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1
Predicting functional performance and locomotion of children with cerebral palsy
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Predicting functional performance and locomotion of children with cerebral palsy

Journal of education, health and sport, 2018-08, Vol.8 (9), p.260-268 [Peer Reviewed Journal]

EISSN: 2391-8306 ;DOI: 10.5281/zenodo.1346614

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2
Predicting functional performance and locomotion of children with cerebral palsy
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Predicting functional performance and locomotion of children with cerebral palsy

Journal of education, health and sport, 2018-08, Vol.8 (9) [Peer Reviewed Journal]

EISSN: 2391-8306

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3
DeepDist: real-value inter-residue distance prediction with deep residual convolutional network
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DeepDist: real-value inter-residue distance prediction with deep residual convolutional network

BMC bioinformatics, 2021-01, Vol.22 (1), p.30-30, Article 30 [Peer Reviewed Journal]

COPYRIGHT 2021 BioMed Central Ltd. ;2021. This work is licensed 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. ;The Author(s) 2021 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-021-03960-9 ;PMID: 33494711

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4
Publisher's Note: “On the prediction of noise generated by urban air mobility (UAM) vehicles. I. Integration of fundamental acoustic metrics” [Phys. Fluids 34, 116117 (2022)]
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Publisher's Note: “On the prediction of noise generated by urban air mobility (UAM) vehicles. I. Integration of fundamental acoustic metrics” [Phys. Fluids 34, 116117 (2022)]

Physics of fluids (1994), 2022-12, Vol.34 (12) [Peer Reviewed Journal]

Author(s) ;2022 Author(s). Published under an exclusive license by AIP Publishing. ;ISSN: 1070-6631 ;EISSN: 1089-7666 ;DOI: 10.1063/5.0135250 ;CODEN: PHFLE6

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5
Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI
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Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI

European journal of nuclear medicine and molecular imaging, 2021-06, Vol.48 (6), p.1795-1805 [Peer Reviewed Journal]

The Author(s) 2020 ;The Author(s) 2020. 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: 1619-7070 ;EISSN: 1619-7089 ;DOI: 10.1007/s00259-020-05140-y ;PMID: 33341915

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6
Skilful forecasting of global fire activity using seasonal climate predictions
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Skilful forecasting of global fire activity using seasonal climate predictions

Nature communications, 2018-07, Vol.9 (1), p.2718-9, Article 2718 [Peer Reviewed Journal]

2018. 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. ;Attribution-NonCommercial-NoDerivs 4.0 Spain info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/es/ ;The Author(s) 2018 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-018-05250-0 ;PMID: 30006529

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7
Predictive analysis in industry
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Predictive analysis in industry

Journal of physics. Conference series, 2024-02, Vol.2701 (1), p.012109 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2701/1/012109

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8
Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors
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Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors

Water resources research, 2017-03, Vol.53 (3), p.2199-2239 [Peer Reviewed Journal]

2017. American Geophysical Union. All Rights Reserved. ;ISSN: 0043-1397 ;EISSN: 1944-7973 ;DOI: 10.1002/2016WR019168

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9
Transmembrane helix predictions revisited
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Transmembrane helix predictions revisited

Protein science, 2002-12, Vol.11 (12), p.2774-2791 [Peer Reviewed Journal]

Copyright © 2002 The Protein Society ;Copyright © Copyright 2002 The Protein Society ;ISSN: 0961-8368 ;EISSN: 1469-896X ;DOI: 10.1110/ps.0214502 ;PMID: 12441377

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10
Sixty-five years of the long march in protein secondary structure prediction: the final stretch?
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Sixty-five years of the long march in protein secondary structure prediction: the final stretch?

