Genetics

Unlocking Early Cancer Detection: Leveraging Machine Learning in Cell-Free DNA Analysis for Precision Oncology.

This study introduces a groundbreaking approach to early cancer detection through the analysis of cell-free DNA (cfDNA), utilizing machine learning algorithms to navigate the complexities of low circulating tumor DNA (ctDNA) fractions and genetic heterogeneity. CfDNA, found in bodily fluids and comprising fragments from apoptotic or necrotic cells, offers a non-invasive means to identify cancer si...

An effective deep learning-based approach for splice site identification in gene expression.

A crucial stage in eukaryote gene expression involves mRNA splicing by a protein assembly known as the spliceosome. This step significantly contributes to generating and properly operating the ultimate gene product. Since non-coding introns disrupt eukaryotic genes, splicing entails the elimination of introns and joining exons to create a functional mRNA molecule. Nevertheless, accurately finding ...

Pentagon Found Daily, Metagenomic Detection of Novel Bioaerosol Threats to Be Cost-Prohibitive: Can Virtualization and AI Make It Cost-Effective?

In 2022, the Pentagon Force Protection Agency found threat agnostic detection of novel bioaerosol threats to be "not feasible for daily operations" du...

Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R.

Deep learning has emerged as a powerful tool for solving complex problems, including reconstruction of gene regulatory networks within the realm of bi...

Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations.

Precision medicine models often perform better for populations of European ancestry due to the over-representation of this group in the genomic datase...

Multi-omics Combined with Machine Learning Facilitating the Diagnosis of Gastric Cancer.

Gastric cancer (GC) is a highly intricate gastrointestinal malignancy. Early detection of gastric cancer forms the cornerstone of precision medicine. ...

Identifying Hub Genes for Glaucoma based on Bulk RNA Sequencing Data and Multi-machine Learning Models.

AIMS: The aims of this study were to determine hub genes in glaucoma through multiple machine learning algorithms.

Comparative Analysis of Machine Learning Algorithms Evaluating the Single Nucleotide Polymorphisms of Metabolizing Enzymes with Clinical Outcomes Following Intravenous Paracetamol in Preterm Neonates with Patent Ductus Arteriosus.

AIMS: Pharmacogenomics has been identified to play a crucial role in determining drug response. The present study aimed to identify significant geneti...

A Machine Learning Approach for Predicting Essentiality of Metabolic Genes.

The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that conver...

Prediction of LncRNA-protein Interactions Using Auto-Encoder, SE-ResNet Models and Transfer Learning.

BACKGROUND: Long non-coding RNA (lncRNA) plays a crucial role in various biological processes, and mutations or imbalances of lncRNAs can lead to seve...

Classification of DNA Sequence Based on a Non-gradient Algorithm: Pseudoinverse Learners.

This chapter proposes a prototype-based classification approach for analyzing DNA barcodes that uses a spectral representation of DNA sequences and a ...

Peptidic Compound as DNA Binding Agent: Fragment-based Design, Machine Learning, Molecular Modeling, Synthesis, and DNA Binding Evaluation.

BACKGROUND: Cancer remains a global burden, with increasing mortality rates. Current cancer treatments involve controlling the transcription of malign...

Hematological Indices and Genetic Variants of Premature Ovarian Insufficiency: Machine Learning Approaches.

BACKGROUND: Premature Ovarian Insufficiency (POI) is associated with infertility. Little is known about the potential circulating biomarkers that coul...

AI will not give us precision medicine.

The completion of human DNA sequencing in the early 2000s initially generated widespread excitement and hope that it would revolutionize medicine. Ove...

Interpreting Gene Ontology Annotations Derived from Sequence Homology Methods.

The Gene Ontology (GO) project describes the functions of the gene products of organisms from all kingdoms of life in a standardized way, enabling pow...

Promoter Prediction in Agrobacterium tumefaciens Strain C58 by Using Artificial Intelligence Strategies.

Promoters are the genomic regions upstream of genes that RNA polymerase binds in order to initiate gene transcription. Understanding the regulation of...

GAN Learning Methods for Bulk RNA-Seq Data and Their Interpretive Application in the Context of Disease Progression.

A generative adversarial network (GAN) is a generative model that consists of two adversarial networks, a discriminator and a generator, usually in th...

Machine and Deep Learning in Hyperspectral Fluorescence-Guided Brain Tumor Surgery.

Malignant glioma resection is often the first line of treatment in neuro-oncology. During glioma surgery, the discrimination of tumor's edges can be c...

Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and m...

An artificial intelligence model for embryo selection in preimplantation DNA methylation screening in assisted reproductive technology.

Embryo quality is a critical determinant of clinical outcomes in assisted reproductive technology (ART). A recent clinical trial investigating preimpl...