Oncology/Hematology

A Benchmark of Evo2 Genomic AI Models for Efficient and Practical Deployment

The rapid advancement of DNA foundation language models has brought about a transformative shift in genomics, allowing for the deciphering of intricate patterns and regulatory mechanisms embedded within DNA sequences. The genomic foundation model Evo2 demonstrates remarkable capabilities in decoding DNA functional patterns through cross-species pretraining. However, despite the great potential of ...

CNN-based learning of single-cell transcriptomes reveals a blood-detectable multi-cancer signature of brain metastasis

Brain metastasis (BrM) is a serious complication of advanced cancers and remains difficult to predict before clinical symptoms appear. To investigate shared transcriptional features of BrM across tumour types, we integrated single-cell RNA sequencing (scRNA-seq) data from malignant epithelial cells derived from six carcinoma types, including lung, breast, colorectal, renal, prostate, and melanoma....

Pathologist-interpretable breast cancer subtyping and stratification from AI-inferred nuclear features

Artificial intelligence (AI) is making notable advances in digital pathology but faces challenges in human interpretability. Here we introduce EXPAND ...

FUSED: Cross-Domain Integration of Foundation Models for Cancer Drug Response Prediction

AI-driven methods for predicting drug responses hold promise for advancing personalized cancer therapy, but cancer heterogeneity and the high cost of ...

Phenotypic AI-based design of cell-specific small molecule cytotoxics

Drug discovery is being transformed by artificial intelligence, which enables the exploration of vast chemical spaces and the generation of novel comp...

Adversarial erasing enhanced multiple instance learning (siMILe): Discriminative identification of oligomeric protein structures in single molecule localization microscopy

Single-molecule localization microscopy (SMLM) achieves nanoscale imaging of complex protein structures in the cell. However, the ability to capture s...

Artificial intelligence-enabled automated analysis of transmission electron micrographs to evaluate chemotherapy impact on mitochondrial morphology in triple negative breast cancer

Advancements in transmission electron microscopy (TEM) have enabled in-depth studies of biological specimens, offering new avenues to large-scale imag...

Application and Characterization of the Multiple Instance Learning Framework in Flow Cytometry

For decades, flow cytometry has allowed for single-cell profiling based on selected biomarkers and is widely used in both clinical and research settin...

A simple circuit to sustain intact tumor microenvironments for complex drug interrogations

Deep learning and large language models can integrate complex datasets to uncover biological insights that are often undetectable through conventional...

Integrative Chemical Genetics Platform Identifies Condensate Modulators Linked to Neurological Disorders

Aberrant biomolecular condensates are implicated in multiple incurable neurological disorders, including Amyotrophic Lateral Sclerosis, Frontotemporal...

DeepVul: A Multi-Task Transformer Model for Joint Prediction of Gene Essentiality and Drug Response

Despite their potential, current precision oncology approaches benefit only a small fraction of patients due to their limited focus on actionable geno...

Effects of Spearman’s and Pearson’s correlations on construction of cancer regulatory networks and biomarker selection

Correlation methods play an important role in machine learning. They apply as a similarity or distance measure in machine learning models such as reco...

Cross-Species Insights from ART-D to Uncover Evolutionarily Conserved Oncogenic Mechanisms

Cancer arises from oncogenic clones, yet the dynamic mechanisms governing their stepwise evolution toward malignancy remain incompletely understood. H...

Multiple instance learning with spatial transcriptomics for interpretable patient-level predictions: application in glioblastoma

Accurate prediction of patient outcomes remains a major challenge in oncology. While recent machine learning (ML) approaches often rely on bulk omics ...

Bioimage analysis for multiplexed FUCCI acquisitions powered by deep learning

The FUCCI sensor fluorescently labels cell cycle phases, which is essential to assess normal and abnormal cell-cycle progression in physiological and ...

Single-cell lineage trajectory defines CDK inhibitor-sensitive cells-of-origin in esophageal squamous cell cancer

Understanding the cells of origin is essential for overcoming therapy resistance in esophageal squamous cell carcinoma (ESCC). We utilized machine lea...

Breast Cancer Subtyping with HyperCLSA: A Hypergraph Contrastive Learning Pipeline for Multi-Omics Data Integration

Accurate molecular subtyping of cancer is crucial for advancing personalized medicine. Although multiomics data contain valuable predictive informatio...

DREAMER-S: Deep leaRning-Enabled Attention-based Multiple-instance approaches with Explainable Representations for Spatial biology

Identifying image features that associate strongly with diagnostic or prognostic classes in large-scale, multi-channel spatial imaging is challenging ...

A blueprint for mutation-defined hallmark vulnerabilities across human cancers

Hallmark gene mutations shape cancer cell vulnerabilities and inform drug discovery1–3. A systematic map of hallmark gene mutation-defined cancer depe...

Circulating DNA reveals nucleosome occupancy patterns that are associated with nucleosome-DNA affinity and are affected in cancer

The study of cell-free circulating DNA (cirDNA) fragments (fragmentomics) from liquid biopsies has received increasing attention. By constructing an a...