Quantitative Biology
Positron emission tomography (PET) imaging is widely used in a number of clinical applications, including cancer and Alzheimer's disease (AD) diagnosis, monitoring of disease development, and treatment effect evaluation. Statistical…
Inspired by key neuroscience principles, deep learning has driven exponential breakthroughs in developing functional models of perception and other cognitive processes. A key to this success has been the implementation of crucial features…
Dengue continues to pose a major global threat, infecting nearly 390 million people annually. Recognizing the pivotal role of vector competence (vc), recent research focuses on mosquito parameters to inform transmission modeling and vector…
The presence of an expanded polyglutamine produces a toxic gain of function in huntingtin. Protein aggregation resulting from this gain of function is likely to be the cause of neuronal death. Two main mechanisms of aggregation have been…
Habitat fragmentation, often driven by human activities, alters ecological landscapes by disrupting connectivity and reshaping species interactions. In such fragmented environments, habitats can be modeled as networks, where individuals…
Social contact patterns are a key input to many infectious disease models. Contact surveys, where participants are asked to provide information on their recent close and casual contacts with others, are one of the standard methods to…
Chimeric Antigen Receptor (CAR) T cell therapy has transformed immunotherapy for resistant cancers, yet it faces major limitations such as lack of persistence, toxicity, exhaustion, and antigen-negative relapse. Enhancing CAR T cells with…
A biofilm is a self-contained community of bacteria that uses signaling molecules called autoinducers (AIs) to coordinate responses through the process of quorum sensing. Biofilms exhibit a dual role that drives interest in both combating…
Medical image labels are often organized by taxonomies (e.g., organ - tissue - subtype), yet standard self-supervised learning (SSL) ignores this structure. We present a hierarchy-preserving contrastive framework that makes the label tree a…
This article presents the general framework of theranostic digital twins (TDTs) in computational nuclear medicine, designed to support clinical decision-making and improve cancer patient prognosis through personalized radiopharmaceutical…
High-dimensional tissue imaging generates highly complex 3D data containing multiple biomarkers, making it challenging to identify biologically relevant regions without an expert user specifying manual labels for regions of interest. We…
Purpose: Computational head models are essential tools for studying the risk of mild traumatic brain injury (mTBI) under different activities and across populations. However, different computational models incorporate varied levels of…
A major challenge for community ecology is using spatio-temporal data to infer parameters of dynamical models without conducting laborious experiments. We present a novel framework from statistical physics -- Maximum Caliber -- to…
Understanding the intertwined contributions of amino acid sequence and spatial structure is essential to explain protein behaviour. Here, we introduce INFUSSE (Integrated Network Framework Unifying Structure and Sequence Embeddings), a deep…
Estimating species and gene trees from sequence data is challenging. Gene tree estimation is often hampered by low phylogenetic signal in alignments, leading to inaccurate trees. Species tree estimation is complicated by incomplete lineage…
Bacteria develop resistance to antibiotics through various mechanisms, with the specific mechanism depending on the drug-bacteria pair. It remains unclear, however, which resistance mechanism best supports favorable treatment outcomes,…
This paper critically examines the conceptual and methodological limitations underlying the current understanding and application of VO2max in exercise science and physical activity prescription. Despite the establishment of WHO guidelines…
Recent evidence suggests that beta-band activity plays a key role in decision-making. Here we review our recent work in humans and non-human primates showing that beta-band frequency shifts in frontal cortex signal categorical decision…
Cortical circuits exhibit high levels of response diversity, even across apparently uniform neuronal populations. While emerging data-driven approaches exploit this heterogeneity to infer effective models of cortical circuit computation…
Precise, generalizable subject-agnostic seizure prediction (SASP) remains a fundamental challenge due to the intrinsic complexity and significant spectral variability of electrophysiological signals across individuals and recording…