MLMVFE: A Machine Learning Approach Based on Muli-view Features Extraction for Drug-Disease Associations Prediction

Ying Wang; Ying Lian Gao; Juan Wang; Junliang Shang; Jin Xing Liu

doi:10.1007/978-3-031-23198-8_1

MLMVFE: A Machine Learning Approach Based on Muli-view Features Extraction for Drug-Disease Associations Prediction

Ying Wang
, Ying Lian Gao
, Juan Wang
, Junliang Shang
, Jin Xing Liu

Qufu Normal University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Determining the associations between drugs and diseases plays an important role in the drugs development processes. However, current drug-disease associations (DDAs) prediction methods are too homogeneous for features extraction, so a machine learning approach based on multi-view features extraction (MLMVFE) is proposed for DDAs prediction. Firstly, proteins are introduced to form a new heterogeneous network, which enriches the associations information. Then, nodes features are extracted from two perspectives: network topology and biological knowledge. Finally, the Light Gradient Boosting Machine classifier is utilized to predict DDAs. The MLMVFE achieves satisfactory results on both B-dataset and F-dataset through 10-fold cross-validation. In addition, to further demonstrate the reliability of the MLMVFE, case study is done where clozapine is used as a case. The result suggests that the MLMVFE has the potential to tap into novel DDAs.

Original language	English
Title of host publication	Bioinformatics Research and Applications - 18th International Symposium, ISBRA 2022, Proceedings
Editors	Mukul S. Bansal, Zhipeng Cai, Serghei Mangul
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	1-8
Number of pages	8
ISBN (Print)	9783031231971
DOIs	https://doi.org/10.1007/978-3-031-23198-8_1
State	Published - 2022
Externally published	Yes
Event	18th International Symposium on Bioinformatics Research and Applications, ISBRA 2022 - Haifa, Israel Duration: 14 Nov 2022 → 17 Nov 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13760 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Symposium on Bioinformatics Research and Applications, ISBRA 2022
Country/Territory	Israel
City	Haifa
Period	14/11/22 → 17/11/22

Keywords

Biological knowledge
Drug-disease associations prediction
Multi-view features extraction
Network topology

Access to Document

10.1007/978-3-031-23198-8_1

Cite this

Wang, Y., Gao, Y. L., Wang, J., Shang, J., & Liu, J. X. (2022). MLMVFE: A Machine Learning Approach Based on Muli-view Features Extraction for Drug-Disease Associations Prediction. In M. S. Bansal, Z. Cai, & S. Mangul (Eds.), Bioinformatics Research and Applications - 18th International Symposium, ISBRA 2022, Proceedings (pp. 1-8). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13760 LNBI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-23198-8_1

Wang, Ying ; Gao, Ying Lian ; Wang, Juan et al. / MLMVFE : A Machine Learning Approach Based on Muli-view Features Extraction for Drug-Disease Associations Prediction. Bioinformatics Research and Applications - 18th International Symposium, ISBRA 2022, Proceedings. editor / Mukul S. Bansal ; Zhipeng Cai ; Serghei Mangul. Springer Science and Business Media Deutschland GmbH, 2022. pp. 1-8 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{7630b4c5e1b243a8ac14b4ac94014cdb,

title = "MLMVFE: A Machine Learning Approach Based on Muli-view Features Extraction for Drug-Disease Associations Prediction",

abstract = "Determining the associations between drugs and diseases plays an important role in the drugs development processes. However, current drug-disease associations (DDAs) prediction methods are too homogeneous for features extraction, so a machine learning approach based on multi-view features extraction (MLMVFE) is proposed for DDAs prediction. Firstly, proteins are introduced to form a new heterogeneous network, which enriches the associations information. Then, nodes features are extracted from two perspectives: network topology and biological knowledge. Finally, the Light Gradient Boosting Machine classifier is utilized to predict DDAs. The MLMVFE achieves satisfactory results on both B-dataset and F-dataset through 10-fold cross-validation. In addition, to further demonstrate the reliability of the MLMVFE, case study is done where clozapine is used as a case. The result suggests that the MLMVFE has the potential to tap into novel DDAs.",

keywords = "Biological knowledge, Drug-disease associations prediction, Multi-view features extraction, Network topology",

author = "Ying Wang and Gao, \{Ying Lian\} and Juan Wang and Junliang Shang and Liu, \{Jin Xing\}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 18th International Symposium on Bioinformatics Research and Applications, ISBRA 2022 ; Conference date: 14-11-2022 Through 17-11-2022",

year = "2022",

doi = "10.1007/978-3-031-23198-8\_1",

language = "英语",

isbn = "9783031231971",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

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editor = "Bansal, \{Mukul S.\} and Zhipeng Cai and Serghei Mangul",

booktitle = "Bioinformatics Research and Applications - 18th International Symposium, ISBRA 2022, Proceedings",