Briefings in bioinformatics, 2018-05, Vol.19 (3), p.482-494 [Peer Reviewed Journal]

The Author 2016. Published by Oxford University Press. 2016 ;The Author 2016. Published by Oxford University Press. ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbw129 ;PMID: 28040746

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11
Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network
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Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network

Journal of computational chemistry, 2014-10, Vol.35 (28), p.2040-2046 [Peer Reviewed Journal]

Copyright © 2014 Wiley Periodicals, Inc. ;ISSN: 0192-8651 ;EISSN: 1096-987X ;DOI: 10.1002/jcc.23718 ;PMID: 25212657

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12
Deep learning for mining protein data
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Deep learning for mining protein data

Briefings in bioinformatics, 2021-01, Vol.22 (1), p.194-218 [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/bbz156 ;PMID: 31867611

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13
The Indo‐Pacific Maritime Continent Barrier Effect on MJO Ensemble Prediction
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The Indo‐Pacific Maritime Continent Barrier Effect on MJO Ensemble Prediction

Geophysical research letters, 2023-11, Vol.50 (21), p.n/a [Peer Reviewed Journal]

2023. The Authors. ;ISSN: 0094-8276 ;EISSN: 1944-8007 ;DOI: 10.1029/2023GL105462

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14
Biological network analysis with deep learning
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Biological network analysis with deep learning

Briefings in bioinformatics, 2021-03, Vol.22 (2), p.1515-1530 [Peer Reviewed Journal]

The Author(s) 2020. Published by Oxford University Press. 2020 ;The Author(s) 2020. Published by Oxford University Press. ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbaa257 ;PMID: 33169146

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15
Flood Prediction Using Machine Learning Models: Literature Review
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Flood Prediction Using Machine Learning Models: Literature Review

Water (Basel), 2018-10, Vol.10 (11), p.1536 [Peer Reviewed Journal]

2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2073-4441 ;EISSN: 2073-4441 ;DOI: 10.3390/w10111536

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16
Do seasonal-to-decadal climate predictions underestimate the predictability of the real world?
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Do seasonal-to-decadal climate predictions underestimate the predictability of the real world?

Geophysical research letters, 2014-08, Vol.41 (15), p.5620-5628 [Peer Reviewed Journal]

2014. The Authors. ;2014. American Geophysical Union. All Rights Reserved. ;2014. The Authors. 2014 ;ISSN: 0094-8276 ;EISSN: 1944-8007 ;DOI: 10.1002/2014GL061146 ;PMID: 25821271

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17
New measures for assessing model equilibrium and prediction mismatch in species distribution models
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New measures for assessing model equilibrium and prediction mismatch in species distribution models

Diversity & distributions, 2013-10, Vol.19 (10), p.1333-1338 [Peer Reviewed Journal]

Copyright © 2013 John Wiley & Sons Ltd. ;2013 John Wiley & Sons Ltd ;2014 INIST-CNRS ;Copyright © 2013 John Wiley & Sons Ltd ;ISSN: 1366-9516 ;EISSN: 1472-4642 ;DOI: 10.1111/ddi.12100

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18
Wind Power Prediction Based on PSO-SVR and Grey Combination Model
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Wind Power Prediction Based on PSO-SVR and Grey Combination Model

IEEE access, 2019, Vol.7, p.136254-136267 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2019.2942012 ;CODEN: IAECCG

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19
Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods
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Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods

International journal of molecular sciences, 2015-12, Vol.16 (12), p.29829-29842 [Peer Reviewed Journal]

Copyright MDPI AG 2015 ;Attribution ;2015 by the authors; licensee MDPI, Basel, Switzerland. 2015 ;ISSN: 1422-0067 ;ISSN: 1661-6596 ;EISSN: 1422-0067 ;DOI: 10.3390/ijms161226202 ;PMID: 26694353

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20
Predicting Ecological Momentary Assessments in an App for Tinnitus by Learning From Each User's Stream With a Contextual Multi-Armed Bandit
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Predicting Ecological Momentary Assessments in an App for Tinnitus by Learning From Each User's Stream With a Contextual Multi-Armed Bandit

Frontiers in neuroscience, 2022-04, Vol.16, p.836834-836834 [Peer Reviewed Journal]

Copyright © 2022 Shahania, Unnikrishnan, Pryss, Kraft, Schobel, Hannemann, Schlee and Spiliopoulou. ;2022. This work is licensed 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. ;Copyright © 2022 Shahania, Unnikrishnan, Pryss, Kraft, Schobel, Hannemann, Schlee and Spiliopoulou. 2022 Shahania, Unnikrishnan, Pryss, Kraft, Schobel, Hannemann, Schlee and Spiliopoulou ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2022.836834 ;PMID: 35478848

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