Genetics

Determining structures of RNA conformers using AFM and deep neural networks.

Much of the human genome is transcribed into RNAs, many of which contain structural elements that are important for their function. Such RNA molecules-including those that are structured and well-folded-are conformationally heterogeneous and flexible, which is a prerequisite for function, but this limits the applicability of methods such as NMR, crystallography and cryo-electron microscopy for str...

Recognizing SARS-CoV-2 infection of nasopharyngeal tissue at the single-cell level by machine learning method.

SARS-CoV-2 has posed serious global health challenges not only because of the high degree of virus transmissibility but also due to its severe effects on the respiratory system, such as inducing changes in multiple organs through the ACE2 receptor. This virus makes changes to gene expression at the single-cell level and thus to cellular functions and immune responses in a variety of cell types. Pr...

Clinical characteristics and prediction model of re-positive nucleic acid tests among Omicron infections by machine learning: a real-world study of 35,488 cases.

BACKGROUND: During the Omicron BA.2 variant outbreak in Shanghai, China, from April to May 2022, PCR nucleic acid test re-positivity (TR) occurred fre...

The constrained-disorder principle defines the functions of systems in nature.

The Constrained Disorder Principle (CDP) defines all systems in nature by their degree of inherent variability. Per the CDP, the intrinsic variability...

Comparison of Intratumoral and Peritumoral Deep Learning, Radiomics, and Fusion Models for Predicting KRAS Gene Mutations in Rectal Cancer Based on Endorectal Ultrasound Imaging.

MAIN OBJECTIVES: We aimed at comparing intratumoral and peritumoral deep learning, radiomics, and fusion models in predicting KRAS mutations in rectal...

Deep profiling of gene expression across 18 human cancers.

Clinical and biological information in large datasets of gene expression across cancers could be tapped with unsupervised deep learning. However, diff...

Advancing miRNA cancer research through artificial intelligence: from biomarker discovery to therapeutic targeting.

MicroRNAs (miRNAs), a class of small non-coding RNAs, play a vital role in regulating gene expression at the post-transcriptional level. Their discove...

Cropformer: An interpretable deep learning framework for crop genomic prediction.

Machine learning and deep learning are extensively employed in genomic selection (GS) to expedite the identification of superior genotypes and acceler...

An Artificial Neural Network for Image Classification Inspired by the Aversive Olfactory Learning Neural Circuit in Caenorhabditis elegans.

This study introduces an artificial neural network (ANN) for image classification task, inspired by the aversive olfactory learning neural circuit in ...

Size-Coded Hydrogel Microbeads for Extraction-Free Serum Multi-miRNAs Quantifications with Machine-Learning-Aided Lung Cancer Subtypes Classification.

Classifying lung cancer subtypes, which are characterized by multi-microRNAs (miRNAs) upregulation, is important for therapy and prognosis evaluation....

Machine Learning and Mendelian Randomization Reveal Molecular Mechanisms and Causal Relationships of Immune-Related Biomarkers in Periodontitis.

This study aimed to investigate the molecular mechanisms of periodontitis and identify key immune-related biomarkers using machine learning and Mendel...

Artificial Intelligence-Assisted Comparative Analysis of the Overlapping Molecular Pathophysiology of Alzheimer's Disease, Amyotrophic Lateral Sclerosis, and Frontotemporal Dementia.

The overlapping molecular pathophysiology of Alzheimer's Disease (AD), Amyotrophic Lateral Sclerosis (ALS), and Frontotemporal Dementia (FTD) was anal...

Advancing precision and personalized breast cancer treatment through multi-omics technologies.

Breast cancer is the most common malignant tumour in women, with more than 685,000 women dying of breast cancer each year. The heterogeneity of breast...

Subspace learning using low-rank latent representation learning and perturbation theorem: Unsupervised gene selection.

In recent years, gene expression data analysis has gained growing significance in the fields of machine learning and computational biology. Typically,...

Fuzzy-Based Identification of Transition Cells to Infer Cell Trajectory for Single-Cell Transcriptomics.

With the continuous evolution of single-cell RNA sequencing technology, it has become feasible to reconstruct cell development processes using computa...

Metabolic Fluxes Using Deep Learning Based on Enzyme Variations: .

Metabolic pathway modeling, essential for understanding organism metabolism, is pivotal in predicting genetic mutation effects, drug design, and biofu...

extract in combination with lytic phage cocktails: a promising therapeutic approach against biofilms of multi-drug resistant .

Antimicrobial resistance (AMR) poses a significant global threat to public health systems, rendering antibiotics ineffective in treating infectious di...

Harnessing lactic acid bacteria for nicotinamide mononucleotide biosynthesis: a review of strategies and future directions.

Nicotinamide mononucleotide (NMN), one of the crucial precursors of nicotinamide adenine dinucleotide, has garnered considerable interest for its phar...

Multimodal Alzheimer's disease classification through ensemble deep random vector functional link neural network.

Alzheimer's disease (AD) is a condition with a complex pathogenesis, sometimes hereditary, characterized by the loss of neurons and synapses, along wi...

Synthetic biology and artificial intelligence in crop improvement.

Synthetic biology plays a pivotal role in improving crop traits and increasing bioproduction through the use of engineering principles that purposeful...