"Long Short-Term Memory" (LSTM) a fast model-based recurrent neural network for sequence analysis e.g. classification, regression, motif detection, protein homology detection. LSTM automatically extracts indicative patterns for the positive class but in contrast to profile methods it also extracts negative patterns and uses correlations between all detected patterns for classification. LSTM is capable to automatically extract useful local and global sequence statistics. LSTM is complementary to alignment based approaches as it does not use predefined similarity measures like BLOSUM or PAM matrices.
Also included is a LSTM which makes logistic regression with the spectrum kernel. Indicative patterns are now k-mers.
LSTM is multi threaded and therefore makes full use of any multi core/processor system. Based on a user given number of threads LSTM trains this number of sequences in parallel. Every thread chooses the next untrained sequence automatically during each training epoch.
- Changes to previous version:
Spectrum LSTM package included
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- URL: Project Homepage
- Supported Operating Systems: Platform Independent
- Data Formats: Fasta
- Tags: Bioinformatics, Sequence Analysis, Classification, Regression, Parallel, Recurrent Neural Networks, Motif Discovery, Genetic, Proteins, Remote Homology Detection, Multi Core, Multi Threaded
- Archive: download here
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