LightGBM: An effective miRNA classification method in breast cancer patients

Dehua Wang; Yang Zhang; Yi Zhao

doi:10.1145/3155077.3155079

LightGBM: An effective miRNA classification method in breast cancer patients

Dehua Wang
, Yang Zhang
, Yi Zhao

Harbin Institute of Technology Shenzhen

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

139 Scopus citations

Abstract

miRNAs are small noncoding RNA molecules, mainly responsible for post-transcriptional control of gene expressions. Machine learning is becoming more and more widely used in breast tumor classification and diagnosis. In this paper, we compared the performance of different machine learning methods, such as Random Forest (RF), eXtreme Gradient Boosting(XGBoost) and Light Gradient Boosting Machine(LightGBM), for miRNAs identification in breast cancer patients. The performance comparison of each algorithm was evaluated based on the accuracy and logistic loss and where LightGBM was found better performing in several aspects. hsa-mir-139 was found as an important target for the breast cancer classification. As a powerful tool, LightGBM can be used to identify and classify miRNA target in breast cancer.

Original language	English
Title of host publication	ICCBB 2017 - Proceedings of 2017 International Conference on Computational Biology and Bioinformatics
Publisher	Association for Computing Machinery
Pages	7-11
Number of pages	5
ISBN (Electronic)	9781450353229
DOIs	https://doi.org/10.1145/3155077.3155079
State	Published - 18 Oct 2017
Externally published	Yes
Event	2017 International Conference on Computational Biology and Bioinformatics, ICCBB 2017 - Newark, United States Duration: 18 Oct 2017 → 20 Oct 2017

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2017 International Conference on Computational Biology and Bioinformatics, ICCBB 2017
Country/Territory	United States
City	Newark
Period	18/10/17 → 20/10/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Classification
LightGBM
MiRNA
RF
XGBoost

Access to Document

10.1145/3155077.3155079

Cite this

@inproceedings{52d2886c4e754ba5a5b1b4f046159b78,

title = "LightGBM: An effective miRNA classification method in breast cancer patients",

abstract = "miRNAs are small noncoding RNA molecules, mainly responsible for post-transcriptional control of gene expressions. Machine learning is becoming more and more widely used in breast tumor classification and diagnosis. In this paper, we compared the performance of different machine learning methods, such as Random Forest (RF), eXtreme Gradient Boosting(XGBoost) and Light Gradient Boosting Machine(LightGBM), for miRNAs identification in breast cancer patients. The performance comparison of each algorithm was evaluated based on the accuracy and logistic loss and where LightGBM was found better performing in several aspects. hsa-mir-139 was found as an important target for the breast cancer classification. As a powerful tool, LightGBM can be used to identify and classify miRNA target in breast cancer.",

keywords = "Classification, LightGBM, MiRNA, RF, XGBoost",

author = "Dehua Wang and Yang Zhang and Yi Zhao",

year = "2017",

month = oct,

day = "18",

doi = "10.1145/3155077.3155079",

language = "英语",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery ",

pages = "7--11",

booktitle = "ICCBB 2017 - Proceedings of 2017 International Conference on Computational Biology and Bioinformatics",

address = "美国",

note = "2017 International Conference on Computational Biology and Bioinformatics, ICCBB 2017 ; Conference date: 18-10-2017 Through 20-10-2017",

}

Wang, D, Zhang, Y & Zhao, Y 2017, LightGBM: An effective miRNA classification method in breast cancer patients. in ICCBB 2017 - Proceedings of 2017 International Conference on Computational Biology and Bioinformatics. ACM International Conference Proceeding Series, Association for Computing Machinery , pp. 7-11, 2017 International Conference on Computational Biology and Bioinformatics, ICCBB 2017, Newark, United States, 18/10/17. https://doi.org/10.1145/3155077.3155079

LightGBM: An effective miRNA classification method in breast cancer patients. / Wang, Dehua; Zhang, Yang; Zhao, Yi.
ICCBB 2017 - Proceedings of 2017 International Conference on Computational Biology and Bioinformatics. Association for Computing Machinery , 2017. p. 7-11 (ACM International Conference Proceeding Series).

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

TY - GEN

T1 - LightGBM

T2 - 2017 International Conference on Computational Biology and Bioinformatics, ICCBB 2017

AU - Wang, Dehua

AU - Zhang, Yang

AU - Zhao, Yi

PY - 2017/10/18

Y1 - 2017/10/18

N2 - miRNAs are small noncoding RNA molecules, mainly responsible for post-transcriptional control of gene expressions. Machine learning is becoming more and more widely used in breast tumor classification and diagnosis. In this paper, we compared the performance of different machine learning methods, such as Random Forest (RF), eXtreme Gradient Boosting(XGBoost) and Light Gradient Boosting Machine(LightGBM), for miRNAs identification in breast cancer patients. The performance comparison of each algorithm was evaluated based on the accuracy and logistic loss and where LightGBM was found better performing in several aspects. hsa-mir-139 was found as an important target for the breast cancer classification. As a powerful tool, LightGBM can be used to identify and classify miRNA target in breast cancer.

AB - miRNAs are small noncoding RNA molecules, mainly responsible for post-transcriptional control of gene expressions. Machine learning is becoming more and more widely used in breast tumor classification and diagnosis. In this paper, we compared the performance of different machine learning methods, such as Random Forest (RF), eXtreme Gradient Boosting(XGBoost) and Light Gradient Boosting Machine(LightGBM), for miRNAs identification in breast cancer patients. The performance comparison of each algorithm was evaluated based on the accuracy and logistic loss and where LightGBM was found better performing in several aspects. hsa-mir-139 was found as an important target for the breast cancer classification. As a powerful tool, LightGBM can be used to identify and classify miRNA target in breast cancer.

KW - Classification

KW - LightGBM

KW - MiRNA

KW - RF

KW - XGBoost

UR - https://www.scopus.com/pages/publications/85040593056

U2 - 10.1145/3155077.3155079

DO - 10.1145/3155077.3155079

M3 - 会议稿件

AN - SCOPUS:85040593056

T3 - ACM International Conference Proceeding Series

SP - 7

EP - 11

BT - ICCBB 2017 - Proceedings of 2017 International Conference on Computational Biology and Bioinformatics

PB - Association for Computing Machinery

Y2 - 18 October 2017 through 20 October 2017

ER -

LightGBM: An effective miRNA classification method in breast cancer patients

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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