Predicting gene dosage using genomic sequence data
نویسندگان
چکیده
منابع مشابه
Predicting antimicrobial susceptibilities for Escherichia coli and Klebsiella pneumoniae isolates using whole genomic sequence data
OBJECTIVES Whole-genome sequencing potentially represents a single, rapid and cost-effective approach to defining resistance mechanisms and predicting phenotype, and strain type, for both clinical and epidemiological purposes. This retrospective study aimed to determine the efficacy of whole genome-based antimicrobial resistance prediction in clinical isolates of Escherichia coli and Klebsiella...
متن کاملPredicting Gene Expression from Sequence
We describe a systematic genome-wide approach for learning the complex combinatorial code underlying gene expression. Our probabilistic approach identifies local DNA-sequence elements and the positional and combinatorial constraints that determine their context-dependent role in transcriptional regulation. The inferred regulatory rules correctly predict expression patterns for 73% of genes in S...
متن کاملGene Recognition in Cyanobacterium Genomic Sequence Data Using the Hidden Markov Model
We have developed a hidden Markov model (HMM) to detect the protein coding regions within one megabase contiguous sequence data, registered in a database called GenBank in eight entries, of the genome of cyanobacterium, Synechocystis sp. strain PCC6803. Detection of the coding regions in the database entry was performed by using HMM whose parameters were determined by taking the statistics from...
متن کاملA Likelihood Ratio Test of Speciation with Gene Flow Using Genomic Sequence Data
Genomic sequence data may be used to test hypotheses about the process of species formation. In this paper, I implement a likelihood ratio test of variable species divergence times over the genome, which may be considered a test of the null model of allopatric speciation without gene flow against the alternative model of parapatric speciation with gene flow. Two models are implemented in the li...
متن کاملPredicting Type2 Diabetes Using Data Mining Algorithms
Background and purpose: Today, information systems and databases are widely used and in order to achieve higher accuracy and speed in making diagnosis, preventing the diseases, and choosing treatments they should be merged with traditional methods. This study aimed at presenting an accurate system for diagnosis of diabetes using data mining and a heuristic method combining neural network and pa...
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: The FASEB Journal
سال: 2008
ISSN: 0892-6638,1530-6860
DOI: 10.1096/fasebj.22.1_supplement.798.2