Search results for "computer.software_genre"
showing 10 items of 3858 documents
Recentrifuge: Robust comparative analysis and contamination removal for metagenomics
2017
Metagenomic sequencing is becoming widespread in biomedical and environmental research, and the pace is increasing even more thanks to nanopore sequencing. With a rising number of samples and data per sample, the challenge of efficiently comparing results within a specimen and between specimens arises. Reagents, laboratory, and host related contaminants complicate such analysis. Contamination is particularly critical in low microbial biomass body sites and environments, where it can comprise most of a sample if not all. Recentrifuge implements a robust method for the removal of negative-control and crossover taxa from the rest of samples. With Recentrifuge, researchers can analyze results f…
iSEE: Interactive SummarizedExperiment Explorer
2018
Data exploration is critical to the comprehension of large biological data sets generated by high-throughput assays such as sequencing. However, most existing tools for interactive visualisation are limited to specific assays or analyses. Here, we present the iSEE (Interactive SummarizedExperiment Explorer) software package, which provides a general visual interface for exploring data in a SummarizedExperiment object. iSEE is directly compatible with many existing R/Bioconductor packages for analysing high-throughput biological data, and provides useful features such as simultaneous examination of (meta)data and analysis results, dynamic linking between plots and code tracking for reproduci…
EvalMSA: A Program to Evaluate Multiple Sequence Alignments and Detect Outliers
2016
8 páginas, 3 figuras, 2 tablas.
Exceptional Pattern Discovery
2017
This chapter is devoted to a discussion on exceptional pattern discovery, namely on scenarios, contexts, and techniques concerning the mining of patterns which are so rare or so frequent to be considered as exceptional and, then, of interest for an expert to shed lights on the domain. Frequent patterns have found broad applications in areas like association rule mining, indexing, and clustering [1, 20, 23]. The application of frequent patterns in classification also achieved some success in the classification of relational data [6, 13, 14, 19, 25], text [15], and graphs [7]. The part is organized as follows. First, the frequent pattern mining on classical datasets is presented. This is not …
Deep learning in next-generation sequencing
2020
Highlights • Machine learning increasingly important for NGS. • Deep learning can improve many NGS applications.
Lack of association between screening interval and cancer stage in Lynch syndrome may be accounted for by over-diagnosis; a prospective Lynch syndrom…
2019
Background Recent epidemiological evidence shows that colorectal cancer (CRC) continues to occur in carriers of pathogenic mismatch repair (path_MMR) variants despite frequent colonoscopy surveillance in expert centres. This observation conflicts with the paradigm that removal of all visible polyps should prevent the vast majority of CRC in path_MMR carriers, provided the screening interval is sufficiently short and colonoscopic practice is optimal. Methods To inform the debate, we examined, in the Prospective Lynch Syndrome Database (PLSD), whether the time since last colonoscopy was associated with the pathological stage at which CRC was diagnosed during prospective surveillance. Path_MMR…
A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning
2019
Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we …
Uptake of hysterectomy and bilateral salpingo-oophorectomy in carriers of pathogenic mismatch repair variants:a Prospective Lynch Syndrome Database r…
2021
Purpose: This study aimed to report the uptake of hysterectomy and/or bilateral salpingo-oophorectomy (BSO) to prevent gynaecological cancers (risk-reducing surgery [RRS]) in carriers of pathogenic MMR (path_MMR) variants.Methods: The Prospective Lynch Syndrome Database (PLSD) was used to investigate RRS by a cross-sectional study in 2292 female path_MMR carriers aged 30-69 years.Results: Overall, 144, 79, and 517 carriers underwent risk-reducing hysterectomy, BSO, or both combined, respectively. Two-thirds of procedures before 50 years of age were combined hysterectomy and BSO, and 81% of all procedures included BSO. Risk-reducing hysterectomy was performed before age 50 years in 28%, 25%,…
Discriminating graph pattern mining from gene expression data
2016
We consider the problem of mining gene expression data in order to single out interesting features that characterize healthy/unhealthy samples of an input dataset. We present and approach based on a network model of the input gene expression data, where there is a labelled graph for each sample. To the best of our knowledge, this is the first attempt to build a different graph for each sample and, then, to have a database of graphs for representing a sample set. Out main goal is that of singling out interesting differences between healthy and unhealthy samples, through the extraction of "discriminating patterns" among graphs belonging to the two different sample sets. Differently from the …
Application of Graph Clustering and Visualisation Methods to Analysis of Biomolecular Data
2018
In this paper we present an approach based on integrated use of graph clustering and visualisation methods for semi-supervised discovery of biologically significant features in biomolecular data sets. We describe several clustering algorithms that have been custom designed for analysis of biomolecular data and feature an iterated two step approach involving initial computation of thresholds and other parameters used in clustering algorithms, which is followed by identification of connected graph components, and, if needed, by adjustment of clustering parameters for processing of individual subgraphs.