Access Google's best plus Claude, Llama, and Gemma. Fine-tune and deploy from one console.
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MongoDB Atlas runs apps anywhere
Deploy in 115+ regions with the modern database for every enterprise.
MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
Builds synthetic data applicable across multiple domains. This package also provides flexibility to control data distribution to make it relevant to many industry examples.
This application allow user to predict dissolution profile of solid dispersion systems based on algorithms like symbolic regression, deep neural networks, random forests or generalized boosted models. Those techniques can be combined to create expert system.
Application was created as a part of project K/DSC/004290 subsidy for young researchers from Polish Ministry of Higher Education.
Suite of community detection algorithms based on Modularity
- MixtureModel_v1r1: overlapping community algorithm [3], which includes novel partition density and fuzzy modularity metrics.
- OpenMP versions of algorithms in [1] are available to download.
- Main suite containing three community detection algorithms based on the Modularity measure containing: Geodesic and Random Walk edge Betweenness [1] and Spectral Modularity [2].
Collaborator: Theologos Kotsos.
[1] M. Newman & M. Girvan, Physical Review, E 69 (026113), 2004.
[2] M....
Supervised Ranking of Contigs in de novo Assemblies
SuRankCo is a machinelearning based software to score and rank contigs from de novo assemblies of next generation sequencing data. It trains with alignments of contigs with known reference genomes and predicts scores and ranking for contigs which have no related reference genome yet.
For more details about SuRankCo and its functioning, please see
"SuRankCo: Supervised Ranking of Contigs in de novo Assemblies"
Mathias Kuhring, Piotr Wojtek Dabrowski, Andreas Nitsche and Bernhard Y. ...
A machinelearning system for supervised document classification
An open source system for supervised document classification based on statistical machinelearning techniques.
On the contrary of the state of art classification techniques, MyNook just requires the title of the document, not the content itself.
Platform for parallel computation in the Amazon cloud, including machinelearning ensembles written in R for computational biology and other areas of scientific research. Home to MR-Tandem, a hadoop-enabled fork of X!Tandem peptide search engine.
The TreeRank project is a R package implementing a MachineLearning algorithm to build tree-based ranking rules from data with binary labels, based on ROC optimization.
Structlab is a machinelearning C++ framework for structured domains, which provides a toolbox of learning methods and tools for preprocessing and visualization. It also provides a GUI to setup elaborate experiments in a visual and intuitive way.
Open, extensible web-based collaborative platform for microarray gene expression, sequence and PPI data analysis, exposing distinct chainable components for clustering, pattern discovery, statistics (thru R), machine-learning algorithms and visualization