Latest AI and machine learning research in genetics for healthcare professionals.
Refined detection methods, more detailed tumor characterization, and adequate distinction between different pediatric tumor subtypes are necessary to improve diagnosis and treatment, enable precision medicine, and advance patient prognosis. However, the application of computational approaches to pediatric brain tumors remains limited, largely due to the lack of accessible datasets. To address part...
Foundation models (FMs) are central to digital pathology, encoding histology images into dense embeddings for facilitating diagnostic classification, molecular alteration prediction, and clinical outcome modeling. However, the opacity of these embeddings renders FM-based systems "black boxes," limiting their trustworthiness for clinical translation and utility for scientific discovery. Here, we in...
Alternative isoform usage can alter gene function independently of total gene expression, creating a need to resolve transcript isoforms at single-cel...
The response of individual cells to drug treatment, virus infections or other molecular stimuli is highly heterogeneous and depends on the cell's init...
Binary data factorization is common, but real-valued methods ignore discreteness and yield hard-to-interpret factors. Boolean Matrix Factorization (Bo...
Background. Three disease-modifying therapies (DMTs) for spinal muscular atrophy (SMA) have been approved since 2016, yet many adults remain untreated...
While cycle threshold (Ct) values from quantitative PCR (qPCR) serve as the gold-standard indicators of target abundance, their clinical interpretatio...
Single-cell RNA sequencing provides high-resolution snapshots of cellular states but lacks direct information about transcriptional dynamics. Metaboli...
Multimodal large language models (MLLMs) hold great potential for medicine, as they inherit knowledge from LLM and allow multiple data modalities to b...
Large multimodal language models (LLMs) have emerged as powerful tools for guiding evolutionary search toward interpretable programmatic policies. How...
Scientific reasoning models for biology combine language models with foundation models trained on multimodal biological data, including DNA, RNA, and ...
Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physical worl...
Diffusion models are increasingly used to predict transcriptional responses to perturbations, but whether they improve on simpler generative and repre...
Current RNA codon design methods are limited by inefficient long-sequence processing and poor generalizability, often relying on a decoupled "generate...
As a cornerstone of the central dogma, RNA has both witnessed and actively shaped three billion years of evolution. Over this vast timescale, a remark...
Vision-language-action (VLA) policies typically inherit their vision encoder from upstream VLM releases, but it is unclear whether an encoder choice v...
Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by extensive molecular complexity, profound stromal remodeling, and limited respo...
Testing for distinctness, uniformity, and stability (DUS) is a requirement for plant variety registration and based on phenotypic traits, which is tim...
Recent spatial multi-omics technologies enable the simultaneous in situ profiling of multiple omics modalities on the same tissue section; however, th...
Lossless compression and probabilistic sequence modeling are two faces of the same coin: a model that assigns high probability to a sequence can encod...