Matlab Code by Mark Schmidt (optimization, graphical models, machine learning) Summary This package contains the most recent version of various Matlab codes I released during my PhD work.
I would recommend downloading and using this package if you plan on using more than one of my Matlab codes. This is because this package includes all the more recent bug-fixes and efficiency-improvements, while in making this package I have updated my old code to make it compatible with the new code and newer versions of Matlab. 11F: Homework 03. Here we will work with the Spambase dataset from HW02 , testing your implementations using Fold 1 as described in HW02.
Precondition your data. Gradient descent will often perform much better on data that has been "normalized" so that the individual features are on a comparable scale. One commonly used normalization is the z-score , sometimes called the standard score. To compute the z-score corresonding to a feature value, one must first compute the mean and standard deviation of the feature.
You should compute these values yourself, in code, but you can check your results against the Spambase page describing various simple statistics over those features. A question and answer site for bioinformatics. Faster Illumina analysis pipeline via streaming. Hello BioStar, After some time working with Illumina and pipelines, I've identified a bottleneck when getting early "draft" results from a run.
Figuring out how the sequencing data looks like in real time, as opposed to wait for the run to finish after ~11 days. The goal would be to get an estimate of how many reads one could expect to get for each sample, thereby guiding setup for a subsequent run for topping up the data for those samples that do not reach the required amounts.
It would be help a lot if we could reduce the the wall-clock time for reaching a decision on which samples need to be re-run. When it comes to implementation, I've been thinking on a file status daemon such as Guard, coupled with the CASAVA/OLB tools from illumina, performing basecalling and demultiplexing as soon as the files get written to disk, without having to wait for the whole run to finish. Cheers & happy new year Bio* !    Ten Simple Rules for Getting Help from Online Scientific Communities. Citation: Dall'Olio GM, Marino J, Schubert M, Keys KL, Stefan MI, et al. (2011) Ten Simple Rules for Getting Help from Online Scientific Communities.
PLoS Comput Biol 7(9): e1002202. doi:10.1371/journal.pcbi.1002202 Editor: Philip E. Bourne, University of California San Diego, United States of America Published: September 29, 2011 Copyright: © 2011 Dall'Olio et al. Funding: GMD is supported by grants SAF-2007-63171 and BFU2010-19443 (subprogram BMC) awarded by Ministerio de Educación y Ciencia (Spain), the Direcció General de Recerca, Generalitat de Catalunya (Grup de Recerca Consolidat 2009 SGR 1101) to JB. Competing interests: The authors have declared that no competing interests exist. Introduction The increasing complexity of research requires scientists to work at the intersection of multiple fields and to face problems for which their formal education has not prepared them. Nevertheless, making proper use of these resources is not easy. Health focused genetic testing and analysis; DNA test - Navigenics. STRING: functional protein association networks.
Gregor Gorjanc (gg) 03. GNA : Genetic Network Analyzer. Genetic Network Analyzer : Modelling and simulation of genetic regulatory networks Genetic Network Analyzer (GNA) is a computer tool for the modeling and simulation of genetic regulatory networks.
The aim of GNA is to assist biologists and bioinformaticians in constructing a model of a genetic regulatory network using knowledge about regulatory interactions in combination with gene expression data. Genetic Network Analyzer consists of a simulator of qualitative models of genetic regulatory networks in the form of piecewise-linear differential equations.
Instead of exact numerical values for the parameters, which are often not available for networks of biological interest, the user of GNA specifies inequality constraints. This information is sufficient to generate a state transition graph that describes the qualitative dynamics of the network. Functionalities of GNA The current version is GNA 8.5. The export of models to the SBML Qual format. The export and import of models in SBML format ; European Bioinformatics Institute.