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

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

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.

源语言	英语
主期刊名	Bioinformatics Research and Applications - 18th International Symposium, ISBRA 2022, Proceedings
编辑	Mukul S. Bansal, Zhipeng Cai, Serghei Mangul
出版商	Springer Science and Business Media Deutschland GmbH
页	1-8
页数	8
ISBN（印刷版）	9783031231971
DOI	https://doi.org/10.1007/978-3-031-23198-8_1
出版状态	已出版 - 2022
已对外发布	是
活动	18th International Symposium on Bioinformatics Research and Applications, ISBRA 2022 - Haifa, 以色列期限: 14 11月 2022 → 17 11月 2022

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	13760 LNBI
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	18th International Symposium on Bioinformatics Research and Applications, ISBRA 2022
国家/地区	以色列
市	Haifa
时期	14/11/22 → 17/11/22

访问文件

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

其它文件与链接

链接到 Scopus 的出版物

引用此

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. 在 M. S. Bansal, Z. Cai, & S. Mangul (编辑), Bioinformatics Research and Applications - 18th International Symposium, ISBRA 2022, Proceedings (页码 1-8). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 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 等. / 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. 编辑 / Mukul S. Bansal ; Zhipeng Cai ; Serghei Mangul. Springer Science and Business Media Deutschland GmbH, 2022. 页码 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",

pages = "1--8",

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. 在 MS Bansal, Z Cai & S Mangul (编辑), 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), 卷 13760 LNBI, Springer Science and Business Media Deutschland GmbH, 页码 1-8, 18th International Symposium on Bioinformatics Research and Applications, ISBRA 2022, Haifa, 以色列, 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 等.
Bioinformatics Research and Applications - 18th International Symposium, ISBRA 2022, Proceedings. 编辑 / Mukul S. Bansal; Zhipeng Cai; Serghei Mangul. Springer Science and Business Media Deutschland GmbH, 2022. 页码 1-8 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 13760 LNBI).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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

UR - https://www.scopus.com/pages/publications/85147850009

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. 在 Bansal MS, Cai Z, Mangul S, 编辑, Bioinformatics Research and Applications - 18th International Symposium, ISBRA 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. 页码 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

摘要

出版系列

会议

访问文件

其它文件与链接

指纹图谱

引用此