Conventional sports anticipation studies have primarily relied on hypothesis-testing paradigms that target predetermined cues. However, such approaches risk overlooking unanticipated sources of predictive information. This study addresses this limita...
Convolutional neural networks (CNNs) provide powerful models of neural sensory encoding, but their complexity makes it difficult to discern computations that support their performance. To address this limitation, we developed a linear-nonlinear subsp...
Metagenomic binning is crucial for reconstructing microbial genomes from metagenomic sequencing samples. However, existing tools struggle in complex communities where short, low-abundance contigs predominate, thereby limiting the recovery of complete...
Triple-negative breast cancer (TNBC) is an aggressive malignancy with limited targeted therapies and variable responses to conventional chemotherapy, influenced by intratumoral heterogeneity and complex tumor microenvironment (TME) interactions. Unde...
Animals actively engage with their environment to gather information, continuously shaping both their sensory input and behavior. Understanding this closed loop between perception and action remains a central challenge in neuroscience. A key example ...
The rational design of high-specificity binders to peptide–HLA (pHLA) complexes remains a major challenge in personalized immunotherapy, particularly for shared neoantigens with single-point mutations. To address this, we have developed an integrated...
As sequencing technology improves, more genomes become available. Most lack annotation, automated methods are error prone, and few genomes are ever manually curated due to time and cost. Protein structure prediction software may provide new angles fo...
When sampling visual information from the environment, humans execute fast sequential saccadic eye movements and yet preserve stability in their visual percept. This ability relies on the continuous integration of external sensory information (e.g., ...
Amyotrophic lateral sclerosis (ALS), a progressive neuromuscular degenerative disease, rapidly impairs communication within years of onset. This loss of communication necessitates assistive technologies to restore interaction and independence. One su...
In this paper, we presents a new machine learning framework called the Metabolic Pathway Graph Neural Network (MP-GNN), aimed at predicting hypercholesterolemia risk based on serum metabolomics data. Many existing analytical frameworks for analyzing ...
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