منابع مشابه
Modeling splice sites with Bayes networks
MOTIVATION The main goal in this paper is to develop accurate probabilistic models for important functional regions in DNA sequences (e.g. splice junctions that signal the beginning and end of transcription in human DNA). These methods can subsequently be utilized to improve the performance of gene-finding systems. The models built here attempt to model long-distance dependencies between non-ad...
متن کاملNonparametric Bayes Modeling of Populations of Networks
Replicated network data are increasingly available in many research fields. In connectomic applications, inter-connections among brain regions are collected for each patient under study, motivating statistical models which can flexibly characterize the probabilistic generative mechanism underlying these network-valued data. Available models for a single network are not designed specifically for...
متن کاملModeling cumulative biological phenomena with Suppes-Bayes causal networks
Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wildtype conditions. Cancer and HIV are two common examples of such diseases, where the mutational load in the cancerous/viral population increases over time. In these cases, selective pressures are often observed along with competition, cooperation and parasitism among d...
متن کاملGene splice sites correlate with nucleosome positions.
Gene sequences in the vicinity of splice sites are found to possess dinucleotide periodicities, especially RR and YY, with the period close to the pitch of nucleosome DNA. This confirms previously reported findings about preferential positioning of splice junctions within the nucleosomes. The RR and YY dinucleotides oscillate counter-phase, i.e., their respective preferred positions are shifted...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2000
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/16.2.152