Exploiting mid-range DNA patterns for sequence classification: binary abstraction Markov models

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Exploiting mid-range DNA patterns for sequence classification: binary abstraction Markov models

Messenger RNA sequences possess specific nucleotide patterns distinguishing them from non-coding genomic sequences. In this study, we explore the utilization of modified Markov models to analyze sequences up to 44 bp, far beyond the 8-bp limit of conventional Markov models, for exon/intron discrimination. In order to analyze nucleotide sequences of this length, their information content is firs...

متن کامل

Abstraction-based probabilistic models for sequence classification

ion-based probabilistic models for sequence classification

متن کامل

Maximum margin hidden Markov models for sequence classification

Discriminative learning methods are known to work well in pattern classification tasks and often show benefits compared to generative learning. This is particularly true in case of model mismatch, i.e. the model cannot represent the true data distribution. In this paper, we derive discriminative maximum margin learning for hidden Markov models (HMMs) with emission probabilities represented by G...

متن کامل

Boosting Input/Output Hidden Markov Models for Sequence Classification

Input/output hidden Markov model (IOHMM) has turned out to be effective in sequential data processing via supervised learning. However, there are several difficulties, e.g. model selection, unexpected local optima and high computational complexity, which hinder an IOHMM from yielding the satisfactory performance in sequence classification. Unlike previous efforts, this paper presents an ensembl...

متن کامل

ذخیره 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: Nucleic Acids Research

سال: 2012

ISSN: 1362-4962,0305-1048

DOI: 10.1093/nar/gks154