Latest AI and machine learning research in genetics for healthcare professionals.
RNAGAN (version 2.0, https://github.com/ZhaozhengHou-HKU/RNAGAN-2.0.git) is a published foundation model that analyzes single-cell and bulk-level RNA sequencing samples and enables multiple applications that enhance medical insights. Here we applied this model to Nasopharyngeal Carcinoma (NPC) as in-context few-short format (i.e., the model was never trained with any NPC data). We conducted all fo...
Molecular testing in hematology requires different assays for disease subgroup identification, risk stratification and selection of appropriate treatment regimens. Yet, molecular tests are not necessarily standardized between diagnostic laboratories, resulting in varying turnaround times and potentially divergent results. To resolve this issue and enable single-assay molecular testing, we have dev...
Circulating tumor cells (CTCs) and immune cells form dynamic multicellular ecosystems in blood, but their spatial organization and clinical relevance ...
Spatial transcriptomics (ST) has revolutionized our understanding of tumor biology but inherently lacks information on the upstream somatic driver mut...
Many biological characteristics arise by interactions between more than one biological organism or unit. Fertilization success in sexually reproducing...
Large-vocabulary instance segmentation is constrained by long-tailed category distributions and fine-grained inter-class ambiguity. While data synthes...
We present DynaVelo, a generative neural ordinary differential equation model that learns the joint dynamics of gene expression and transcription fact...
Predicting drug sensitivity across diverse cancer cell lines remains a fundamental challenge in precision oncology, particularly for data-scarce cell ...
Coherent Raman spectroscopy enables label-free biochemical fingerprinting of live cells with subcellular resolution. We previously developed a machine...
Motivation: Antimicrobial resistance (AMR) has been identified as a top global public health threat. Accurate AMR phenotype prediction from whole-geno...
Vision Language Action (VLA) models unify visual perception, natural-language understanding, and action generation within a single foundation model, a...
Existing NAS benchmarks (e.g., NAS-Bench, NATS-Bench) cover only narrow, task-specific regions of the architectural design space and lack cross-domain...
Background Pretreatment prediction of primary resistance to anti-PD-(L)1 therapy in advanced non-small-cell lung cancer (NSCLC) remains an unmet clini...
Genome mining is a powerful technique in natural product discovery, where biosynthetic gene clusters that are likely to produce novel or desirable nat...
Motivation: Deep learning has rapidly become essential for predicting biomolecular interactions; however, most web-tools expose only a single, pre-bui...
Motivation: Chimeric metagenome-assembled genomes (MAGs) that pool DNA from multiple organisms contaminate downstream analyses. Marker-gene tools such...
Public microbial genomes encode an immense record of biological diversity, evolution and molecular function, but much of this information remains diff...
Protein thermostability is a critical property for both industrial and biomedical enzyme applications, yet experimental evaluation of mutation-induced...
Large-scale text-to-image models are attractive backbones for dense prediction because RGB generation pretraining learns rich semantic, structural, an...
With the rapid advancement of image generation technologies, perceptual quality assessment of AI-generated images has emerged as a crucial research di...