Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: Evidence for intelligent (algorithm) design. | Meeting report Evidence for intelligent algorithm design Balaji S Srinivasan Chuong B Do and Serafim Batzoglou Addresses Department of Electrical Engineering Stanford University Stanford CA 94305 USA. Department of Developmental Biology Stanford University Stanford CA 94305 USA. Department of Computer Science Stanford University Stanford CA 94305 USA. Correspondence Serafim Batzoglou. Email serafim@ Published 25 July 2006 Genome Biology 2006 7 322 doi gb-2006-7-7-322 The electronic version of this article is the complete one and can be found online at http 2006 7 7 322 2006 BioMed Central Ltd A report on the 10th annual Research in Computational Molecular Biology RECOMB Conference Venice Italy 2-5 April 2006. More than 700 computational biologists convened in beautiful Venice in early April for RECOMB 2006 the 10th annual Conference on Research in Computational Molecular Biology. After 40 talks 6 keynote lectures 180 posters and at least two cameos by the Riemann zeta function several emerging trends in computational biology are apparent. First there has been a strong shift towards empirical studies of molecular evolution and variation with approximately 25 of the papers in this broad area. We expect that this number can only increase in the near future given the ENCODE project http 10005107 and the forthcoming release of several new eukaryotic genomes. Second there is a resurgence of interest in two of the oldest problems in computational biology RNA folding and protein sequence alignment. The interest in noncoding RNAs ncRNAs is driven by experiment recent work on RNA interference RNAi microRNAs ribozymes and the rest of the modern RNA world has once again stimulated interest in the classical problems of ncRNA identification and fold prediction. Advances in protein sequence alignment draw on the development of new algorithmic and machine-learning techniques for principled estimation of gap penalties the penalty
