Lymphoma

Bounds on the computational complexity of neurons due to dendritic morphology

The simple linear threshold units used in many artificial neural networks have a limited computational capacity. Famously, a single unit cannot handle non-linearly separable problems like XOR. In contrast, real neurons exhibit complex morphologies as well as active dendritic integration, suggesting that their computational capacities outperform those of simple linear units. Considering specific fa...

CellFuse Enables Multi-modal Integration of Single-cell and Spatial Proteomics data

Single-cell and spatial proteomic technologies capture complementary biological information, yet no single platform can measure all modalities within the same cell. Most existing integration methods are optimized for transcriptomic data and rely on a large set of shared, strongly linked features, an assumption that often fails for low-dimensional proteomic modalities. We present CellFuse, a deep l...

Mechanistically Informed Machine Learning Links Non-Canonical TCA Cycle Activity to Warburg Metabolism and Hallmarks of Malignancy

Cancer cells undergo extensive metabolic rewiring to support growth, survival, and phenotypic plasticity. A non-canonical variant of the tricarboxylic...

Quantum Convolutional HLA Immunogenic Peptide Prediction (Q-CHIPP): Next-Generation Neoantigen Prediction with Quantum Neural Networks

The immune system is an intricately evolved series of cellular and protein-protein interactions, which defend the body against pathogens and abnormal ...

Design of peptides with non-canonical amino acids using flow matching

The canonical vocabulary of twenty amino acids limits the chemical space available to proteins and peptides. Expanding this vocabulary to hundreds of ...

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 acq...

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...

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...

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...

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...