SD-MSAEs: Promoter recognition in human genome based on deep feature extraction

Journal of Biomedical Informatics

Volumn 61, Issue , 2016, Pages 55-62

  Xu, Wenxuan ^a,b   Zhang, Li ^a,b   Lu, Yaping ^a,b  

^a SOOCHOW UNIVERSITY   (China)

^b NANJING UNIVERSITY   (China)

Author keywords

Context features; Promoter recognition; Sparse autoencoder; Statistical divergence; Support vector machine

Indexed keywords

BIOMEDICAL SIGNAL PROCESSING; DEEP LEARNING; DNA; DNA SEQUENCES; ENTROPY; EXTRACTION; GENES; INFORMATION THEORY; LEARNING SYSTEMS; SUPPORT VECTOR MACHINES;

AUTO ENCODERS; CLASSIFICATION MODELS; CONTEXT FEATURES; DECISION MODELING; DNA SEQUENCE ANALYSIS; PROMOTER RECOGNITION; SPARSE DISTRIBUTION; STATISTICAL DIVERGENCE;

FEATURE EXTRACTION;

DNA;

ANALYTIC METHOD; ARTICLE; DNA EXTRACTION; DNA SEQUENCE; GENE CONSTRUCT; HUMAN; HUMAN GENOME; INTRON; MOLECULAR RECOGNITION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; PROMOTER REGION; SENSITIVITY AND SPECIFICITY; STATISTICAL DIVERGENCE MULTIPLE SPARSE AUTOENCODERS; SUPPORT VECTOR MACHINE; BIOLOGY;

COMPUTATIONAL BIOLOGY; DNA; GENOME, HUMAN; HUMANS; PROMOTER REGIONS, GENETIC; SEQUENCE ANALYSIS, DNA;

EID: 84961714889     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2016.03.018     Document Type: Article

References (25)

1. Bajic V.B., Chong A., Seah S.H., et al. An intelligent system for vertebrate promoter recognition. IEEE Intell. Syst. 2002, 17(4):64-70.
2. Fickett J.W., Hatzigeorgiou A.G. Eukaryotic promoter recognition. Genome Res. 1997, 7(September):861-878.
3. U P., Dubey J.K., K R.v., Cherian B.S., Gopalakrishnan G., Nair A.S. A novel sequence and context based method for promoter recognition. Bioinformation 2014, 10(4):175-179.
4. Zeng J., Zhao X.Y., Cao X.Q., Yan H. SCS: signal, context, and structure features for genome-wide human promoter recognition. IEEE/ACM Trans. Comput. Biol. Bioinform. 2010, 7(3):550-562.
5. Deng J., Liang H., Zhang R., Ying G., Xie X., Yu J., Fan D., Hao X. Methylated CpG site count of dapper homolog 1 (DACT1) promoter prediction the poor survival of gastric cancer. Am. J. Cancer Res. 2014, 4(September):518-527.
6. Huang W.L., Tung C.W., Liaw C., Huang H.L., Ho S.Y. Rule-based knowledge acquisition method for promoter prediction in human and Drosophila species. Scient. World J. 2014, 2014:1-14.
7. Werner T. The state of the art of mammalian promoter recognition. Brief Bioinform. 2014, 2014.
8. Wu S., Xie X., Liew A.W.-C., Yan H. Eukaryotic promoter prediction based on relative entropy and positional information. Phys. Rev. E 2007, 75:041908.
9. Vinga S. Information theory applications for biological sequence analysis. Brief Bioinform. 2014, 15(3):376-389.
10. Jia Zeng, Xiao-Qin Cao, Hong Yan, Human promoter recognition using Kullback-Leibler divergence, in: International Conference on Machine Learning and Cybernetics, 2007, vol. 6, August 2007, pp. 3319-3325.
11. E.P. Wigner, Information-theoretic algorithms in bioinformatics and bio-/medical-imaging: a review, in: 2011 International Conference on Recent Trends in Information Technology (ICRTIT), June 2011, pp. 183-188.
12. Nielsen F., Nock R. Sided and symmetrized Bregman centroids. IEEE Trans. Inf. Theory 2009, 55(6):2882-2904.
13. Anwar F., Baker S.M., Jabid T., Mehedi Hasan M., Shoyaib M., Khan H., Walshe R. Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach. BMC Bioinf. 2008, 9(1):414-418.
14. Zhao X.Y., Zhang J., Chen Y.Y., Li Q., Yang T., Pian C., Zhang L.Y. Promoter recognition based on the maximum entropy hidden Markov model. Comput. Biol. Med. 2014, 51(August):73-81.
15. Li Y., Lee K.K., Walsh S., Smith C., Hadingham S., Sorefan K., Cawley G., Bevan M.W. Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine. Genome Res. 2006, 16(3):414-427.
16. Lu J., Luo L. Prediction for human transcription start site using diversity measure with quadratic discriminant. Bioinformation 2008, 2(7):316-321.
17. Wang J., Ungar L.H., Tseng H., Hannenhalli S. MetaProm: a neural network based meta-predictor for alternative human promoter prediction. BMC Genom. 2007, 8(October):374.
18. A. Ng, Sparse autoencoder, in: CS294A Lecture Notes for Stanford University, 2011.
19. Baldi P., Lu Z. Complex-valued autoencoders. Neural Netw. 2012, 33(September):136-147.
20. A. Ng, J. Ngiam, C.Y. Foo, Y. Mai, C. Suen, UFLDL tutorial: building deep networks for classification, an online tutorial, 2013.
21. Suzuki A., Wakaguri H., Yamashita R., Kawano S., Tsuchihara K., Sugano S., Suzuki Y., Nakai K. "DBTSS as an integrative platform for transcriptome, epigenome and genome sequence variation data". Nucl. Acids Res. 2014, 43(November). (Database issue D87-91).
22. Vapnik V., Cortes C. Support-vector networks. Mach. Learn. 1995, 20(3):273-297.
23. Saxonov S., Daizadeh I., Fedorov A., Gilbert W. EID: the exon-intron database-an exhaustive database of proteincoding intron-containing genes. Nucl. Acids Res. 2000, 28:185-190.
24. Pesole G., Liuni S., Grillo G., Licciulli F., Mignone F., Gissi C., Saccone C. UTRdb and UTRsite: specialized databases of sequences and functional elements of 5' and 3' untranslated regions of eukaryotic mRNAs. Nucl. Acids Res. 2002, 30:335-340.
25. Bajic V.B. Comparing the success of different prediction programs in sequence analysis: a review. Brief Bioinform. 2000, 1(3):214-228.