Latest AI and machine learning research in genetics for healthcare professionals.
The development of artificial intelligence (AI) including generative large language models (LLMs) and software like ChatGPT is likely to significantly influence existing workforces. Genetic counseling has been identified as a profession likely impacted by advancements of LLMs in natural language processing tasks. It is important therefore to understand LLMs before using them in practice. We provid...
Given the exponentially growing volumes of genomic data, there are extensive efforts to accelerate genome analysis. We demonstrate a major bottleneck that greatly limits and diminishes the benefits of state-of-the-art genome analysis accelerators: the data preparation bottleneck, where genomic data is stored in compressed form and needs to be decompressed and formatted first before an accelerato...
How DNA-binding proteins locate specific genomic targets remains a central challenge in molecular biology. Traditional protein-centric approaches, w...
Lung cancer is the primary cause of cancer death globally, with non-small cell lung cancer (NSCLC) emerging as its most prevalent subtype. Among NSC...
The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational mod...
MOTIVATION: Spatial transcriptomics (ST) addresses the loss of spatial context in single-cell RNA-sequencing by simultaneously capturing gene expressi...
MOTIVATION: Several machine learning (ML) algorithms dedicated to the detection of healthy and diseased cell types from single-cell RNA sequencing (sc...
With the advance of high-throughput genotyping and sequencing technologies, it becomes feasible to comprehensive evaluate the role of massive geneti...
Nonlinear dynamics system identification is crucial for circuit emulation. Traditional continuous-time domain modeling approaches have limitations i...
Pre-trained conditional diffusion models have demonstrated remarkable potential in image editing. However, they often face challenges with temporal ...
Background: Chromosome karyotype analysis is crucial for diagnosing hereditary diseases, yet detecting structural abnormalities remains challenging....
Motivation: Bulk RNA-Seq is a widely used method for studying gene expression across a variety of contexts. The significance of RNA-Seq studies has ...
We introduce Vision as LoRA (VoRA), a novel paradigm for transforming an LLM into an MLLM. Unlike prevalent MLLM architectures that rely on external...
High Content Screening (HCS) microscopy datasets have transformed the ability to profile cellular responses to genetic and chemical perturbations, e...
Text encoders in diffusion models have rapidly evolved, transitioning from CLIP to T5-XXL. Although this evolution has significantly enhanced the mo...
Multimodal learning combining pathology images and genomic sequences enhances cancer survival analysis but faces clinical implementation barriers du...
With the rapid advancement of synthetic biology, CRISPR-Cas systems have emerged as a powerful tool for gene editing, demonstrating significant potent...
Synthetic biology is an emerging interdisciplinary field at the convergence of biology, engineering, and computer science. It employs a bottom-up appr...
Proteins are the basic building blocks of life. Studying the protein expression mechanism is essential for understanding the cellular organization pri...
High-quality test datasets are crucial for assessing the reliability of Deep Neural Networks (DNNs). Mutation testing evaluates test dataset quality...