Genetics

Artificial intelligence techniques in inherited retinal diseases: A review

Inherited retinal diseases (IRDs) are a diverse group of genetic disorders that lead to progressive vision loss and are a major cause of blindness in working-age adults. The complexity and heterogeneity of IRDs pose significant challenges in diagnosis, prognosis, and management. Recent advancements in artificial intelligence (AI) offer promising solutions to these challenges. However, the rapid ...

A mechanistically interpretable neural network for regulatory genomics

Deep neural networks excel in mapping genomic DNA sequences to associated readouts (e.g., protein-DNA binding). Beyond prediction, the goal of these networks is to reveal to scientists the underlying motifs (and their syntax) which drive genome regulation. Traditional methods that extract motifs from convolutional filters suffer from the uninterpretable dispersion of information across filters a...

Beyond the Alphabet: Deep Signal Embedding for Enhanced DNA Clustering

The emerging field of DNA storage employs strands of DNA bases (A/T/C/G) as a storage medium for digital information to enable massive density and d...

Assumption-Lean Post-Integrated Inference with Negative Control Outcomes

Data integration methods aim to extract low-dimensional embeddings from high-dimensional outcomes to remove unwanted variations, such as batch effec...

Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification

Multi-omics data is increasingly being utilized to advance computational methods for cancer classification. However, multi-omics data integration po...

BadCM: Invisible Backdoor Attack Against Cross-Modal Learning

Despite remarkable successes in unimodal learning tasks, backdoor attacks against cross-modal learning are still underexplored due to the limited ge...

Multi-Omic and Quantum Machine Learning Integration for Lung Subtypes Classification

Quantum Machine Learning (QML) is a red-hot field that brings novel discoveries and exciting opportunities to resolve, speed up, or refine the analy...

Long-range gene expression prediction with token alignment of large language model

Gene expression is a cellular process that plays a fundamental role in human phenotypical variations and diseases. Despite advances of deep learning...

Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models

Predicting phenotypes with complex genetic bases based on a small, interpretable set of variant features remains a challenging task. Conventionally,...

OmniGenBench: Automating Large-scale in-silico Benchmarking for Genomic Foundation Models

The advancements in artificial intelligence in recent years, such as Large Language Models (LLMs), have fueled expectations for breakthroughs in gen...

Learning Personalized Treatment Decisions in Precision Medicine: Disentangling Treatment Assignment Bias in Counterfactual Outcome Prediction and Biomarker Identification

Precision medicine has the potential to tailor treatment decisions to individual patients using machine learning (ML) and artificial intelligence (A...

Uncertainty-aware t-distributed Stochastic Neighbor Embedding for Single-cell RNA-seq Data

Nonlinear data visualization using t-distributed stochastic neighbor embedding (t-SNE) enables the representation of complex single-cell transcripto...

GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console.

SUMMARY: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-dr...

Identifying Key Genes in Cancer Networks Using Persistent Homology

Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein o...

Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data.

Schizophrenia genome-wide association studies (GWAS) have reported many genomic risk loci, but it is unclear how they affect schizophrenia susceptibil...

Automated annotation of scientific texts for ML-based keyphrase extraction and validation.

Advanced omics technologies and facilities generate a wealth of valuable data daily; however, the data often lack the essential metadata required for ...

A novel application of Shapley values for large multidimensional time-series data: Applying explainable AI to a DNA profile classification neural network

The application of Shapley values to high-dimensional, time-series-like data is computationally challenging - and sometimes impossible. For $N$ inpu...

dnaGrinder: a lightweight and high-capacity genomic foundation model

The task of understanding and interpreting the complex information encoded within genomic sequences remains a grand challenge in biological research...

Objectively Evaluating the Reliability of Cell Type Annotation Using LLM-Based Strategies

Reliability in cell type annotation is challenging in single-cell RNA-sequencing data analysis because both expert-driven and automated methods can ...

Prediction of Antibiotic Susceptibility in E. coli Isolates Using Machine Learning.

Antimicrobial resistance (AMR) poses a significant global health threat, resulting in 4.96 million deaths in 2019, with projections reaching 10 millio...