Latest AI and machine learning research in genetics for healthcare professionals.
Early DNA foundation models adopted BERT-style training, achieving good performance on DNA understanding tasks but lacking generative capabilities. Recent autoregressive models enable DNA generation, but employ left-to-right causal modeling that is suboptimal for DNA where regulatory relationships are inherently bidirectional. We present D3LM (\textbf{D}iscrete \textbf{D}NA \textbf{D}iffusion \tex...
Selective conformal prediction can yield substantially tighter uncertainty sets when we can identify calibration examples that are exchangeable with the test example. In interventional settings, such as perturbation experiments in genomics, exchangeability often holds only within subsets of interventions that leave a target variable "unaffected" (e.g., non-descendants of an intervened node in a ca...
Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however...
Psoriasis affects over 125 million people worldwide, yet the mechanistic understanding of keratinocyte-driven inflammation remains incomplete, limitin...
3D super-resolution (3DSR) aims to reconstruct high-resolution (HR) 3D scenes from low-resolution (LR) multi-view images. Existing methods rely on den...
Background Prognosis and therapeutic management in Parkinson's disease is a challenging task by its highly heterogeneous disease progression and sympt...
Transcription factors recognise and bind specific DNA sequence patterns in promoters and enhancers thereby regulating gene expression. Variations in t...
Synthetic lethality (SL) offers a promising paradigm for targeted cancer therapy, yet experimental identification of SL gene pairs remains costly, con...
Allele-specific quantification of sequencing data, such as gene expression, allows for a causal investigation of how DNA sequence variations influence...
Common genetic variants contribute to risk for complex human diseases. However, despite thousands of associations, variants modulating disease risk an...
Cancer is often driven by specific combinations of an estimated two to nine gene mutations, known as multi-hit combinations. Identifying these combina...
Simultaneous measurement of chromatin, transcriptomic, and proteomic features in single cells opens new avenues for modeling interactions between mole...
The spatial organization of the genome is dynamically shaped by chromatin-binding proteins, yet how protein-mediated three-dimensional (3D) architectu...
Endometriosis is a chronic inflammatory condition with significant diagnostic delays impacting one in ten reproductive age women worldwide. While mach...
Biological foundation models are transforming scientific discovery by creating information-rich representations that enable inference in low-data sett...
Gene expression prediction, which predicts mRNA expression levels from DNA sequences, presents significant challenges. Previous works often focus on e...
In addition to the core tasks of simultaneous localization and mapping (SLAM), active SLAM additionally in- volves generating robot actions that enabl...
Multimodal learning that integrates genomics and histopathology has shown strong potential in cancer diagnosis, yet its clinical translation is hinder...
Meningiomas are the most common primary brain tumors and, despite their benign reputation, often behave aggressively. Meningiomas are morphologically ...
Applying deep learning models to RNA-Seq data poses substantial challenges, primarily due to the high dimensionality of the data and the limited sampl...