COVID-19

Integrating Data Across Oscillatory Power Bands Predicts the Seizure Onset Zone in Focal Epilepsies

Accurate identification of the seizure onset zone (SOZ) using intracranial electroencephalography (iEEG) remains challenging. Although diverse methods have leveraged spectral features to classify patient outcomes, few approaches focus on identifying individual electrodes within the SOZ or integrate a broad spectrum of frequency ranges within a single model. We developed an interpretable machine le...

Deep Coupled Kuramoto Oscillatory Neural Network (DcKONN): A Biologically Inspired Deep Neural Model for EEG Signal Analysis

Deep neural networks applied to signal processing tasks often need specialized architectural mechanisms to capture the temporal history of input signals. Traditional approaches include recurrent loops between layers, gated units, or tapped delay lines. However, biological brains exhibit much richer dynamics, characterized by activity across multiple frequency bands (alpha, beta, gamma, delta) and ...

A Genomic Language Model for Zero-Shot Prediction of Promoter Variant Effects

Disease-associated genetic variants occur extensively in noncoding regions like promoters, but current methods focus primarily on single nucleotide va...

A deep learning approach for rational affinity maturation of anti-VEGF nanobodies

Nanobodies offer several advantages over conventional antibodies due to their lower immunogenicity, enhanced stability, and superior tissue penetratio...

Predicting enhancer-gene links from single-cell multi-omics data by integrating prior Hi-C information

Enhancers play an important role in transcriptional regulation by modulating gene expression from distal genomic locations. Although single-cell ATAC ...

Predictive models of the genetic bases underlying budding yeast fitness in multiple environments

The ability of organisms to adapt and survive depends on the effects of genes and the environment on fitness. However, the multigenic nature of fitnes...

pLM-SAV: A Δ-Embedding Approach for Predicting Pathogenic Single Amino Acid Variants

Predicting whether single amino acid variants (SAVs) in proteins lead to pathogenic outcomes is a critical challenge in molecular biology and precisio...

Developmental Dysregulation of Synaptic and Myelin-Related Genes in Frontal Cortex and Serum Infrared Spectroscopy Signature in the Valproic Acid Model of Autism

Neural circuits emerge during development through dynamic interactions between genetic instructions and environmental cues that shape cell fate, conne...

Structurally Informed Fitness Landscapes for Surveillance of Emerging PRRSV Variants

Antibodies play a central role in neutralizing pathogens through direct interference with viral entry and recruitment of effector immune cells. Howeve...

PubMind: Literature-Based Genetic Variant Extraction and Functional Annotation Using Large Language Models

The rapid growth of biomedical literature has produced extensive functional knowledge on genetic variants, much of which remains buried in unstructure...

Neural Activity Dynamics in Primate Cortex Across Consciousness Levels: Insights from High-Density Neuropixel Recording

This study investigates the anesthesia mechanisms induced by sevoflurane and how it modulates neural activity in the posterior parietal cortex (PPC) a...

Functional MRI signals as fast as 1Hz are coupled to brain states and predict spontaneous neural activity

fMRI signals were traditionally seen as slow and sampled in the order of seconds, but recent technological advances have enabled much faster sampling ...

Generative design of antibody Fc-variants with synthetic and programmable functional profiles

Beyond antigen recognition, antibodies direct diverse immune effector functions through their constant (Fc) domain. While the Fc domain is central to ...

Inference over hidden contexts shapes the geometry of conceptual knowledge for flexible behaviour

Flexible decision-making in uncertain environments requires inferring latent structure and selecting behaviourally relevant information. Here, we test...

Exploring Log-Likelihood Scores for Ranking Antibody Sequence Designs

Generative models trained on antibody sequences and structures have shown great potential in advancing machine learning-assisted antibody engineering ...

Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network

Pyrazinamide is an important first-line antibiotic for treating tuberculosis and resistance is primarily caused by mutations in the pncA gene. Traditi...

A structure and function-based complete mutational map of Human Hemoglobin using AI

Hemoglobin (Hb), a well-characterized protein central to oxygen transport and molecular medicine, serves as a model for studying how sequence variatio...

CNValidatron: Accurate And Efficient Validation of PennCNV Calls Using Computer Vision

Large rare copy number variants (CNVs) are a main source of genetic variation in the genome and are important in both evolution and disease risk. CNVs...

peleke-1: A Suite of Protein Language Models Fine-Tuned for Targeted Antibody Sequence Generation

The discovery of therapeutic antibodies is a traditionally arduous process. Today, the lab-based process of antibody discovery consists of several tim...

An integrated multi-tissue atlas of epigenomic landscapes and regulatory elements in the bovine genome

Deciphering the regulatory syntax of the genome is essential to understand the genetic and molecular architecture of complex traits, as most trait-ass...