Oncology/Hematology

TCUP – An Open Access Tool to Predict Tissue of Origin and Cancer of Unknown Primary (CUP)

Cancer of unknown primary (CUP) remains a major diagnostic hurdle, compromising therapies that depend on accurately identifying tissue of origin. We present TCUP, an ensemble learning framework that combines Contrastive Autoencoders (CAE) and Siamese Neural Networks (SNN) with base classifiers and a meta-learning layer to classify and interpret CUP, adding biological insight through Monte-Carlo ab...

Characterization of metabolic phenotypes in breast cancer through the integration of genome-scale metabolic models and machine learning

The metabolic heterogeneity of breast cancer represents a significant challenge for the identification of biomarkers and therapeutic targets. To address this problem, we integrated genome-scale metabolic models with machine learning algorithms, aiming to characterize the metabolic phenotypes associated with the disease. There were 90 specific metabolic models generated from clinical and gene expre...

Integrating Artificial Intelligence-Driven Digital Pathology and Genomics to Establish Patient-Derived Organoids as a Novel Alternative Model for Drug Response in Head and Neck Cancer

Patient-derived organoids (PDOs) are emerging as advanced 3D ex vivo novel alternative method (NAM) preclinical models, offering significant advantage...

Cytoplasmic dynamics are overlooked in single nuclei RNA-seq but can be rescued by CytoRescue, a generative AI model to recover cytoplasm enriched gene

Single-nucleus RNA sequencing (snRNA-seq) generates single cell data from nuclei. It provides valuable compatibility with frozen or difficult-to-disso...

Integrative network modeling of colorectal cancer reveals diagnostic signatures and therapeutic targets

Emerging evidence suggests that the interplay between multiple signaling pathways and the immune microenvironment influences tumorigenesis in cancers ...

Screening of Cellular Senescence (CS) Related Genes as Biomarkers and Therapeutic Targets for Glioblastoma (GBM) by Integrated Machine Learning (IML)

Glioblastoma (GBM) is an aggressive brain tumor with limited prognostic biomarkers and therapeutic targets. This study applied an integrated machine l...

Reengineering the antigen optimization process for superior neoantigen vaccine design

Identifying effective neoantigen sequences is essential for enhancing anti-tumor immunity. However, the vast sequence space (>109 possible peptides) a...

Conceptualization and Feasibility Testing of a Vibro-Acoustic Solution for Tumor Detection

Accurate detection of tumors is critical for the success of oncologic surgical intervention. Along with any modality for the localization of tumors, s...

ESPWA: a deep learning-enabled tool for precision-based use of endocrine therapy in resource-limited settings

Cancer morbidity disproportionately affects patients in low- and middle-income countries (LMICs), where timely and accurate tumor profiling is often n...

MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells

Existing breast cancer risk models inadequately identify individuals at latent risk, particularly among women without known genetic mutations or famil...

A Computational Pipeline for Physiologically Informed Calibration of Ligand Reaction-Diffusion Models Using High-Throughput Sequencing

All physiological processes fundamentally rely on continuous cellular cross-talk to maintain organization and ensure proper function. Among the variou...

Mechanistically explainable AI model for predicting synergistic cancer therapy combinations

This study introduces a Large Language Model (LLM)-based framework that combines drug combination data with a knowledge graph to predict synergistic o...

ChatMDV: Democratising Bioinformatics Analysis Using Large Language Models

The rapid advancement in single-cell, spatial omics, imaging, and genomic technologies requires robust analytical and visualisation platforms capable ...

Trustworthy Sleep Staging from EEG: Deep Ensembles, MC Dropout, and Predictive Calibration

Reliable sleep stage classification from EEG signals is critical for the development of clinical decision support systems. However, many deep learning...

Cumulative microscopy reveals cellular states in fibroblasts from patients with genetic disorders

Analysis of cellular states and signaling trajectories can provide insights into causes of disease. We developed cumulative microscopy, a method to pe...

Impact of variation in tissue staining and scanning devices on performance of pan-cancer AI models: a study of sarcoma and their mimics

Histopathological analysis is considered the gold standard for the diagnosis and prognostication of cancer. Recent advances in AI, driven by large-sca...

The hidden predictors of human haematopoietic clonal fate

Human haematopoietic stem and progenitor cells (HSPCs) exhibit heterogeneous lineage output, but the molecular programs underlying clonal fate remain ...

Decoding the interconnected splicing patterns of hepatitis B virus and host using large language and deep learning models

Hepatitis B virus (HBV) infection causes approximately one million deaths annually and remains a major driver of hepatocellular carcinoma. Despite its...

Decoding Helicobacter pylori Resistance: Machine Learning–Enhanced Prediction of Antibiotic Susceptibility using Whole-Genome Sequencing

Helicobacter pylori is a significant risk factor for gastric cancer, peptic ulcers, and MALT lymphoma. Rising antibiotic resistance rates complicate t...

Extracellular Vesicle Gene Expression Enables Sensitive Detection of Colorectal Neoplasia

Extracellular vesicles (EVs), including exosomes, are emerging as promising carriers of disease-specific biomarkers due to their molecular cargo refle...