Search Results for "data mining algorithms in c#.net"

The ChIP-Seq software provides methods for the analysis of ChIP-seq data and other types of mass genome annotation data. The most common analysis tasks include positional correlation analysis, peak detection, and genome partitioning into signal-rich and signal-depleted regions.

Clone-by-clone sequencing, as a means of achieving high quality assemblies for large and complex genomes, continues to be of great relevance in the era of high throughput sequencing. However, assemblies obtained using current whole genome assemblers are often fragmented and sometimes have issues of genome completeness owing to different data characteristics introduced by multiplexed sequencing. With iCAS the data filtering process is based on a novel kmer frequency algorithm, resulting...

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).