Articles by Yasset Perez-Riverol

Adding CITATION to your R package

August 27, 2014 | 0 Comments

Original post from Robin's Blog:Software is very important in science – but good software takes time and effort that could be used to do other work instead. I believe that it is important to do this work – but to make it worthwhile, people need to get credit for their work, ... [Read more...]

Making Your Code Citable

August 26, 2014 | 0 Comments

Original post from GitHub Guides:Digital Object Identifiers (DOI) are the backbone of the academic reference and metrics system. If you’re a researcher writing software, this guide will show you how to make the work you share on GitHub citable by archiving one of your GitHub repositories and assigning ...
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One step ahead in Bioinformatics using Package Repositories

October 28, 2013 | 0 Comments

About a year ago I published a post about in-house tools in research and how using this type of software may end up undermining the quality of a manuscript and the reproducibility of its results.  While I can certainly relate to someone reluctant to release nasty code (i.e. not ... [Read more...]

Little Book of R for Bioinformatics by Avril Coghlan

October 25, 2013 | 0 Comments

Introduction to bioinformatics, with a focus on genome analysis, using the R statistics software. By Avril Coghlan(Wellcome Trust Sanger Institute, Cambridge,UK). Original Site: [Read more...]

Creating an Open Source Revolution in Computational Proteomics

October 24, 2013 | 0 Comments

First of all, I don’t want to discuss in this post about Open-Source, its strengths & strengths. This post is about the most useful Open-Source packages, frameworks or libraries in the field of computational proteomics (a short version of our manuscript “Open source libraries and frameworks for Mass Spectrometry based ... [Read more...]

Introduction to Feature selection for bioinformaticians using R, correlation matrix filters, PCA & backward selection

October 17, 2013 | 0 Comments

Bioinformatics is becoming more and more a Data Mining field. Every passing day, Genomics and Proteomics yield bucketloads of multivariate data (genes, proteins, DNA, identified peptides, structures), and every one of these biological data units are described by a number of features: length, physicochemical properties, scores, etc. Careful consideration of ...
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