}

Wang, Y, Gao, YL, Wang, J, Shang, J & Liu, JX 2022, MLMVFE: A Machine Learning Approach Based on Muli-view Features Extraction for Drug-Disease Associations Prediction. in MS Bansal, Z Cai & S Mangul (eds), Bioinformatics Research and Applications - 18th International Symposium, ISBRA 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13760 LNBI, Springer Science and Business Media Deutschland GmbH, pp. 1-8, 18th International Symposium on Bioinformatics Research and Applications, ISBRA 2022, Haifa, Israel, 14/11/22. https://doi.org/10.1007/978-3-031-23198-8_1

MLMVFE: A Machine Learning Approach Based on Muli-view Features Extraction for Drug-Disease Associations Prediction. / Wang, Ying; Gao, Ying Lian; Wang, Juan et al.
Bioinformatics Research and Applications - 18th International Symposium, ISBRA 2022, Proceedings. ed. / Mukul S. Bansal; Zhipeng Cai; Serghei Mangul. Springer Science and Business Media Deutschland GmbH, 2022. p. 1-8 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13760 LNBI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - MLMVFE

T2 - 18th International Symposium on Bioinformatics Research and Applications, ISBRA 2022

AU - Wang, Ying

AU - Gao, Ying Lian

AU - Wang, Juan

AU - Shang, Junliang

AU - Liu, Jin Xing

PY - 2022

Y1 - 2022

N2 - Determining the associations between drugs and diseases plays an important role in the drugs development processes. However, current drug-disease associations (DDAs) prediction methods are too homogeneous for features extraction, so a machine learning approach based on multi-view features extraction (MLMVFE) is proposed for DDAs prediction. Firstly, proteins are introduced to form a new heterogeneous network, which enriches the associations information. Then, nodes features are extracted from two perspectives: network topology and biological knowledge. Finally, the Light Gradient Boosting Machine classifier is utilized to predict DDAs. The MLMVFE achieves satisfactory results on both B-dataset and F-dataset through 10-fold cross-validation. In addition, to further demonstrate the reliability of the MLMVFE, case study is done where clozapine is used as a case. The result suggests that the MLMVFE has the potential to tap into novel DDAs.

AB - Determining the associations between drugs and diseases plays an important role in the drugs development processes. However, current drug-disease associations (DDAs) prediction methods are too homogeneous for features extraction, so a machine learning approach based on multi-view features extraction (MLMVFE) is proposed for DDAs prediction. Firstly, proteins are introduced to form a new heterogeneous network, which enriches the associations information. Then, nodes features are extracted from two perspectives: network topology and biological knowledge. Finally, the Light Gradient Boosting Machine classifier is utilized to predict DDAs. The MLMVFE achieves satisfactory results on both B-dataset and F-dataset through 10-fold cross-validation. In addition, to further demonstrate the reliability of the MLMVFE, case study is done where clozapine is used as a case. The result suggests that the MLMVFE has the potential to tap into novel DDAs.

KW - Biological knowledge

KW - Drug-disease associations prediction

KW - Multi-view features extraction

KW - Network topology

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U2 - 10.1007/978-3-031-23198-8_1

DO - 10.1007/978-3-031-23198-8_1

M3 - 会议稿件

AN - SCOPUS:85147850009

SN - 9783031231971

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1

EP - 8

BT - Bioinformatics Research and Applications - 18th International Symposium, ISBRA 2022, Proceedings

A2 - Bansal, Mukul S.

A2 - Cai, Zhipeng

A2 - Mangul, Serghei

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 14 November 2022 through 17 November 2022

ER -

Wang Y, Gao YL, Wang J, Shang J, Liu JX. MLMVFE: A Machine Learning Approach Based on Muli-view Features Extraction for Drug-Disease Associations Prediction. In Bansal MS, Cai Z, Mangul S, editors, Bioinformatics Research and Applications - 18th International Symposium, ISBRA 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 1-8. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-23198-8_1

MLMVFE: A Machine Learning Approach Based on Muli-view Features Extraction for Drug-Disease Associations Prediction

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Keywords

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