Lymphoma

Integration of steady-state diffusion MRI with Neural Posterior Estimation (NPE) for post-mortem investigations

Post-mortem diffusion MRI plays a key role in investigative pipelines to characterise tissue microstructure, with long scan times facilitating the acquisition of datasets with improved spatial/angular resolution and reduced artefacts versus in vivo. Diffusion-weighted steady-state free precession (DW-SSFP) has emerged as a powerful technique for post-mortem imaging, achieving high SNR-efficiency a...

Peripheral immune patterns enable robust cross-platform prediction of ALS onset and progression

Amyotrophic lateral sclerosis (ALS) progression rates vary dramatically between patients, yet the basis of this heterogeneity remains elusive, with no prognostic biomarkers existing to guide clinical decisions or stratify patients for therapeutic trials. Here, we identify a network of coordinated immune cell types, which exhibit differential disruption across progression groups. Using mass cytomet...

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...

Virtual Brain Inference (VBI): A flexible and integrative toolkit for efficient probabilistic inference on virtual brain models

Network neuroscience has proven essential for understanding the principles and mechanisms underlying complex brain (dys)function and cognition. In thi...

Evaluation of Deep Learning Algorithms to Predict Multiple Dementia-Related Neuropathologies from Brain MRI, Clinical and Genetic Data

Alzheimer’s disease and related dementias (ADRD) involve overlapping neurodegenerative and vascular pathologies—such as amyloid-β (Aβ), tau, cerebral ...

Regularized Single-cell Imaging Enables Generalizable AI models for Stain-free Cell Viability Screening

Cell viability assays are essential tools in biomedical research and drug development. Artificial intelligence (AI) offers the potential to simplify t...

Distinct contributions of memorability and object recognition to the representational goals of the macaque inferior temporal cortex

The primate inferior temporal (IT) cortex, at the apex of the ventral visual stream, encodes information that supports diverse representational goals—...

Geographical origin and cultivar differentiation of Kava (Piper methysticum) using Artificial Neural Network with FTIR Spectroscopy: A Novel Method

This study presents a novel method for authenticating the geographical origin and cultivar of kava (Piper methysticum) by combining Fourier Transform ...

Quantum Deep Learning Pipeline for Next Generation Network Biology

Module discovery in omics networks is central to interpretation. Classical pipelines capture broad community structure, but exact search for small, co...

Automatic Classification of Circulating Blood Cell Clusters based on Multi-channel Flow Cytometry Imaging

Circulating blood cell clusters (CCCs) containing red blood cells (RBCs), white blood cells (WBCs), and platelets are significant biomarkers linked to...

IRCAS: a novel end-to-end approach to identify, rectify and classify comprehensive alternative splicing events in a transcriptome without genome reference

Alternative splicing (AS) is a fundamental post-transcriptional mechanism that amplifies proteomic diversity and enables adaptive responses across euk...

Prediction of lncRNA-protein interacting pairs using LLM embeddings based on evolutionary information

Interactions of long non-coding RNAs (lncRNAs) with proteins is responsible for numerous cellular processes, including transcriptional regulation, chr...

Disentangling Superpositions: Interpretable Brain Encoding Model with Sparse Concept Atoms

Encoding models based on word embeddings or artificial neural network (ANN) features reliably predict brain responses to naturalistic stimuli but rema...

Beyond one-size-fits-all: single-cell transcriptomic signatures predict drug efficacy and reveal responder subgroups in endometriosis

Endometriosis affects ∼10% of reproductive-age women, yet targeted non-hormonal therapies remain unavailable, and treatment response is highly variabl...

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...

scCotag: Diagonal integration of single-cell multi-omics data via prior-informed co-optimal transport and regularized barycentric mapping

Recent advances in high-throughput single-cell technologies have enabled characterization of cellular states across distinct omics layers, yielding co...

Leveraging Longitudinal Patient-Reported Outcomes Trajectories to Predict Survival in Non-Small-Cell Lung Cancer

Despite their potential, patient-reported outcomes (PROs) are often underutilized in clinical decision-making, especially when improvements in PROs do...

End-to-end prediction of clinical outcomes in head and neck squamous cell carcinoma with foundation model-based multiple instance learning

Foundation models (FMs) show promise in medical AI by learning flexible features from large datasets, potentially surpassing handcrafted radiomics. Ou...

Multimodal Deep Learning Model to Estimate CT-based Body Composition Measures Using Chest radiographs and Clinical Data

Body composition metrics such as visceral fat volume, subcutaneous fat volume and skeletal muscle volume, are important predictors for cardiovascular ...

Fusing Data from CT Deep Learning, CT Radiomics and Peripheral Blood Immune profiles to Diagnose Lung Cancer in Symptomatic Patients

Lung cancer is the leading cause of cancer-related deaths. Diagnosis at late stages is common due to the largely non-specific nature of presenting sym...