tailieunhanh - Báo cáo hóa học: " Research Article Novel Data Fusion Method and Exploration of Multiple Information Sources for "

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Novel Data Fusion Method and Exploration of Multiple Information Sources for | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 235795 15 pages doi 2010 235795 Research Article Novel Data Fusion Method and Exploration of Multiple Information Sources for Transcription Factor Target Gene Prediction Xiaofeng Dai 1 2 Olli Yli-Harja 1 and Harri Lahdesmaki1 3 1 Department of Signal Processing Tampere University of Technology . Box 553 33101 Tampere Finland 2 Institute of Molecular Medicine University of Helsinki . Box20 00014 Helsinki Finland 3 Department of Information and Computer Science Aalto University School of Science and Technology . Box 15400 00076Aalto Finland Correspondence should be addressed to Xiaofeng Dai and Harri Lahdesmaki Received 17 April 2010 Revised 29 June 2010 Accepted 10 August 2010 Academic Editor Byung-Jun Yoon Copyright 2010 Xiaofeng Dai et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Background. Revealing protein-DNA interactions is a key problem in understanding transcriptional regulation at mechanistic level. Computational methods have an important role in predicting transcription factor target gene genomewide. Multiple data fusion provides a natural way to improve transcription factor target gene predictions because sequence specificities alone are not sufficient to accurately predict transcription factor binding sites. Methods. Here we develop a new data fusion method to combine multiple genome-level data sources and study the extent to which DNA duplex stability and nucleosome positioning information either alone or in combination with other data sources can improve the prediction of transcription factor target gene. Results. Results on a carefully constructed test set of verified binding sites in mouse genome demonstrate that our