Search Results for "training"

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.

...According to the repository, it uses the StripedHyena 2 architecture, was pretrained with Savanna, and was trained autoregressively on the OpenGenome2 dataset containing 8.8 trillion tokens. The codebase is focused on local inference and generation through the Vortex inference stack rather than serving as a full training framework alone, although it also points users to training and fine-tuning resources. It supports multiple ways of working with the model, including forward passes, embeddings, generation workflows, notebooks, hosted APIs, and self-hosted deployment through NVIDIA NIM.

The RDP Classifier is a naive Bayesian classifier that can rapidly and accurately provides taxonomic assignments for bacterial and archaeal 16S rRNA sequences, fungal LSU and fungal ITS sequences, with confidence estimates for each assignment. More information and tutorials on how to install, use and retrain RDP Clasifier can be found on at https://github.com/rdpstaff/classifier and John Quensen's blog (https://john-quensen.com/). Citation: 1. Wang Q, Garrity GM, Tiedje JM, Cole JR....

The Collaborative Computational Project for NMR (CCPN) provides tools and knowledge to maximise the impact of the biological NMR studies. The CCPN software facilitates data analysis and software integration. The project actively promotes the exchange of knowledge and provides training and best practices for the NMR community. CCPN also has a leading role in the development of a NMR data-sharing standard and coordination of NMR instrumentation proposals for RCUK and BIS. The 28 partners of CCPN jointly cover all aspects of biomolecular NMR and together they promote excellence in science in their respective fields.

Gene Prediction Pipeline for Plant Genomes using Self-Training Gene Models and Transcriptomic Data

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.

...FASTQSim is sequencing platform-independent, and computes distributions of read length, quality scores, indel rates, single point mutation rates, indel size, and similar statistics for any sequencing platform. To create training or testing datasets, FASTQSim has the ability to convert target sequences into in silico reads with matching error profiles. FASTQsim allows the user to simulate individual read datasets that can be used as standardized test scenarios for planning sequencing projects or for benchmarking metagenomic software. In this regard, in silico datasets generated with the FASTQsim tool hold several advantages over natural datasets: they are sequencing platform independent, extremely well characterized, and less expensive to generate.

Homepage: www.cellnopt.org Based on prior knowledge networks CellNOptR can derive functional information about the network using logic modeling. CytoCopteR is a friendly way to use CellNOptR taking advantage of the network visualization and analysis of Cytoscape and without requiring knowledge on R.

The Self-Organizing Map Maker for Education and Research. Features include 3D-visualization of the training process, various 2D and 3D map topologies, easy extensibility to additional topologies.

GLEAN is an unsupervised learning system to integrate disparate sources of gene structure evidence (gene model predictions, EST/protein genomic sequence alignments, SAGE/peptide tags, etc) to produce a consensus gene prediction, without prior training.

GRANULE is a flashcards program based on Leitner cardfile methodology for learning new words. It features long-term memory training capabilities with scheduling, integrated pictures, sound, and full-screen mode.

Foad is an open source software which receive an EKG Signal from scanner, WFDB database or heart sensors. Finding patient disease started by taking Fourier transform (FFT) from input signal and extract a single cycle. Based on some heuristic algorithm the most important feature like P , Q , R , S , T captured and feed to trained neural network. and so the final decision made by CNN library. As mentioned before this software also capable do some image processing on scanned paper to lower...

D-finder is a bioinformatic search algorithm for the identification of D-sites in JNK interacting proteins. The algorithm is a combination of pattern matching and a hidden markov model (HMM) based on a training set of known JNK D-sites.

Moara is a biological text mining tool and consists of a Java library and some auxiliary MySQL databases for gene/protein training and extraction of mentions and its further normalization and disambiguation.

BCAR is a library for the associative classification, which denotes "Boosting Class Association Rules". BCAR provides a general tool for classification tasks with various types of input data.

The Stem Cell Artificial Neural network project entails the analysis and integration of genomics data for extracting the stemness signature of several tissues by training a multiclass single-layer linear artificial neural network.

Tools to build molecular-docking activity prediction models by PLS regression with iterative training and pose-selection. Descriptors include (i) docking score(s), (ii) pharmacophore features, (iii) multi-feature descriptors learned by decision trees.

FORce based Cluster Editing (FORCE) is a Java software heuristically solving the graph cluster editing problem on weighted edges using BLAST E-values. It further provides a training mode for heuristic parameter estimation.

MLP(Multilayer Perceptron) training library written in LISP like language called Scheme. Can work with any type of MLP.

NeuroBox is an .NET OOP Library to generate, propagate and train complex neuronal networks with technologies like backpropagation with weight decay, momentum term, manhattan training, flatspot elimination etc.

Multilayered feed-forward neural network software written in C++. Backpropagation and RPROP are available as training algorithms. Design goals: speed of execution when calculating the output to new data, and quality of training (preprocessing: PCA).

Yawn: Yet Another W* Network: a java framework for developing, training and testing neural networks independently of the model and test environment.