Cardiovascular

Transcript architecture predetermines m6A remodeling and sensory neuron vulnerability in chemotherapy-induced peripheral neuropathy

Whether individual transcripts carry intrinsic features that predetermine their response to external perturbations is unknown. Here we used nanopore direct RNA sequencing of male mouse dorsal root ganglia (DRG) to simultaneously profile N6-methyladenosine (m6A) modifications, poly(A) tail dynamics, and full-length isoform identity on individual RNA molecules from mice treated with bortezomib, a pr...

Predicting Response to Neoadjuvant Chemotherapy in Ovarian Cancer from CT Baseline Using Multi-Loss Deep Learning

Ovarian cancer is the most lethal gynecologic malignancy: around 60% of patients are diagnosed at an advanced stage, with an associated 5-year survival rate of about 30%. Early identification of non-responders to neoadjuvant chemotherapy remains a key unmet need, as it could prevent ineffective therapy and avoid delays in optimal surgical management. This work proposes a non-invasive deep learning...

Feature Dimensionality Outweighs Model Complexity in Breast Cancer Subtype Classification Using TCGA-BRCA Gene Expression Data

Accurate classification of breast cancer subtypes from gene expression data is critical for diagnosis and treatment selection. However, such datasets ...

BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation

We introduce a new strategy for compositional neural surrogates for radiation-matter interactions, a key task spanning domains from particle physics t...

Metastatic Site Prediction in Breast Cancer using Kirchhoff's Law and Omics Knowledge Graph

Predicting the anatomical site of metastasis from a primary tumour remains an unsolved problem in breast cancer (BRCA) and metastatic disease more bro...

Integrated Multi-Omics Analysis for the Identification of Disease-Associated Variations and Prognostic Biomarkers in Triple-Negative Breast Cancer (TNBC)

Background: Triple-negative breast cancer (TNBC) exhibits substantial molecular heterogeneity and lacks targeted receptor therapies. Single-omic appro...

Mechanistic learning to predict and understand minimal residual disease

Mechanistic modeling has long been used as a tool to describe the dynamics of biological systems, especially cancer in response to treatment. Their ke...

Recurrent Deep Reinforcement Learning for Chemotherapy Control under Partial Observability

Chemotherapy dose optimization can be formulated as a dynamic treatment regime, requiring sequential decisions under uncertainty that must balance tum...

Virtual Scanning for NSCLC Histology: Investigating the Discriminatory Power of Synthetic PET

Accurate histological differentiation between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) is critical for personalized treatment in non-sma...

Early prediction of skeletal muscle loss using longitudinal clinical data in patients with gastric cancer after radical gastrectomy and adjuvant chemotherapy: a retrospective cohort study

Gastric cancer patients frequently experience skeletal muscle loss during the perioperative and adjuvant treatment period, which has been associated w...

CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification

Motivation: Multi-omics integration can improve cancer subtyping, but modality informativeness and noise vary across cancer types and patients. Existi...

On the predictability of progression-free survival in ovarian cancer from NanoString gene expression data

In the treatment of high grade serous ovarian cancer (HGSC), patients initially diagnosed with unresectable tumors are first treated with neoadjuvant ...

An autonomous LLM agent platform for computational binder design and conjugation-aware prioritization of antibody drug conjugates

Large language model (LLM) agents have automated tool use in chemistry, but orchestrating multi step computational biology workflows spanning structur...

CHRep: Cross-modal Histology Representation and Post-hoc Calibration for Spatial Gene Expression Prediction

Spatial transcriptomics (ST) enables spatially resolved gene profiling but remains expensive and low-throughput, limiting large-cohort studies and rou...

AI Approach for MRI-only Full-Spine Vertebral Segmentation and 3D Reconstruction in Paediatric Scoliosis

MRI is preferred over CT in paediatric imaging because it avoids ionising radiation, but its use in spine deformity assessment is largely limited by t...

SCOPE: Integrating Organoid Screening and Clinical Variables Through Machine Learning for Cancer Trial Outcome Prediction

BackgroundPredicting whether a treatment will demonstrate meaningful clinical benefit before committing to a large-scale trial remains a major unmet n...

Vision Transformers for Preoperative CT-Based Prediction of Histopathologic Chemotherapy Response Score in High-Grade Serous Ovarian Carcinoma

Purpose. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological and spatial heterogeneity and is frequently diagnosed a...

AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer

Extranodal extension (ENE) is an emerging prognostic factor in human papillomavirus (HPV)-associated oropharyngeal cancer (OPC), although it is curren...

HistDiT: A Structure-Aware Latent Conditional Diffusion Model for High-Fidelity Virtual Staining in Histopathology

Immunohistochemistry (IHC) is essential for assessing specific immune biomarkers like Human Epidermal growth-factor Receptor 2 (HER2) in breast cancer...

Cardiovascular Adverse Events After Definitive Chemoradiotherapy for Lung Cancer in an Appalachian Population: Incidence and Machine Learning Based Prediction

Background Cardiovascular adverse events (CVAEs) after chemoradiotherapy (CRT) for lung cancer are major concerns in Appalachia due to high rates of s...