bioinformatics

This repository contains the code and documentation for a computational framework that leverages machine learning techniques to enable accurate classification of bacterial species, even closely related strains.

The framework integrates genomic analysis methods, such as motif screening and single nucleotide polymorphism (SNP) extraction, to derive informative features from bacterial genomes. These genomic insights are then fed into machine learning models, which are trained to reliably differentiate between bacterial species based on their distinctive patterns and characteristics.

PROTEIN STRUCTURE AND SYNTHETIC MULTI-VIEW CLUSTERING DATASETS

Multi-View Clustering (MVC) datasets used in the following paper:

Evolutionary Multi-objective Clustering Over Multiple Conflicting Data Views. Authors: Mario Garza-Fabre, Julia Handl, and Adán José-García. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION. Accepted for publication, November 2022.

This entry contains all 420 datasets used in the paper, including:

This study presented six datasets for DNA/RNA sequence alignment for one of the most common alignment algorithms, namely, the Needleman–Wunsch (NW) algorithm. This research proposed a fast and parallel implementation of the NW algorithm by using machine learning techniques. This study is an extension and improved version of our previous work . The current implementation achieves 99.7% accuracy using a multilayer perceptron with ADAM optimizer and up to 2912 giga cell updates per second on two real DNA sequences with a of length 4.1 M nucleotides.

Large p small n problem is a challenging problem in big data analytics. There are no de facto standard methods available to it. In this study, we propose a tensor decomposition (TD) based unsupervised feature extraction (FE) formalism applied to multiomics datasets, where the number of features is more than 100000 while the number of instances is as small as about 100.

The dataset is used to detect essential protein in uncertain PPI network.

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Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintain cellular functions. Recently, it has become evident that metabolism is not only responsible for generating the required energy and controlling the abundance of metabolites within a cell, but also has an important role in and influence on cellular fate specification.