About: KernelBased Analysis Of Biological Sequences Changes:

About: revised version of BACOM Changes:Initial Announcement on mloss.org.

About: The SHOGUN machine learning toolbox's focus is on large scale learning methods with focus on Support Vector Machines (SVM), providing interfaces to python, octave, matlab, r and the command line. Changes:This is mostly a bugfix release: Features
Bugfixes

About: Estimates statistical significance of association between variables and their principal components (PCs). Changes:Initial Announcement on mloss.org.

About: hapFabia is an R package for identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data. It detects 100 times smaller segments than previous methods. Changes:o citation update o plot function improved

About: FABIA is a biclustering algorithm that clusters rows and columns of a matrix simultaneously. Consequently, members of a row cluster are similar to each other on a subset of columns and, analogously, members of a column cluster are similar to each other on a subset of rows. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. Applications include detection of transcriptional modules in gene expression data and identification of haplotypes/>identity by descent< consisting of rare variants obtained by next generation sequencing. Changes:CHANGES IN VERSION 2.8.0NEW FEATURES
CHANGES IN VERSION 2.4.0
CHANGES IN VERSION 2.3.1NEW FEATURES
2.0.0:
1.4.0:

About: A Java framework for statistical analysis and classification of biological sequences Changes:New classes:
New features and improvements:
Restructuring:
Several minor new features, bug fixes, and code cleanups

About: MATLAB toolbox for advanced BrainComputer Interface (BCI) research. Changes:Initial Announcement on mloss.org.

About: Tools for functional network analysis. Changes:Initial Announcement on mloss.org.

About: Epistatic miniarray profiles (EMAPs) are a highthroughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from EMAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values  up to 35%  that can reduce the effectiveness of some data analysis techniques and prevent the use of others. This project contains nearest neighbor based tools for the imputation and prediction of these missing values. The code is implemented in Python and uses a nearest neighbor based approach. Two variants are used  a simple weighted nearest neighbors, and a local least squares based regression. Changes:Initial Announcement on mloss.org.

About: Implementation of LSTM for biological sequence analysis (classification, regression, motif discovery, remote homology detection). Additionally a LSTM as logistic regression with spectrum kernel is included. Changes:Spectrum LSTM package included

About: Accurate splice site predictor for a variety of genomes. Changes:Asp now supports three formats: g fname for gff format s fname for spf format b dir for a binary format compatible with mGene. And a new switch t which switches on a sigmoidbased transformation of the svm scores to get scores between 0 and 1.

About: Investigation of dependencies between multiple data sources allows the discovery of regularities and interactions that are not seen in individual data sets. The demand for such methods is increasing with the availability and size of cooccurring observations in computational biology, open data initiatives, and in other domains. We provide practical, open access implementations of generalpurpose algorithms that help to realize the full potential of these information sources. Changes:Three independent modules (drCCA, pint, MultiWayCCA) have been added.

About: BACKGROUND:Over the last decade several prediction methods have been developed for determining the structural and functional properties of individual protein residues using sequence and sequencederived information. Most of these methods are based on support vector machines as they provide accurate and generalizable prediction models. RESULTS:We present a general purpose protein residue annotation toolkit (svmPRAT) to allow biologists to formulate residuewise prediction problems. svmPRAT formulates the annotation problem as a classification or regression problem using support vector machines. One of the key features of svmPRAT is its ease of use in incorporating any userprovided information in the form of feature matrices. For every residue svmPRAT captures local information around the reside to create fixed length feature vectors. svmPRAT implements accurate and fast kernel functions, and also introduces a flexible windowbased encoding scheme that accurately captures signals and pattern for training eective predictive models. CONCLUSIONS:In this work we evaluate svmPRAT on several classification and regression problems including disorder prediction, residuewise contact order estimation, DNAbinding site prediction, and local structure alphabet prediction. svmPRAT has also been used for the development of stateoftheart transmembrane helix prediction method called TOPTMH, and secondary structure prediction method called YASSPP. This toolkit developed provides practitioners an efficient and easytouse tool for a wide variety of annotation problems. Availability: http://www.cs.gmu.edu/~mlbio/svmprat/ Changes:Initial Announcement on mloss.org.

About: SeqAn is an open source C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data. Changes:

About: The Easysvm package provides a set of tools based on the Shogun toolbox allowing to train and test SVMs in a simple way. Changes:Fixes for shogun 0.7.3.

About: ARTS is an accurate predictor for Transcription Start Sites (TSS). Changes:Initial Announcement on mloss.org.

About: *binsdfc* is a command line implementation of the algorithm described in [Endres,Oram,Schindelin,Foldiak:*Bayesian binning beats approximate alternatives: estimating peristimulus time histograms*, [...] Changes:Changed build system from automake to cmake. Moved download page to www.compsens.unituebingen.de
