COVID-19

Leveraging machine learning and accelerometry to classify animal behaviours with uncertainty

Animal-worn sensors have revolutionised the study of animal behaviour and ecology. Accelerometers, which measure changes in acceleration across planes of movement, are increasingly being used in conjunction with machine learning models to classify animal behaviours across taxa and research questions. However, the widespread adoption of these methods faces challenges from imbalanced training data, ...

Generating Synthetic MR Perfusion Maps from DWI and FLAIR in Acute Ischemic Stroke using Deep Learning

Magnetic resonance imaging (MRI) is critical for acute stroke triage, but time-consuming, and often requires contrast injection for perfusion imaging. This study aimed to synthesize T-map perfusion maps from routinely available, non-contrast DWI and FLAIR sequences by means of deep generative models. We hypothesized that relevant perfusion information could be inferred from these modalities that w...

Deep Learning-Based Genetic Perturbation Models Do Outperform Uninformative Baselines on Well-Calibrated Metrics

Single cell genetic perturbation modeling involves predicting the effects of unobserved genetic manipulations, enabling scalable in silico screens for...

Benchmarking generative AI tools for literature retrieval and summarization in genomic variant interpretation

Generative AI is increasingly used to extract structured information across domains, but its reliability in academic and clinical research, where prec...

Adaptive disorder as the hallmark of nanobodies antigen-binding loops

Nanobodies are antigen-binding proteins of great interest as diagnostics and therapeutics. Accurate and fast characterization of their complementarity...

A comprehensive functional landscape of α-tubulin TUBA1A variants illuminates microtubule biology and refines clinical classification

Missense variant interpretation in highly conserved, paralog-rich gene families remains a critical bottleneck for precision medicine. Here, we develop...

DeepEmbCas9: Cas9 coevolution and sgRNA structural information for CRISPR-Cas9 cleavage activity prediction

The development of CRISPR-Cas9 cleavage activity prediction tools hinges on data produced from high-throughput guide-target lentiviral library screens...

Circadian and Sleep-Wake Modulation of Functional Connectivity Across Brain Oscillations and States Linked to Cognition in Humans

Sleep and circadian rhythms both contribute to cognitive performance, but the underlying neuronal network-level changes remain unclear. We quantified ...

Multivariate analysis of glycogenes reveals coordinated regulation of immunoglobulin glycosylation in an immortalized human B cell system

While neutralizing ability has traditionally been considered the most important antibody function, appreciation has grown for Fc-mediated ‘extra-neutr...

The Impact of Stability Considerations on Genetic Fine-mapping

Fine-mapping methods, which aim to identify genetic variants responsible for complex traits following genetic association studies, typically assume th...

Feature-Mask-Based Strategies for Subtype-Specific Freezing of Gait Detection using CNNs

Freezing of gait (FOG), a disabling symptom of Parkinson’s disease, varies in manifestations and motion contexts. Its heterogeneity motivates subtype ...

Structure-based Predictions of Conformational B Cell Epitopes by Protein Language Model and Deep Learning

Mapping conformational B-cell epitopes remains a central challenge for antibody discovery: experiments are costly and most computational tools trained...

CIViC MCP: Integrating Large Language Models with the Clinical Interpretations of Variants in Cancer

The Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase provides a community-driven, open-source platform for discussing the biologica...

REM sleep predicts reductions in pathophysiological daytime basal ganglia-cortical circuit activity in Parkinson’s disease

Sleep disturbances have been shown to be intimately and bidirectionally related to disease progression across a wide range of neurodegenerative disord...

RoBep: A Region-Oriented Deep Learning Model for B-Cell Epitope Prediction

Accurate in silico identification of B-cell epitope residues is crucial for antibody design and structure-guided vaccine development. Although recent ...

An AI-driven approach for nanobody affinity maturation

B7-H3 (CD276), an immunoregulatory checkpoint molecule overexpressed in numerous cancers, is a promising therapeutic target. Nanobodies possess unique...

Harnessing Contextual Embeddings: A Deep Learning Framework for Predicting PCR Amplification Using BERT Tokenization

Polymerase Chain Reaction (PCR) is a widely used molecular biology technique to amplify DNA sequences. PCR amplification is affected by factors such a...

Nucleotide context models outperform protein language models for predicting antibody affinity maturation

Antibodies play a crucial role in adaptive immunity. They develop as B cell receptors (BCRs): membrane-bound forms of antibodies that are expressed on...

From bench assays to bedside: a context-embedding transformer predicts monoclonal antibody viscosity, clearance, and regulatory success

Late-stage failures of monoclonal antibody (mAb) programs often reflect developability liabilities, including high-concentration viscosity and rapid c...

A high-throughput platform for biophysical antibody developability assessment to enable AI/ML model training

Antibodies must bind their targets with high affinity and specificity to achieve useful therapeutic activity. They must also possess suitable developa...