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Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

Computational intelligence and neuroscience
Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonpara...

Machine learning applications in genetics and genomics.

Nature reviews. Genetics
The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning ap...

Assessing the effects of data selection and representation on the development of reliable E. coli sigma 70 promoter region predictors.

PloS one
As the number of sequenced bacterial genomes increases, the need for rapid and reliable tools for the annotation of functional elements (e.g., transcriptional regulatory elements) becomes more desirable. Promoters are the key regulatory elements, whi...

Improved feature-based prediction of SNPs in human cytochrome P450 enzymes.

Interdisciplinary sciences, computational life sciences
Single nucleotide polymorphisms (SNPs) make up the most common form of mutations in human cytochrome P450 enzymes family, and have the potential to bring with different drug responses or specific diseases in individual patients. Here, based on machin...

Estimation of teaching-learning-based optimization primer design using regression analysis for different melting temperature calculations.

IEEE transactions on nanobioscience
Primers plays important role in polymerase chain reaction (PCR) experiments, thus it is necessary to select characteristic primers. Unfortunately, manual primer design manners are time-consuming and easy to get human negligence because many PCR const...

Prediction of DNA i-motifs via machine learning.

Nucleic acids research
i-Motifs (iMs), are secondary structures formed in cytosine-rich DNA sequences and are involved in multiple functions in the genome. Although putative iM forming sequences are widely distributed in the human genome, the folding status and strength of...

H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA.

Briefings in bioinformatics
2'-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high-throughput detection, the chemical stability of 2OM makes it diff...

ORI-Explorer: a unified cell-specific tool for origin of replication sites prediction by feature fusion.

Bioinformatics (Oxford, England)
MOTIVATION: The origins of replication sites (ORIs) are precise regions inside the DNA sequence where the replication process begins. These locations are critical for preserving the genome's integrity during cell division and guaranteeing the faithfu...

HEAP: a task adaptive-based explainable deep learning framework for enhancer activity prediction.

Briefings in bioinformatics
Enhancers are crucial cis-regulatory elements that control gene expression in a cell-type-specific manner. Despite extensive genetic and computational studies, accurately predicting enhancer activity in different cell types remains a challenge, and t...

iDeLUCS: a deep learning interactive tool for alignment-free clustering of DNA sequences.

Bioinformatics (Oxford, England)
SUMMARY: We present an interactive Deep Learning-based software tool for Unsupervised Clustering of DNA Sequences (iDeLUCS), that detects genomic signatures and uses them to cluster DNA sequences, without the need for sequence alignment or taxonomic ...