Genetics

Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches.

Aberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites. We used genome-wide DNA methylation patterns from both cancerous and no...

A machine-learning based model for automated recommendation of individualized treatment of rifampicin-resistant tuberculosis.

BACKGROUND: Rifampicin resistant tuberculosis remains a global health problem with almost half a million new cases annually. In high-income countries patients empirically start a standardized treatment regimen, followed by an individualized regimen guided by drug susceptibility test (DST) results. In most settings, DST information is not available or is limited to isoniazid and fluoroquinolones. W...

A machine learning approach for rapid early detection of Campylobacter spp. using absorbance spectra collected from enrichment cultures.

Enumeration of Campylobacter from environmental waters can be difficult due to its low concentrations, which can still pose a significant health risk....

Improving viral annotation with artificial intelligence.

Viruses of bacteria, "phages," are fundamental, poorly understood components of microbial community structure and function. Additionally, their depend...

Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences.

N-terminal coding sequence (NCS) influences gene expression by impacting the translation initiation rate. The NCS optimization problem is to find an N...

m5C-Seq: Machine learning-enhanced profiling of RNA 5-methylcytosine modifications.

Epigenetic modifications, particularly RNA methylation and histone alterations, play a crucial role in heredity, development, and disease. Among these...

Probing Nanotopography-Mediated Macrophage Polarization via Integrated Machine Learning and Combinatorial Biophysical Cue Mapping.

Inflammatory responses, leading to fibrosis and potential host rejection, significantly hinder the long-term success and widespread adoption of biomed...

Joint trajectory inference for single-cell genomics using deep learning with a mixture prior.

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights int...

Low-pass whole genome sequencing of circulating tumor cells to evaluate chromosomal instability in triple-negative breast cancer.

Chromosomal Instability (CIN) is a common and evolving feature in breast cancer. Large-scale Transitions (LSTs), defined as chromosomal breakages lead...

Deep neural networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer.

MOTIVATION: There exists an unexplained diverse variation within the predefined colon cancer stages using only features from either genomics or histop...

Deciphering colorectal cancer radioresistance and immune microrenvironment: unraveling the role of EIF5A through single-cell RNA sequencing and machine learning.

BACKGROUND: Radiotherapy (RT) is a critical component of treatment for locally advanced rectal cancer (LARC), though patient response varies significa...

MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.

Drug therapy remains the primary approach to treating tumours. Variability among cancer patients, including variations in genomic profiles, often resu...

Genetic Artificial Hummingbird Algorithm-Support Vector Machine for Timely Power Theft Detection.

Utilities face serious obstacles from power theft, which calls for creative ways to maintain income and improve operational effectiveness. This study ...

SG-Fusion: A swin-transformer and graph convolution-based multi-modal deep neural network for glioma prognosis.

The integration of morphological attributes extracted from histopathological images and genomic data holds significant importance in advancing tumor d...

Deciphering Dormant Cells of Lung Adenocarcinoma: Prognostic Insights from O-glycosylation-Related Tumor Dormancy Genes Using Machine Learning.

Lung adenocarcinoma (LUAD) poses significant challenges due to its complex biological characteristics and high recurrence rate. The high recurrence ra...

Salivary Molecular Spectroscopy with Machine Learning Algorithms for a Diagnostic Triage for Amelogenesis Imperfecta.

Amelogenesis imperfecta (AI) is a genetic disease characterized by poor formation of tooth enamel. AI occurs due to mutations, especially in AMEL, ENA...

Identification of immune-related biomarkers for intracerebral hemorrhage diagnosis based on RNA sequencing and machine learning.

BACKGROUND: Intracerebral hemorrhage (ICH) is a severe stroke subtype with high morbidity, disability, and mortality rates. Currently, no biomarkers f...

Deciphering the Language of Protein-DNA Interactions: A Deep Learning Approach Combining Contextual Embeddings and Multi-Scale Sequence Modeling.

Deciphering the mechanisms governing protein-DNA interactions is crucial for understanding key cellular processes and disease pathways. In this work, ...

CNVDeep: deep association of copy number variants with neurocognitive disorders.

BACKGROUND: Copy number variants (CNVs) have become increasingly instrumental in understanding the etiology of all diseases and phenotypes, including ...

Deep learning aided measurement of outer retinal layer metrics as biomarkers for inherited retinal degenerations: opportunities and challenges.

PURPOSE OF REVIEW: The purpose of this review was to provide a summary of currently available retinal imaging and visual function testing methods for ...