Genetics

Machine learning enhances genotoxicity assessment using MultiFlow® DNA damage assay.

Genotoxicity is a critical determinant for assessing the safety of pharmaceutical drugs, their metabolites, and impurities. Among genotoxicity tests, mechanistic assays such as the MultiFlow® DNA damage assay (MFA) allows the investigations on mode of action (MoA) of DNA damage through four mechanistic markers recorded at two time points. Previous studies have shown that machine learning (ML) can ...

Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival.

Cervical cancer is a deadly disease in women globally. There is a greater chance of getting rid of cervical cancer in case of earliest diagnosis. But for some patients, there is a chance of recurrence. The chances of treating the Recurrence of cervical carcinoma arelimited. The main objective of a research is to find the key features that will predict the cervical cancer recurrence and survival ra...

Analysis of diagnostic genes and molecular mechanisms of Crohn's disease and colon cancer based on machine learning algorithms.

Crohn's disease (CD) is a chronic inflammatory bowel condition, and colon adenocarcinoma (COAD), as one of the most prevalent malignant tumors of the ...

Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper.

Antibiotic resistance is a global public health concern. Bacteria have evolved resistance to most antibiotics, which means that for any given bacteria...

Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction.

The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental m...

From Outbreaks to Artificial Intelligence: A Comprehensive Review of Monkeypox Virus Epidemiology, Diagnosis, Treatment, Vaccination, and Deep Learning Applications.

After the global impact of the COVID-19 pandemic, concerns over virus transmission have risen. A state of health emergency was declared in 2022 due t...

An appropriate DNA input for bisulfite conversion reveals LINE-1 and Alu hypermethylation in tissues and circulating cell-free DNA from cancers.

The autonomous and active Long-Interspersed Element-1 (LINE-1, L1) and the non-autonomous Alu retrotransposon elements, contributing to 30% of the hum...

A Robust and Efficient Representation-based DNA Storage Architecture by Deep Learning.

As one main form of multimedia data, images play a critical role in various applications. In this paper, a representation-based architecture is propos...

Multiomics integration and machine learning reveal prognostic programmed cell death signatures in gastric cancer.

Gastric cancer (GC) is characterized by notable heterogeneity and the impact of molecular subtypes on treatment and prognosis. The role of programmed ...

Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells.

Double negative T (DNT) cells are a unique subset of CD3 + TCRαβ + T lymphocytes that lack CD4, CD8, or NK1.1 expression and constitute 3-5% of the to...

Novel anoikis-related diagnostic biomarkers for aortic dissection based on machine learning.

Aortic dissection (AD) is one of the most dangerous diseases of the cardiovascular system, which is characterized by acute onset and poor prognosis, w...

Integrating immune multi-omics and machine learning to improve prognosis, immune landscape, and sensitivity to first- and second-line treatments for head and neck squamous cell carcinoma.

In recent years, immune checkpoint inhibitors (ICIs) has emerged as a fundamental component of the standard treatment regimen for patients with head a...

Phenotype prediction in plants is improved by integrating large-scale transcriptomic datasets.

Research on the dynamic expression of genes in plants is important for understanding different biological processes. We used the large amounts of tran...

Pathomics-based machine learning models for predicting pathological complete response and prognosis in locally advanced rectal cancer patients post-neoadjuvant chemoradiotherapy: insights from two independent institutional studies.

BACKGROUND: Accurate prediction of pathological complete response (pCR) and disease-free survival (DFS) in locally advanced rectal cancer (LARC) patie...

Hypothesis generation for rare and undiagnosed diseases through clustering and classifying time-versioned biological ontologies.

Rare diseases affect 1-in-10 people in the United States and despite increased genetic testing, up to half never receive a diagnosis. Even when using ...

Machine Learning Analysis of the Orbitofrontal Cortex Transcriptome of Human Opioid Users Identifies Shisa7 as a Translational Target Relevant for Heroin Seeking Leveraging a Male Rat Model.

BACKGROUND: Identifying neurobiological targets predictive of the molecular neuropathophysiological signature of human opioid use disorder (OUD) could...

Predicting RNA structure and dynamics with deep learning and solution scattering.

Advanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult t...

Predicting candidate biomarkers for COVID-19 associated with leukemia in children.

Since the COVID-19 pandemic, a significant number of pediatric leukemia patients have shown to have also contracted COVID-19 several weeks or months p...

LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning.

Sequence-function data provides valuable information about the protein functional landscape but is rarely obtained during directed evolution campaigns...

Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning.

Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosyst...