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UnknownA lossless reference-free sequence compression algorithm leveraging grammatical, statistical, and substitution rules.
Deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) sequence compressors for novel species frequently face challenges when processing wide-scale raw, FASTA, or multi-FASTA structured data. For years, molecular sequence databases have favored the widely used general-purpose Gzip and Zstd compressor...
S-EPMC11735755 | biostudies-literature
UnknownConstructing large-scale benchmark datasets for the hierarchical hub location problem using geospatial information: a case study on populous Indian cities.
This paper introduces an innovative approach for constructing large-scale, realistic benchmark geospatial datasets specifically designed for the single-allocation hierarchical hub median problem, a key variant of the hierarchical hub location problem. Addressing the lack of publicly available large-...
S-EPMC12533260 | biostudies-literature
UnknownA novel biclustering approach to association rule mining for predicting HIV-1-human protein interactions.
Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimen...
S-EPMC3335119 | biostudies-literature
UnknownMulti-class clustering of cancer subtypes through SVM based ensemble of pareto-optimal solutions for gene marker identification.
With the advancement of microarray technology, it is now possible to study the expression profiles of thousands of genes across different experimental conditions or tissue samples simultaneously. Microarray cancer datasets, organized as samples versus genes fashion, are being used for classification...
S-EPMC2980474 | biostudies-literature
UnknownPan-cancer classification by regularized multi-task learning.
Classifying pan-cancer samples using gene expression patterns is a crucial challenge for the accurate diagnosis and treatment of cancer patients. Machine learning algorithms have been considered proven tools to perform downstream analysis and capture the deviations in gene expression patterns across...
S-EPMC8688544 | biostudies-literature
UnknownMachine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins.

Background

COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Prot...
S-EPMC7470713 | biostudies-literature
UnknownDiscovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model.
Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease's progression, helping the disease's etiology, which is still mostly unknown...
S-EPMC8041769 | biostudies-literature
UnknownMultiobjective triclustering of time-series transcriptome data reveals key genes of biological processes.

Background

Exploratory analysis of multi-dimensional high-throughput datasets, such as microarray gene expression time series, may be instrumental in understanding the genetic programs underlying numerous biological processes. In such datasets, variations in the gene expression profiles are ...
S-EPMC4480927 | biostudies-literature
UnknownAn ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods that best classify driver mutations in cancer.

Background and objective

Accurate identification and prioritization of driver-mutations in cancer is critical for effective patient management. Despite the presence of numerous bioinformatic algorithms for estimating mutation pathogenicity, there is significant variation in their assessments...
S-EPMC11744934 | biostudies-literature
GenomicsABO_OICR_DATASET - samples
OICR PANCREATIC CANCER DATASET
EGAD00001000262 | EGA
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