AI Medical Compendium Topic

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DNA, Mitochondrial

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Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms.

Frontiers in immunology
Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality worldwide. Preeclampsia is linked to mitochondrial dysfunction as a contributing factor in its progression. This study aimed to develop a novel diagnostic model b...

Diagnosis of Wilson Disease and Its Phenotypes by Using Artificial Intelligence.

Biomolecules
WD is caused by variants disrupting copper efflux resulting in excessive copper accumulation mainly in liver and brain. The diagnosis of WD is challenged by its variable clinical course, onset, morbidity, and variant type. Currently it is diagnosed...

MitoScape: A big-data, machine-learning platform for obtaining mitochondrial DNA from next-generation sequencing data.

PLoS computational biology
The growing number of next-generation sequencing (NGS) data presents a unique opportunity to study the combined impact of mitochondrial and nuclear-encoded genetic variation in complex disease. Mitochondrial DNA variants and in particular, heteroplas...

The application of machine learning to predict genetic relatedness using human mtDNA hypervariable region I sequences.

PloS one
Human identification of unknown samples following disaster and mass casualty events is essential, especially to bring closure to family and friends of the deceased. Unfortunately, victim identification is often challenging for forensic investigators ...

A new linear combination method of haplogroup distribution central vectors to model population admixtures.

Molecular genetics and genomics : MGG
We introduce a novel population genetic approach suitable to model the origin and relationships of populations, using new computation methods analyzing Hg frequency distributions. Hgs were selected into groups which show correlated frequencies in sub...

Evolution and dispersal of mitochondrial DNA haplogroup U5 in Northern Europe: insights from an unsupervised learning approach to phylogeography.

BMC genomics
BACKGROUND: We combined an unsupervised learning methodology for analyzing mitogenome sequences with maximum likelihood (ML) phylogenetics to make detailed inferences about the evolution and diversification of mitochondrial DNA (mtDNA) haplogroup U5,...

Adaptive sequencing using nanopores and deep learning of mitochondrial DNA.

Briefings in bioinformatics
Nanopore sequencing is an emerging technology that reads DNA by utilizing a unique method of detecting nucleic acid sequences and identifies the various chemical modifications they carry. Deep learning has increased in popularity as a useful techniqu...

Screening mitochondria-related biomarkers in skin and plasma of atopic dermatitis patients by bioinformatics analysis and machine learning.

Frontiers in immunology
BACKGROUND: There is a significant imbalance of mitochondrial activity and oxidative stress (OS) status in patients with atopic dermatitis (AD). This study aims to screen skin and peripheral mitochondria-related biomarkers, providing insights into th...

Mitochondrial mt12361A>G increased risk of metabolic dysfunction-associated steatotic liver disease among non-diabetes.

World journal of gastroenterology
BACKGROUND: Insulin resistance, lipotoxicity, and mitochondrial dysfunction contribute to the pathogenesis of metabolic dysfunction-associated steatotic liver disease (MASLD). Mitochondrial dysfunction impairs oxidative phosphorylation and increases ...