Oncology/Hematology

Foundation model reveals the shared organization of transcription and topologically associating domains

The three-dimensional organization of chromatin into topologically associating domains (TADs) may impact gene regulation by bringing distant genes into contact. However, studies of TADs’ function and their influence on transcription have been constrained by ambiguities in TAD boundary definitions and challenges in directly measuring their regulatory effects. We overcome these limitations by develo...

A comprehensive bulk and single-cell transcriptional atlas of pediatric leukemias

Advancements in understanding the molecular factors driving pediatric leukemias have led to an ever-increasing volume and diversity of data being generated. Recent studies are moving beyond DNA-based profiling to incorporate transcriptional data, enhancing the characterization of these cancers and informing clinical decisions. However, many existing datasets focus on specific leukemia subtypes, li...

From Big Data to Small Scales: Machine Learning Enhances Microclimate Model Predictions

1. Microclimates are critical for understanding how organisms interact with their environments, influencing behaviour, physiology, and species distrib...

Using GPT-4 to Automate the Generation of Lay Summaries for Cancer Publications

Cancer research literature is often riddled with technical jargon that is not digestible to the average person. Individuals interested in research stu...

ROSIE-Enabled Spatial Mapping Reveals Architectural Fragmentation and Immune Reprogramming in Lung Adenocarcinoma Evolution

The progression of lung adenocarcinoma (LUAD) from precancerous lesions to invasive carcinoma entails extensive remodeling of tissue architecture and ...

Generative AI Enables Breast Cancer Genomic Subtype Prediction from Histology Images

Breast cancer subtyping is essential for precision oncology, influencing prognosis, treatment selection, and clinical trial design. The Integrative Su...

Redefining the topology of the human bone marrow using augmented spatial transcriptomic analysis

The bone marrow (BM) is the main site of haematopoiesis in adult life. Our understanding of the pathogenesis of BM-derived blood cancers is limited by...

MOTLAB: A Weighted Multi-Omics Transfer Learning Approach to Mitigate Breast Cancer Racial Disparities

Breast cancer (BC) is a leading cause of cancer death among women in United States. Previous studies have indicated that Black American women have dis...

Sampling Function-Related Metastable States of Proteins With DASH

Rational discovery of function-specific protein modulators as well as activity-enhanced engineering proteins underscore the need to identify function-...

Epigenetic profile drives accurate survival prediction in breast cancer via a multi-omics machine learning model

Accurate overall survival (OS) prediction is key for personalized treatment in breast cancer, but mutation burden alone is insufficient. To improve pr...

MetaPaCS: A Novel Meta-Learning Framework for Pancreatic Cancer Subtype Identification

As the third leading cause of cancer related deaths in the United States, pancreatic cancer (PaC) is a highly heterogenous malignancy that can be divi...

TASC: A transcriptome-driven machine learning classifier to explore molecular heterogeneity and relapse-associated programs in T-cell Acute Lymphoblastic Leukemia

T-cell acute lymphoblastic leukemia is a biologically heterogeneous malignancy characterized by diverse transcriptional and genomic alterations. Recen...

OmniCell: Unified Foundation Modeling of Single-Cell and Spatial Transcriptomics for Cellular and Molecular Insights

Single-cell RNA sequencing (scRNA-seq) enables characterization of cellular heterogeneity but lacks spatial context, while Spatially Transcriptomics m...

SigSpace: an LLM-based agent for drug response signature interpretation

Agent systems powered by large language models (LLMs) are increasingly applied in computational biology to automate analysis, integrate data, and acce...

Phenotypic Screening Coupled with AI-Driven Target Deconvolution Identifies α-Terthienyl as a Dual DPP-IV/HSD17β13 Modulator with Efficacy in a Mouse Model of MASLD

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a highly prevalent condition characterized by fat build-up in the liver and ranges...

scE2TM improves single-cell embedding interpretability and reveals cellular perturbation signatures

Single-cell RNA sequencing technologies have revolutionized our understanding of cellular heterogeneity, yet computational methods often struggle to b...

Deep learning inference of universal dormancy pseudotime reveals the cellular targets of anti-cancer therapies

Controlled exit from and re-entry into the cell cycle is essential for multi-cellular life, while aberrant quiescent and senescent cell states have be...

A Generalizable Machine Learning Framework for cfDNA based Early Detection of Hepatocellular Carcinoma: a Feasibility Study with Preclinical Validation

Early detection of hepatocellular carcinoma (HCC) is critical for improving patient outcomes, yet current screening tools lack sensitivity and specifi...

High throughput quantitative tracking of single parasite in Plasmodium falciparum

New systematic profiling of drug effects is in urgent demand due to limitations in existing drug assessment approaches to evaluate comprehensive drug ...

BCL-XL Dependence is a Subtype Agnostic Actionable Feature of Difficult-to-Treat Kidney Cancers

The BCL-XL anti-apoptotic protein is a clear cell Renal Cell Carcinoma (ccRCC) dependency; however, the mechanism of this dependence and its relevance...