Abstract
Sparse linear models approximate target variable(s) by a sparse linear combination of input variables. Since they are simple, fast, and able to select features, they are widely used in classification and regression. Essentially they are shallow feed-forward neural networks that have three limitations: (1) incompatibility to model nonlinearity of features, (2) inability to learn high-level features, and (3) unnatural extensions to select features in a multiclass case. Deep neural networks are models structured by multiple hidden layers with nonlinear activation functions. Compared with linear models, they have two distinctive strengths: the capability to (1) model complex systems with nonlinear structures and (2) learn high-level representation of features. Deep learning has been applied in many large and complex systems where deep models significantly outperform shallow ones. However, feature selection at the input level, which is very helpful to understand the nature of a complex system, is still not well studied. In genome research, the cis-regulatory elements in noncoding DNA sequences play a key role in the expression of genes. Since the activity of regulatory elements involves highly interactive factors, a deep tool is str...Continue Reading
References
Apr 1, 1997·Trends in Genetics : TIG·M RebhanD Lancet
May 12, 1997·FEBS Letters·I Bredemeier-ErnstR Janknecht
Jun 13, 2006·Neural Computation·Geoffrey E HintonYee-Whye Teh
Jul 29, 2006·Science·G E Hinton, R R Salakhutdinov
Oct 13, 2006·Molecular and Cellular Biology·Christopher R VakocGerd A Blobel
Apr 23, 2010·BMC Genomics·Anton KratzCarsten O Daub
Dec 1, 2010·Nature Reviews. Genetics·Vicky W ZhouBradley E Bernstein
Apr 9, 2011·BMC Genomics·Milos PjanicNicolas Mermod
May 17, 2011·Nature Immunology·Wataru IseKenneth M Murphy
Sep 8, 2012·Nature·UNKNOWN ENCODE Project Consortium
Feb 27, 2013·Molecular and Cellular Biology·Baeck-seung LeePhilip W Tucker
Jun 22, 2013·IEEE Transactions on Pattern Analysis and Machine Intelligence·Yoshua BengioPascal Vincent
Jul 3, 2013·Nature Immunology·Robert NechanitzkyRudolf Grosschedl
Feb 18, 2014·Genomics·Ying WangHaiyan Hu
Mar 13, 2014·Nature Reviews. Genetics·Daria ShlyuevaAlexander Stark
Mar 29, 2014·Nature·Robin AnderssonAlbin Sandelin
Mar 29, 2014·Nature·Alistair R R ForrestYoshihide Hayashizaki
Citations
Dec 25, 2016·Briefings in Bioinformatics·Yifeng LiAlioune Ngom
Mar 15, 2018·BMC Bioinformatics·Diego FioravantiCesare Furlanello
Dec 16, 2017·Nature Reviews. Drug Discovery·Gisbert Schneider
Nov 22, 2016·Molecular Informatics·Alex T MüllerGisbert Schneider
Jun 2, 2018·BMC Bioinformatics·Yifeng LiWyeth W Wasserman
Dec 19, 2018·BMC Bioinformatics·Jie HaoMingon Kang
Dec 24, 2019·BMC Medical Genomics·Jie HaoMingon Kang
Dec 14, 2018·Nature Reviews. Rheumatology·Afshin JamshidiJohanne Martel-Pelletier
May 11, 2018·BMC Systems Biology·Wenying HeQuan Zou
Jul 28, 2018·Frontiers in Genetics·Polina MamoshinaAlex Zhavoronkov
Dec 24, 2019·Briefings in Bioinformatics·Qiang ShiZhidong Xue
Sep 17, 2017·Scientific Reports·Safoora YousefiLee A D Cooper
Jan 3, 2019·Journal of the American Medical Informatics Association : JAMIA·Xing SongMei Liu
Nov 15, 2019·Scientific Reports·Tongjun Gu, Xiwu Zhao
Oct 2, 2020·BMC Bioinformatics·Md Abid Hasan, Stefano Lonardi
Oct 27, 2020·Journal of Pharmacokinetics and Pharmacodynamics·Ruihao HuangHao Zhu
Apr 1, 2020·Computational and Structural Biotechnology Journal·Hang XuMulin Jun Li
Dec 12, 2020·Aging·Alex ZhavoronkovMaria Mitina
Apr 6, 2021·PeerJ. Computer Science·Nikita BhandariKetan Kotecha
Jun 1, 2019·Quantitative Biology·Shashank SinghJian Ma
Jul 3, 2021·Life·Yuchai WanSaid Boumaraf
Aug 4, 2021·Aging and Disease·Fedor GalkinAlex Zhavoronkov
Aug 18, 2020·Journal of Chemical Information and Modeling·Lei DengHui Liu
Dec 7, 2019·Genomics·Ying-Li ChenQian-Zhong Li
Jul 12, 2021··Hui ZhaoBo Zheng
Oct 19, 2020··Zhixuan ChuSheng Li