Genetics

Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis.

Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (M.tb), causes highest number of deaths globally for any bacterial disease necessitating novel diagnosis and treatment strategies. High-throughput sequencing methods generate a large amount of data which could be exploited in determining multi-drug resistant (MDR-TB) associated mutations. The present work is a computatio...

Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.

Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis. With the purpose of advancing to personalized medicine framework, accurate diagnoses allow prescription of more effective treatments adapted to the specificities of each individual case. In the last years, next-generation sequencing has impelled ca...

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO.

The high density, large capacity, and long-term stability of DNA molecules make them an emerging storage medium that is especially suitable for the lo...

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.

OBJECTIVES: To assess the diagnostic accuracy of machine learning (ML) in predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma ...

Machine Learning Techniques for Classifying the Mutagenic Origins of Point Mutations.

There is increasing interest in developing diagnostics that discriminate individual mutagenic mechanisms in a range of applications that include ident...

MethylNet: an automated and modular deep learning approach for DNA methylation analysis.

BACKGROUND: DNA methylation (DNAm) is an epigenetic regulator of gene expression programs that can be altered by environmental exposures, aging, and i...

Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival.

Total tumor size (TS) metrics used in TS models in oncology do not consider tumor heterogeneity, which could help to better predict drug efficacy. We ...

Nonlinear Spiking Neural P Systems.

This paper proposes a new variant of spiking neural P systems (in short, SNP systems), nonlinear spiking neural P systems (in short, NSNP systems). In...

Mb-level CpG and TFBS islands visualized by AI and their roles in the nuclear organization of the human genome.

Unsupervised machine learning that can discover novel knowledge from big sequence data without prior knowledge or particular models is highly desirabl...

Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases.

Hepatocellular carcinoma (HCC) is a common malignant tumor in China. In the present study, we aimed to construct and verify a prediction model of recu...

Application of Artificial Neural Network for Prediction of Risk of Multiple Sclerosis Based on Single Nucleotide Polymorphism Genotypes.

The artificial neural network (ANN) is a sort of machine learning method which has been used in determination of risk of human disorders. In the curre...

Molecular and DNA Artificial Neural Networks via Fractional Coding.

This article considers implementation of artificial neural networks (ANNs) using molecular computing and DNA based on fractional coding. Prior work ha...

Machine Learning-Guided Prediction of Antigen-Reactive In Silico Clonotypes Based on Changes in Clonal Abundance through Bio-Panning.

c-Met is a promising target in cancer therapy for its intrinsic oncogenic properties. However, there are currently no c-Met-specific inhibitors availa...

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning.

Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treat...

ProNA2020 predicts protein-DNA, protein-RNA, and protein-protein binding proteins and residues from sequence.

The intricate details of how proteins bind to proteins, DNA, and RNA are crucial for the understanding of almost all biological processes. Disease-cau...

Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately iden...

Collective effects of long-range DNA methylations predict gene expressions and estimate phenotypes in cancer.

DNA methylation of various genomic regions has been found to be associated with gene expression in diverse biological contexts. However, most genome-w...

DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images.

Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and...

Big data in IBD: big progress for clinical practice.

IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system,...

Machine learning methods for microbiome studies.

Researches on the microbiome have been actively conducted worldwide and the results have shown human gut bacterial environment significantly impacts o...