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CUDA-JMI: Acceleration of feature selection on heterogeneous systems
(Elsevier, 2020-01)
[Abstract]: Feature selection is a crucial step nowadays in machine learning and data analytics to remove irrelevant and redundant characteristics and thus to provide fast and reliable analyses. Many research works have ...
SMusket: Spark-based DNA error correction on distributed-memory systems
(Elsevier B.V., 2020)
[Abstract]: Next-Generation Sequencing (NGS) technologies have revolutionized genomics research over the last decade, bringing new opportunities for scientists to perform groundbreaking biological studies. Error correction ...
Fiuncho: a program for any-order epistasis detection in CPU clusters
(Springer, 2022)
[Abstract]: Epistasis can be defined as the statistical interaction of genes during the expression of a phenotype. It is believed that it plays a fundamental role in gene expression, as individual genetic variants have ...
ParRADMeth: Identification of Differentially Methylated Regions on Multicore Clusters
(IEEE, 2023)
[Abstract]: The discovery of Differentially Methylated (DM) regions is an important research field in biology, as it can help to anticipate the risk of suffering from specific diseases. Nevertheless, the high computational ...
bioScience: A new python science library for high-performance computing bioinformatics analytics
(Elsevier Ltd, 2024)
[Abstract]: BioScience is an advanced Python library designed to satisfy the growing data analysis needs in the field of bioinformatics by leveraging High-Performance Computing (HPC). This library encompasses a vast multitude ...
PARamrfinder: detecting allele-specific DNA methylation on multicore clusters
(Springer, 2024-01)
[Abstract]: The discovery of Allele-Specific Methylation (ASM) is an important research field in biology as it regulates genomic imprinting, which has been identified as the cause of some genetic diseases. Nevertheless, ...
CUDA acceleration of MI-based feature selection methods
(Elsevier, 2024-08)
[Abstract]: Feature selection algorithms are necessary nowadays for machine learning as they are capable of removing irrelevant and redundant information to reduce the dimensionality of the data and improve the quality of ...
BigDEC: A multi-algorithm Big Data tool based on the k-mer spectrum method for scalable short-read error correction
(Elsevier, 2024-05)
[Abstract]: Despite the significant improvements in both throughput and cost provided by modern Next-Generation Sequencing (NGS) platforms, sequencing errors in NGS datasets can still degrade the quality of downstream ...