| Both sides previous revision
Previous revision
Next revision
|
Previous revision
|
software [2016/08/19 21:00]
Jaroslaw Zola |
software [2023/09/09 22:38] (current)
Jaroslaw Zola |
| Visit [[https://gitlab.com/groups/SCoRe-Group|our GitLab repository]] to see latest software development activities! | Visit [[https://gitlab.com/groups/SCoRe-Group|our GitLab repository]] to see latest software development activities! |
| \\ | \\ |
| | * [[https://gitlab.com/SCoRe-Group/scool|SCoOL]] A simple programming model designed to facilitate and accelerate the search space exploration phase of the optimization processes. |
| | * [[https://cse.buffalo.edu/~jzola/smarten/|SMARTEn]] Framework for mobile DNA analytics. This software suite aims to provide new ways to analyze metagenomic DNA using fully mobile setups. It is foundation for the [[https://gitlab.com/SCoRe-Group/smarten/-/tree/master/coriolis?ref_type=heads|Coriolis]] metagenomic classifier. |
| | * [[https://gitlab.com/SCoRe-Group/SABNAtk|SABNAtk]] Toolkit for fast counting over categorical data. This toolkit can be used to power any application that involves counting of data configurations, e.g., to estimate likelihoods, evaluate model scoring functions, etc. Currently, SABNAtk is the workhorse supporting SABNA. |
| * [[https://gitlab.com/SCoRe-Group/SABNA-Release|SABNA]] Scalable Accelerated Bayesian Network Analytics. This very actively developed software toolkit provides a set of sequential and parallel tools for structure learning of Bayesian networks. It is designed to provide exact solutions on large-scale data in acceptable time limits. | * [[https://gitlab.com/SCoRe-Group/SABNA-Release|SABNA]] Scalable Accelerated Bayesian Network Analytics. This very actively developed software toolkit provides a set of sequential and parallel tools for structure learning of Bayesian networks. It is designed to provide exact solutions on large-scale data in acceptable time limits. |
| | * [[https://gitlab.com/SCoRe-Group/APSPark|APSPark]] Efficient and scalable All-Pairs Shortest-Path Solver for Apache Spark. On a modest Spark cluster (e.g., 1024 Intel Xeon cores), the solver can handle arbitrary undirected graphs with over 200,000 vertices. This work has been extended by Mohammad Javanmard, Zafar Ahmad and colleagues into [[https://github.com/TEAlab/DPSpark|DPSPark]] to cover a broader spectrum of dynamic programming algorithms. |
| | * [[https://gitlab.com/SCoRe-Group/IsomapSpark/|IsomapSpark]] is a tool to efficiently learn manifolds from large-scale high-dimensional data. The method is based on Isomap spectral dimensionality reduction and is implemented entirely in Apache Spark. |
| * [[http://www.jzola.org/elastic|ELaSTIC]] is a software suite for a rapid identification and clustering of similar sequences from large-scale biological sequence collections. At its core is an efficient MinHash-based strategy to detect similar sequence pairs without aligning all sequences against each other. It is designed to work with data sets consisting of millions of DNA/RNA or amino acid strings, using various alignment criteria. | * [[http://www.jzola.org/elastic|ELaSTIC]] is a software suite for a rapid identification and clustering of similar sequences from large-scale biological sequence collections. At its core is an efficient MinHash-based strategy to detect similar sequence pairs without aligning all sequences against each other. It is designed to work with data sets consisting of millions of DNA/RNA or amino acid strings, using various alignment criteria. |