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software [2019/06/24 15:53]
Jaroslaw Zola |
software [2019/08/20 12:24]
Jaroslaw Zola |
| * [[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. | * [[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. |
| | * [[https://gitlab.com/SCoRe-Group/IsomapSpark/|somapSpark]] 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. |
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