Efficient Mode of Action Identification by Support Vector Machine Regression

被引：0
|
作者：
Bevilacqua, Vitoantonio [1 ]
Pannarale, Paolo [1 ]
机构：
[1] Politecn Bari, Dipartimento Elettrotecn & Elettron, I-70125 Bari, Italy
来源：
EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS | 2012年 / 304卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Discovering the molecular targets of compounds or the cause of physiological conditions, among the multitude of known genes, is one of the major challenges of bioinformatics. Our approach has the advantage of not needing control samples, libraries or numerous assays. The so far proposed implementations of this strategy are computationally demanding. Our solution, while performing comparably to state of the art algorithms in terms of discovered targets, is more efficient in terms of memory and time consumption.
引用
收藏
页码：191 / 196
页数：6
相关论文
共 50 条
  • [31] Nonlinear dynamic system identification using least squares support vector machine regression
    Wang, XD
    Ye, MY
    PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2004, : 941 - 945
  • [32] Online support vector regression for system identification
    Yu, ZH
    Fu, X
    Li, YL
    ADVANCES IN NATURAL COMPUTATION, PT 2, PROCEEDINGS, 2005, 3611 : 627 - 630
  • [33] Parallel randomized sampling for support vector machine (SVM) and support vector regression (SVR)
    Lu, Yumao
    Roychowdhury, Vwani
    KNOWLEDGE AND INFORMATION SYSTEMS, 2008, 14 (02) : 233 - 247
  • [34] Parallel randomized sampling for support vector machine (SVM) and support vector regression (SVR)
    Yumao Lu
    Vwani Roychowdhury
    Knowledge and Information Systems, 2008, 14 : 233 - 247
  • [35] Interval regression analysis using support vector machine and quantile regression
    Hwang, CH
    Hong, DH
    Na, E
    Park, H
    Shim, J
    FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PT 1, PROCEEDINGS, 2005, 3613 : 100 - 109
  • [36] A simple quantile regression via support vector machine
    Hwang, C
    Shim, J
    ADVANCES IN NATURAL COMPUTATION, PT 1, PROCEEDINGS, 2005, 3610 : 512 - 520
  • [37] A Novel Feedback Support Vector Regression Machine Method
    Chen, Yao
    Song, Zhaoqing
    2016 INTERNATIONAL CONFERENCE ON MECHANICAL MANUFACTURING AND ENERGY ENGINEERING (ICMMEE 2016), 2016, : 224 - 229
  • [38] Total Least Square Support Vector Machine for Regression
    Fu, Guanghui
    Hu, Guanghua
    INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL 1, PROCEEDINGS, 2008, : 271 - 275
  • [39] GPR Data Regression and Clustering by the Fuzzy Support Vector Machine and Regression
    Hosseinzadeh, Shahram
    Shaghaghi, Mehdi
    PROGRESS IN ELECTROMAGNETICS RESEARCH M, 2020, 93 : 175 - 184
  • [40] Liver fat analysis using optimized support vector machine with support vector regression
    Pushpa, B.
    Baskaran, B.
    Vivekanandan, S.
    Gokul, P.
    TECHNOLOGY AND HEALTH CARE, 2023, 31 (03) : 867 - 886
12345