Although a variety of statistical methods for clustering and inferring gene networks from expression profiles have been proposed, most of these are not tailored to simultaneously treat expression data collected under multiple stimulation conditions.
In this work, we present a novel method for learning and inferring the sparse representation of disparities using sparse autoencoder, which is then used to define the sparsity prior.
We have proposed LDALR, a practical method for discovering and inferring appropriate eligibility criteria in clinical trial protocols without labeled data.
Irrespective of the best protocol used for generating short sequence tags, our study provides insight into the potential of short tag sequencing as a method for investigating and inferring differential expression when different reference transcriptomes are available.
Topics include advanced Perl programming, R and Bioconductor, sequence alignment, MySQL database (DBI), web programming and services (CGI), microarray analysis, and methods for inferring and analyzing regulatory, protein-protein interaction, and metabolite networks.
A comparison of MLVA with PFGE for typing of the snake-related isolates confirmed the MLVA13-Lyon scheme to be a robust method for quickly discriminating and inferring genetic relatedness among environmental isolates.
This dataset is used by phylogenetic inference strategies that utilize the concatenation-based species tree construction methods with the maximum likelihood, maximum parsimony, and neighbor-joining gene tree inference methods (see "Inferring gene trees" and "Inferring species trees").
A principal focus is on specialized experimental and modeling approaches to obtain such insights; these include molecular-level models, various modes of microscopy, and chromatographic and other methods for inferring intracolumn and intraparticle behavior.
Uses instruments for trace gas and aerosol measurements and methods for inferring fundamental information from these measurements.
