Open Source Linux Bio-Informatics Software

The AlphaGenome API provides access to AlphaGenome, Google DeepMind’s unifying model for deciphering the regulatory code within DNA sequences. This repository contains client-side code, examples, and documentation to help you use the AlphaGenome API. AlphaGenome offers multimodal predictions, encompassing diverse functional outputs such as gene expression, splicing patterns, chromatin features, and contact maps. The model analyzes DNA sequences of up to 1 million base pairs in length and can deliver predictions at single-base-pair resolution for most outputs. AlphaGenome achieves state-of-the-art performance across a range of genomic prediction benchmarks, including numerous diverse variant effect prediction tasks.

Lush is a Lisp dialect with extensions for object-oriented and array-oriented programming. Lush is intended for prototyping numerically intensive applications and is designed for easy integration of existing C/C++/Fortran codes.

BioNeMo is an AI-powered framework developed by NVIDIA for protein and molecular generation using deep learning models. It provides researchers and developers with tools to design, analyze, and optimize biological molecules, aiding in drug discovery and synthetic biology applications.

Febrl (Freely Extensible Biomedical Record Linkage) does data standardisation (segmentation and cleaning) and probabilistic record linkage ("fuzzy" matching) of one or more files or data sources which do not share a unique record key or identifier.

Critterding is a "Petri dish" universe in 3D that demonstrates evolving artificial life. Critters start out with completely random brains and bodies, but will automatically start evolving into something with much better survival skills.

This ohnlp project has released "pipelines" that were contributed by members of the OHNLP Consortium. The pipelines are based on the Apache UIMA framework. medKAT/P, MedCoref, MedTagger, MedXN, and cTAKES are licensed under Apache License V2.0. MedTime is licensed under GNU General Public License version 3.0 (GPLv3). cTAKES development has moved to apache.org. See http://ctakes.apache.org/

JGAP is a Genetic Algorithms and Genetic Programming package written in Java. It is designed to require minimum effort to use, but is also designed to be highly modular. JGAP features grid functionality and a lot of examples. Many unit tests included. Legal notice/Impressum: Klaus Meffert An der Struth 25 D-65510 Idstein sourceforge <at> klausmeffert.de

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, ...

Source code from the Research Institute for Signals, Systems and Computational Intelligence http://fich.unl.edu.ar/sinc

An implementation of the Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method for MATLAB and R. The supplied functionality includes e.g. cross-validation, kernel parameter optimization, model diagnostics and plot tools.

The source code has been migrated and is now hosted on Github: https://github.com/jenetics/jenetics Jenetics is an advanced Genetic Algorithm, Evolutionary Algorithm and Genetic Programming library, respectively, written in modern day Java. It is designed with a clear separation of the several algorithm concepts, e. g. Gene, Chromosome, Genotype, Phenotype, Population and fitness Function. Jenetics allows you to minimize or maximize the given fitness function without tweaking it. In contrast to other GA implementations, the library uses the concept of an evolution stream (EvolutionStream) for executing the evolution steps. Since the EvolutionStream implements the Java Stream interface, it works smoothly with the rest of the Java Stream API. Other repositories: - https://github.com/jenetics/jenetics Maven: - https://mvnrepository.com/artifact/io.jenetics

formalisation of scientific experiments

This project is a quest for conscious artificial intelligence. A number of prototypes will be developed as the project progresses. This project has 2 subprojects: Object Pascal based CAI NEURAL API - https://github.com/joaopauloschuler/neural-api Python based K-CAI NEURAL API - https://github.com/joaopauloschuler/k-neural-api A video from the first prototype has been made: http://www.youtube.com/watch?v=qH-IQgYy9zg Above video shows a popperian agent collecting mining ore from 3 mining sites and bringing to the base. At the time the agent is born, it doesn't know how to walk nor it knows that it feels pleasure by mining. He has tact only (blind agent). The video shows learning, planning, executing and plan optimization.

A Python interface to the Wordnet database of word meanings and lexical relationships. allows the user to type expressions such as N['dog'], hyponyms(N['dog'][0]), and closure(ADJ['red'], SYNONYM) to query the database for lexical relationships.

A minimalist approach to share text documents and data annotations. Allows a large number of different annotations to be represented. Project files contain: - simple code to hold/read/write data and perform sample processing. - BioC-formatted corpora - BioC tools that work with BioC corpora BioC goals - simplicity - interoperability - broad use - reuse There should be little investment required to learn to use a format or a software module to process that format. We are interested in reuse, and we focus on common NLP tasks that are broadly useful for textmining.

Darwin 2: Java Framework for Evolutionary Computation (genetic algorithm, GA). A true framework with out-of-the-box functionality and extensibility of all classes. Interface-based pattern with dependency-injection to configure components.

The General Hidden Markov Model Library (GHMM) is a C library with additional Python bindings implementing a wide range of types of Hidden Markov Models and algorithms: discrete, continous emissions, basic training, HMM clustering, HMM mixtures.

The objective of this project is to make available an open-source version of our Multifactor Dimensionality Reduction (MDR) software. MDR is a nonparametric alternative to logistic regression for detecting and characterizing nonlinear interactions.

CILib is a framework for developing Computational Intelligence software in swarm intelligence, evolutionary computing, neural networks, artificial immune systems, fuzzy logic and robotics.

MASyV (Multi-Agent System Visualization) enables one to write agent-based models/cellular automata, eg. in C, visualize them in real time & capture to movie file with MASyVs GUI & message passing lib. Includes examples: Hello World, ants, viral infection

This is a Matlab software package for single molecule FRET data analysis.

SwisTrack 4 is a powerful software for tracking robots, humans, animals and objects using a camera or a recorded video as input source. It contains interfaces for USB, FireWire and GigE cameras, as well as AVI files.

Arena is a computer simulation in which programs compete for CPU time and access to main memory. Processes such as the dynamics of punctuated equilibrium, host-parasite co-evolution and density dependent natural selection are amenable to investigation.

From Amat et al., Nature Methods, 2014*: "The comprehensive reconstruction of cell lineages in complex multicellular organisms is a central goal of developmental biology. We present an open-source computational framework for segmentation and tracking of cell nuclei with high accuracy and speed. We demonstrate its (1) generality, by reconstructing cell lineages in four-dimensional, terabyte-sized image data of fruit-fly, zebrafish and mouse embryos, acquired with three different types of fluorescence microscopes, (2) scalability, by analyzing advanced stages of development with up to 20,000 cells per time point, at 26,000 cells min-1 on a single computer workstation, and (3) ease of use, by adjusting only two parameters across all data sets and providing visualization and editing tools for efficient data curation. Our approach achieves on average 97.0% linkage accuracy across all species and imaging modalities." *Please cite this paper if you use this code for your research

A base for programs. Includes algorythms for Q-learning and SOM's etc. too. Examples: Hamron: Simulates evolution, uses the 2D-renderer. DriveUnit: created for school, for a robotic arm, uses the 3D-renderer. Hlearn: http://www.sagenb.org/home/pub/8