Orzechowski, P. | bioRxiv Science

bioRxiv2017

runibic: a Bioconductor package for parallel row-based biclustering of gene expression data

MotivationBiclustering (called also co-clustering) is an unsupervised technique of simultaneous analysis of rows and columns of input matrix. From the first application to gene expression data, multiple algorithms have been proposed. Only a handful of them were able to provide accurate results and were fast enough to be suitable for large-scale genomic datasets.\n\nResultsIn this paper we introduce a Bioconductor package with parallel version of UniBic biclustering algorithm: one of the most accurate biclustering methods that have been developed so far. For the convenience of usage, we have wrapped the algorithm in an R package called runibic. The package includes: (1) a couple of times faster parallel version of the original sequential algorithm,(2) muchmore efficient memory management, (3) modularity which allows to build new methods on top of the provided one, (4) integration with the modern Bioconductor packages such as SummarizedExperiment, ExpressionSetand biclust.\n\nAvailabilityThe package is implemented in R (3.4) and will be available in the new release of Bioconductor (3.6). Currently it could be downloaded from the following URL: http://github.com/athril/runibic/\n\nContactpatryk.orzechowski@gmail.com, jhmoore@upenn.edu\n\nSupplementary informationSupplementary informations are available in vignette of the package.

bioinformatics↗

bioRxiv2017

Mapping Patient Trajectories using Longitudinal Extraction and Deep Learning in the MIMIC-III Critical Care Database

Electronic Health Records (EHRs) contain a wealth of patient data useful to biomedical researchers. At present, both the extraction of data and methods for analyses are frequently designed to work with a single snapshot of a patients record. Health care providers often perform and record actions in small batches over time. By extracting these care events, a sequence can be formed providing a trajectory for a patients interactions with the health care system. These care events also offer a basic heuristic for the level of attention a patient receives from health care providers. We show that is possible to learn meaningful embeddings from these care events using two deep learning techniques, unsupervised autoencoders and long short-term memory networks. We compare these methods to traditional machine learning methods which require a point in time snapshot to be extracted from an EHR.

bioinformatics↗