Genetics

The role of mitophagy-related genes in prognosis and immunotherapy of cutaneous melanoma: a comprehensive analysis based on single-cell RNA sequencing and machine learning.

Mitophagy, the selective degradation of mitochondria by autophagy, plays a crucial role in cancer progression and therapy response. This study aims to elucidate the role of mitophagy-related genes (MRGs) in cutaneous melanoma (CM) through single-cell RNA sequencing (scRNA-seq) and machine learning approaches, ultimately developing a predictive model for patient prognosis. The scRNA-seq data, bulk ...

Cooking loss estimation of semispinalis capitis muscle of pork butt using a deep neural network on hyperspectral data.

This study evaluated the performance of a deep-learning-based model that predicted cooking loss in the semispinalis capitis (SC) muscle of pork butts using hyperspectral images captured 24 h postmortem. To overcome low-scale samples, 70 pork butts were used with pixel-based data augmentation. Principal component regression (PCR) and partial least squares regression (PLSR) models for predicting coo...

The KMeansGraphMIL Model: A Weakly Supervised Multiple Instance Learning Model for Predicting Colorectal Cancer Tumor Mutational Burden.

Colorectal cancer (CRC) is one of the top three most lethal malignancies worldwide, posing a significant threat to human health. Recently proposed imm...

A graph neural network-based model with out-of-distribution robustness for enhancing antiretroviral therapy outcome prediction for HIV-1.

Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limit...

Combining machine learning and single-cell sequencing to identify key immune genes in sepsis.

This research aimed to identify novel indicators for sepsis by analyzing RNA sequencing data from peripheral blood samples obtained from sepsis patien...

SMR-guided molecular subtyping and machine learning model reveals novel prognostic biomarkers and therapeutic targets in non-small cell lung adenocarcinoma.

Non-small cell lung adenocarcinoma (LUAD) is a markedly heterogeneous disease, with its underlying molecular mechanisms and prognosis prediction prese...

The role of chromatin state in intron retention: A case study in leveraging large scale deep learning models.

Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and com...

A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data.

Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effe...

Applying artificial intelligence to uncover the genetic landscape of coagulation factors.

Artificial intelligence (AI) is rapidly advancing our ability to identify and interpret genetic variants associated with coagulation factor deficienci...

Machine Learning Recognition of Artificial DNA Sequence with Quantum Tunneling Nanogap Junction.

Artificially synthesized DNA holds significant promise in addressing fundamental biochemical questions and driving advancements in biotechnology, gene...

Estimation of TP53 mutations for endometrial cancer based on diffusion-weighted imaging deep learning and radiomics features.

OBJECTIVES: To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical varia...

single nucleotide polymorphism reduces dabigatran acylglucuronide formation in humans.

BACKGROUND: Dabigatran etexilate (DABE), a prodrug of dabigatran (DAB), is a direct thrombin inhibitor used to prevent ischemic stroke and thromboembo...

Genomic determinants of biological age estimated by deep learning applied to retinal images.

With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from laten...

A machine learning approach for estimating Eastern Asian origins from massive screening of Y chromosomal short tandem repeats polymorphisms.

Inferring the ancestral origin of DNA evidence recovered from crime scenes is crucial in forensic investigations, especially in the absence of a direc...

Phenotypic antibiotic resistance prediction using antibiotic resistance genes and machine learning models in Mannheimia haemolytica.

Mannheimia haemolytica is one of the most common causative agents of bovine respiratory disease (BRD); however, antibiotic resistance in this species ...

Single-nucleotide polymorphisms in genes associated with the vitamin D pathway related to clinical and therapeutic outcomes of American tegumentary leishmaniasis.

BACKGROUND: The vitamin D pathway contributes to the microbicidal activity of macrophages against infection. In addition to induction of this pathway...

Understanding the impacts of drought on peanuts L.): exploring physio-genetic mechanisms to develop drought-resilient peanut cultivars.

Peanut is a vital source of protein, particularly in the tropical regions of Asian and African countries. About three-quarters of peanut production oc...

Diagnostic and prognostic perspectives of Fabry disease via fiber evanescent wave spectroscopy advanced by machine learning.

Fabry disease (FD) is a rare disorder resulting from a genetic mutation characterized by the accumulation of sphingolipids in various cells throughout...

ARGai 1.0: A GAN augmented in silico approach for identifying resistant genes and strains in E. coli using vision transformer.

The emergence of infectious disease and antibiotic resistance in bacteria like Escherichia coli (E. coli) shows the necessity for novel computational ...

SProtFP: a machine learning-based method for functional classification of small ORFs in prokaryotes.

Small proteins (≤100 amino acids) play important roles across all life forms, ranging from unicellular bacteria to higher organisms. In this study, we...