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co-ocurrence_analysis

Co-ocurrence analysis algorithm

denovotesdmel

Scripts for genome assembly and TE annotation in D. melanogaster genomes

g-saip

Graphical sequence alignment though High Performance Computing in the post-genomic era

inpactor

Inpactor: Integrated and Parallel Analyzer and Classifier of LTR Retrotransposons for Large Genomes

inpactor2

Inpactor2: LTR retrotransposon detector and classificator using Deep Learning

machinelearningintes

Application of Machine Learning Approaches to detect and classify TEs

ml_dl_microarrays

Here, we describe the comparison of the most used algorithms in classical ML and DL to classify carcinogenic tumors described on 11_tumor data base, obtaining accuracies between 76.97% and 100% for tumor identification. Our results bring up a more efficient an accurate classification method based on gene expression (microarray data) and ML/DL algorithms, which facilitates the prediction of the tumor type from a multiple cancer types scenario

mlintes

Application of Machine Learning to Annotate Transposable Elements in Plants

ncbi-blast_sobre_xeon_phi

paralleldotplot

ParaGrAl: Parallel graphic alignment of sequences. Addressing the comparison of sequences in the post-genomic era by high perfomance computing.

senmap

SENMAP: A Convolutional Neural Network Architecture for Curation of LTR-RT Libraries from Plant Genomes

te-aid

Annotation helper tool for the manual curation of transposable element consensus sequences

tip_finder

TIP_finder: Transposable element Insertion Polymorphism Finder for huge genomic datasets

transposondltoolkit

yoro

Genomic Object Detection: An Improved Approach for TE Detection and Classification using CNN