Genetics

Integrating Machine Learning-Based Approaches into the Design of ASO Therapies.

Rare diseases impose a significant burden on affected individuals, caregivers, and healthcare systems worldwide. Developing effective therapeutics for these small patient populations presents substantial challenges. Antisense oligonucleotides (ASOs) have emerged as a promising therapeutic approach that targets the underlying genetic cause of disease at the RNA level. Several ASOs have gained FDA a...

ERNIE-ac4C: A Novel Deep Learning Model for Effectively Predicting N4-acetylcytidine Sites.

RNA modifications are known to play a critical role in gene regulation and cellular processes. Specifically, N4-acetylcytidine (ac4C) modification has emerged as a significant marker involved in mRNA translation efficiency, stability, and various diseases. Accurate identification of ac4C modification sites is essential for unraveling its functional implications. However, currently available experi...

Machine Learning and Experiments Revealed Key Genes Related to PANoptosis Linked to Drug Prediction and Immune Landscape in Spinal Cord Injury.

Spinal cord injury (SCI) is a severe central nervous system injury without effective therapies. PANoptosis is involved in the development of many dise...

Identification of therapeutic targets for Alzheimer's Disease Treatment using bioinformatics and machine learning.

Alzheimer's disease (AD) is a complex neurodegenerative disorder that currently lacks effective treatment options. This study aimed to identify potent...

Interpretable multi-stage attention network to predict cancer subtype, microsatellite instability, TP53 mutation and TMB of endometrial and colorectal cancer.

Mismatch repair deficiency (dMMR), also known as high-grade microsatellite instability (MSI-H), is a well-established biomarker for predicting the imm...

ZeRPI: A graph neural network model for zero-shot prediction of RNA-protein interactions.

RNA-protein interactions are crucial for biological functions across multiple levels. RNA binding proteins (RBPs) intricately engage in diverse biolog...

Neural-symbolic hybrid model for myosin complex in cardiac ventriculum decodes structural bases for inheritable heart disease from its genetic encoding.

BACKGROUND: Human ventriculum myosin (βmys) powers contraction sometimes in complex with myosin binding protein C (MYBPC3). The latter regulates βmys ...

Learning the language of life with AI.

In 2021, a year before ChatGPT took the world by storm amid the excitement about generative artificial intelligence (AI), AlphaFold 2 cracked the 50-y...

Integrating multiomics analysis and machine learning to refine the molecular subtyping and prognostic analysis of stomach adenocarcinoma.

Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and a lack of highly precise treatment options. We downloaded the multiom...

Improved GOA-based fuzzy PI speed control of PMSM with predictive current regulation.

To address the susceptibility of conventional vector control systems for permanent magnet synchronous motors (PMSMs) to motor parameter variations and...

The prediction of RNA-small-molecule ligand binding affinity based on geometric deep learning.

Small molecule-targeted RNA is an emerging technology that plays a pivotal role in drug discovery and inhibitor design, with widespread applications i...

Pathogen genomic surveillance and the AI revolution.

The unprecedented sequencing efforts during the COVID-19 pandemic paved the way for genomic surveillance to become a powerful tool for monitoring the ...

A multi-modal transformer for cell type-agnostic regulatory predictions.

Sequence-based deep learning models have emerged as powerful tools for deciphering the cis-regulatory grammar of the human genome but cannot generaliz...

Spatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics.

The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylatio...

Fluo-Cast-Bright: a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live oocytes.

In mammalian oocytes, large-scale chromatin organization regulates transcription, nuclear architecture, and maintenance of chromosome stability in pre...

scSMD: a deep learning method for accurate clustering of single cells based on auto-encoder.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has transformed biological research by offering new insights into cellular heterogeneity, developme...

Unveiling genetic basis of seedling emergence from deep soil depth under dry direct- seeded conditions in rice ( L.).

Water scarcity and labor shortage pose significant challenges in rice farming. Direct-seeded rice (DSR) is an efficient method that conserves water, r...

Multimodal learning for mapping genotype-phenotype dynamics.

How complex phenotypes emerge from intricate gene expression patterns is a fundamental question in biology. Integrating high-content genotyping approa...

F-FDG PET/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients.

PURPOSE: Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer...

Caps-ac4C: An effective computational framework for identifying N4-acetylcytidine sites in human mRNA based on deep learning.

N4-acetylcytidine (ac4C) is a crucial post-transcriptional modification in human mRNA, involving the acetylation of the nitrogen atom at the fourth po...