187379 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Murugesan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 Murugesan et al 2017 Murugesan et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0187379 ;PMID: 29099838

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