Search Results for "python feature selection"

MutaNET comes with a next generation sequencing (NGS) pipeline that calls mutations based on paired-end NGS reads, an automated mutation analysis tool and various file converters and mergers. The mutation analysis feature considers the coding region, protein domains, regulation and transcription factor binding site information, and can be used to analyse the potential impact of mutations on antibiotic resistance.

RELEASE NOTE: Get raxmlGUI 2.0 at the NEW PROJECT LOCATION: https://antonellilab.github.io/raxmlGUI/ raxmlGUI is a graphical user interface to RAxML, one of the most popular and widely used software for phylogenetic inference using maximum likelihood. A userfriendly graphical front-end for phylogenetic analyses using RAxML (Stamatakis, 2006). Please cite: Silvestro, Michalak (2012) - raxmlGUI: a graphical front-end for RAxML. Organisms Diversity and Evolution 12, 335-337. DOI:...

ABC-SysBio implements likelihood free parameter inference and model selection in dynamical systems. It is designed to work with both stochastic and deterministic models written in Systems Biology Markup Language (SBML).

This project contains Python scripts usable for a dual transcript-discovery approach that improves the delimitation of gene features from RNA-seq data in the chicken model. Documentation: http://bio.biologists.org/content/biolopen/suppl/2018/01/17/bio.028498.DC1/BIO028498supp.pdf Citation: Orgeur M., Martens M., Börno S. T., Timmermann B., Duprez D. and Stricker S. (2018). A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the...

NESSY is an open source software to analyse NMR relaxation dispersion data of either CPMG or R1p (R1rho) dispersion experiments. The graphical interface enables simple management of large experimental data sets and simple and automated analysis. NESSY automatically calculates effective transverse relaxation rate (R2eff) and performs model selection between different relaxation dispersion models.

diCal-IBD can be used for predicting identical by descent (IBD) tracts in sequence data. It provides means for calculating the accuracy of the prediction, if the true tracts are available, plotting of the predicted tracts, their TMRCA (time to the most recent common ancestor) and corresponding posterior probabilities, and identification of putative recent positive selection through investigation of average IBD sharing

Java Machine Learning Library is a library of machine learning algorithms and related datasets. Machine learning techniques include: clustering, classification, feature selection, regression, data pre-processing, ensemble learning, voting, ...

The SeqSelector toolset is a suite of user-friendly, platform independent python scripts to facilitate selection of sequences for targeted enrichment of next-generation libraries through hybridization-based sequence capture. The scripts require no knowledge of programming, and can be applied to genome sequences of model or non-model species. We suggest a workflow in which genes of interest are first identified from previous studies and publicly available datasets of functional gene annotation. ...

This is an automatic spike detection program which takes account of probe geometry and produces a .mask file to be used with the new masked version of KlustaKwik. We recommend you use Python 2.6 or 2.7, e.g. a free academic version can be obtained from Entthought Python. The input files for SpiKeDeteKt are: .dat (raw data file) .probe (probe file, described below - user constructed) parameters.py (optional - otherwise it uses defaultparameters.py) SpiKeDeteKt outputs the following files: .fet.n (feature file) .mask.n (needed for using the new (masked) KlustaKwik) .clu.n (a trivial clue file where everything is put into a single cluster) .fmask.n (trial - float masks instead of binary, we are using this for testing masked KlustaKwik) .spk.n (spike file) .upsk.n (unfiltered spike waveform) .res.n (list of spike times) .xml (an xml file with all the parameters that can subsequently be used by neuroscope or klusters) .fil (highpass filtered data) .h5 (

Protein ALignment Optimiser (PALO) is a script for the selection and alignment of the best combination of transcripts among orthologous genes. PALO is mainly written in Python, although other programming languages are also implemented (R, Perl...).

DimReduction project provide an open-source multiplatform (Java) graphical environment for bioinformatics problems that supports many feature selection algorithms, pattern recognition techniques, criterion functions and graphic visualization tools.

A collection of tools for use in gene naming, including standardization, selection, and comparison.

Java GUI to analyze one (or more, in batch mode) datasets with several feature selection methods at the same time and also performing an "ensemble" analysis. It can be easily extended to include any feature selection algorithm.

A python library for handling and analyzing biological sequence annotations as described in GFF files (General Feature Format). The library is intended to be a complete framework for this file format.