To address the limitations of calcium imaging data, we propose a segmentation-agnostic deep learning framework that integrates Quantile-Based Time-Series Network (QTN) representations with convolutional neural networks to classify neuronal dynamics a...
Predicting host phenotypes from shotgun metagenomic data is essential for translating microbiome research into clinical practice. Despite the development of numerous computational tools for this task, researchers often default to traditional machine ...
Acute myeloid leukemia (AML) often enters remission after chemotherapy but frequently relapses due to chemotherapy-resistant leukemic stem cells (LSCs). Relapsed AML remains largely unresponsive to current therapies and carries a poor prognosis. We d...
Type IV secretion systems (T4SS) enable the spread of antibiotic resistance and other virulence factors. In Gram-positive bacteria, T4SSs have long been thought to lack VirB2-like proteins that form conjugative pili and instead rely on adhesins for c...
Cell biology has been booming with foundation models trained on large single-cell RNA-seq databases, but benchmarks and capabilities remain unclear. We propose an additive benchmark across a gymnasium of tasks to discover which features improve perfo...
Microbiome beta diversity analysis relies on distance-based methods including PERMANOVA combined with fixed ecological distance metrics (Bray-Curtis, Euclidean, Jaccard, and UniFrac), which treat all microbial taxa uniformly regardless of their biolo...
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease caused by the loss of motor neurons. Accurate and accessible blood-based diagnosis of neurodegenerative diseases, including ALS, is becoming increasingly critical. It would be o...
Declarative memory deficits represent considerable challenges to adequate functioning and wellbeing in temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE), two of the most common pharmaco-resistant epilepsies. TLE and FLE are impacted differ...
Accurate prediction of ligand-bound protein complexes and ranking them by affinity are central problems in drug discovery. While deep learning co-folding methods can help address these challenges, their evaluation has been hampered by the difficultie...
Accurate prediction of molecular-protein binding affinity (MPBA) is paramount in drug discovery, yet current computational models often lack generalizability, interpretability, and explicit mechanistic understanding. To address these limitations, we ...
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