Genetics

A deep-learning model for predicting tyrosine kinase inhibitor response from histology in gastrointestinal stromal tumor.

Over 90% of gastrointestinal stromal tumors (GISTs) harbor mutations in KIT or PDGFRA that can predict response to tyrosine kinase inhibitor (TKI) therapies, as recommended by NCCN (National Comprehensive Cancer Network) guidelines. However, gene sequencing for mutation testing is expensive and time-consuming and is susceptible to a variety of preanalytical factors. To overcome the challenges asso...

Interpretable AI for inference of causal molecular relationships from omics data.

The discovery of molecular relationships from high-dimensional data is a major open problem in bioinformatics. Machine learning and feature attribution models have shown great promise in this context but lack causal interpretation. Here, we show that a popular feature attribution model, under certain assumptions, estimates an average of a causal quantity reflecting the direct influence of one vari...

A Perspective on Artificial Intelligence for Molecular Pathologists.

The widespread adoption of next-generation sequencing technology in molecular pathology has enabled us to interrogate the genome as never before. The ...

Integrating single-cell sequencing and machine learning to uncover the role of mitophagy in subtyping and prognosis of esophageal cancer.

Globally, esophageal cancer stands as a prominent contributor to cancer-related fatalities, distinguished by its poor prognosis. Mitophagy has a signi...

Functionally characterizing obesity-susceptibility genes using CRISPR/Cas9, in vivo imaging and deep learning.

Hundreds of loci have been robustly associated with obesity-related traits, but functional characterization of candidate genes remains a bottleneck. A...

Reliability-enhanced data cleaning in biomedical machine learning using inductive conformal prediction.

Accurately labeling large datasets is important for biomedical machine learning yet challenging while modern data augmentation methods may generate no...

Comparative analysis of machine learning models for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer: An MRI radiomics approach.

PURPOSE: The aim of this work is to compare different machine learning models for predicting pathological complete response (pCR) to neoadjuvant chemo...

metaCDA: A Novel Framework for CircRNA-Driven Drug Discovery Utilizing Adaptive Aggregation and Meta-Knowledge Learning.

In the emerging field of RNA drugs, circular RNA (circRNA) has attracted much attention as a novel multifunctional therapeutic target. Delving deeper ...

Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.

Machine learning (ML) is changing the world of computational protein design, with data-driven methods surpassing biophysical-based methods in experime...

Deep learning-based quick MLC sequencing for MRI-guided online adaptive radiotherapy: a feasibility study for pancreatic cancer patients.

One bottleneck of magnetic resonance imaging (MRI)-guided online adaptive radiotherapy is the time-consuming daily online replanning process. The curr...

Accuracy of Commercial Large Language Model (ChatGPT) to Predict the Diagnosis for Prehospital Patients Suitable for Ambulance Transport Decisions: Diagnostic Accuracy Study.

OBJECTIVES: While ambulance transport decisions guided by artificial intelligence (AI) could be useful, little is known of the accuracy of AI in makin...

DeepInterAware: Deep Interaction Interface-Aware Network for Improving Antigen-Antibody Interaction Prediction from Sequence Data.

Identifying interactions between candidate antibodies and target antigens is a key step in developing effective human therapeutics. The antigen-antibo...

Molecular structure of NRG-1 protein and its impact on adult hypertension and heart failure: A new clinical Indicator diagnosis based on advanced machine learning.

The purpose of this study was to investigate the molecular structure of NRG-1 protein and its mechanism of action in adult hypertensive heart failure....

Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging.

Deep learning (DL) and explainable artificial intelligence (XAI) have emerged as powerful machine-learning tools to identify complex predictive data p...

Circular RNA-Drug Association Prediction Based on Multi-Scale Convolutional Neural Networks and Adversarial Autoencoders.

The prediction of circular RNA (circRNA)-drug associations plays a crucial role in understanding disease mechanisms and identifying potential therapeu...

Comparing statistical learning methods for complex trait prediction from gene expression.

Accurate prediction of complex traits is an important task in quantitative genetics. Genotypes have been used for trait prediction using a variety of ...

Deep learning for RNA structure prediction.

Predicting RNA structures from sequences with computational approaches is of vital importance in RNA biology considering the high costs of experimenta...

Machine learning-enhanced surface-enhanced spectroscopic detection of polycyclic aromatic hydrocarbons in the human placenta.

The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, polycyclic aromatic compounds (PACs), are essential...

Interpretable Dynamic Directed Graph Convolutional Network for Multi-Relational Prediction of Missense Mutation and Drug Response.

Tumor heterogeneity presents a significant challenge in predicting drug responses, especially as missense mutations within the same gene can lead to v...

Forecasting Epidemic Spread With Recurrent Graph Gate Fusion Transformers.

Predicting the unprecedented, nonlinear nature of COVID-19 presents a significant public health challenge. Recent advances in deep learning, such as g...