79 papers
The paper introduces SDSR, a scalable spectral divide-and-conquer algorithm for species tree reconstruction that achieves up to 10-fold faster runtimes compared to standard methods while maintaining comparable accuracy under the multispecies coalescent model.
This paper demonstrates that single cells can rapidly and accurately infer the direction of a chemoattractant source at ultra-low concentrations by leveraging the disproportionately high directional information contained in early, extreme first-passage receptor binding events rather than waiting for steady-state concentration profiles.
This paper presents an agentic framework leveraging large language models and containerized tools to automatically retrieve, extract, and re-analyze omics data from biomedical literature, thereby transforming static publications into a scalable, executable resource for automated data reuse and cross-study discovery.
This paper introduces a general sampling-based continuous optimization framework that iteratively refines parameterized distributions to design messenger RNA sequences, effectively navigating the vast synonymous space to optimize multiple coupled stability and performance objectives outperforming existing methods like LinearDesign and EnsembleDesign.
This study combines in vitro experiments and hybrid computational modeling to reveal how pulsed electric field intensity governs the temporal dynamics of damage-associated molecular pattern release and the dual fate of quiescent cells, ultimately driving either tumor suppression or accelerated regrowth in multicellular tumor spheroids.
This paper proposes an unsupervised medical anomaly detection method that augments CNN autoencoder latent representations with in-batch relational features via hypergraph estimation and graph convolution, significantly improving the separation of healthy anatomical variations from pathologies and reducing false positives on a heterogeneous brain tumor dataset.
This paper critiques conventional aDNA methods for oversimplifying molecular origins and introduces the HSF posterior traceability framework, which utilizes first-principles analysis and a four-system classification to improve authenticity evaluation and reduce misassignment in complex, mixed-signal fossil samples.
The paper introduces HIDDENdb, a freely accessible web-based database that integrates large-scale perturbation screens, multi-omics data, and curated repositories to map and visualize genetic and protein co-dependency relationships, revealing functional modules and potential structural interactions across diverse biological contexts.
This paper proposes a time-varying lambda-omega dynamical systems framework to model the gradual, asymmetrical metabolic adaptations that drive obesity development and complicate weight loss, offering a more nuanced understanding of metabolic state transitions than traditional "calories in, calories out" perspectives.
This paper proposes a machine and deep learning pipeline that classifies individuals based on genotype data and utilizes feature importance to identify phenotype-associated genes, demonstrating that SNPs selected by high-performing models effectively prioritize disease-related genetic markers with a mean gene identification ratio of 0.84.
This study benchmarks the performance of 29 machine learning algorithms, 80 deep learning models, and 3 polygenic risk score tools across 80 binary phenotypes from the openSNP dataset, revealing that machine learning approaches outperformed traditional tools for 44 phenotypes while polygenic risk scores were superior for the remaining 36.
GWASPoker is a Python-based tool that streamlines polygenic risk score calculation by efficiently triaging GWAS Catalog files through partial downloads and header parsing to identify suitable datasets without requiring full file transfers.
This study develops a network-based model to overcome the limitations of traditional homogeneous mixing assumptions in studying Frogeye Leaf Spot, revealing that early targeted roguing is the most effective management strategy while tillage practices showed no significant impact on disease spread.
This study demonstrates that while vaccination alone is ineffective against Foot-and-Mouth Disease in Mato Grosso do Sul, Brazil, combining high depopulation capacity with limited vaccination is the optimal strategy, capable of controlling 100% of simulated outbreaks within 10 to 15 days.
This paper proposes a control-theoretic model of biological aging that decomposes damage into regulatable and information-limited classes, demonstrating that sustained healthspan requires endogenous repair to exceed regulatable damage production while engineered interventions must actively bound information-limited damage, a finding supported by sensitivity analyses showing the latter's dominance in driving asymptotic aging rates.
This paper employs Turing instability and weakly nonlinear analyses, supported by numerical simulations, to investigate how immune cell squeezing probability and chemotactic response influence the formation of spatial patterns in a reaction-diffusion model of Multiple Sclerosis demyelinating lesions.
This paper derives sharp upper and lower bounds for the basic reproduction number () and final epidemic sizes in multitype SIR models where the next-generation matrix is only partially known through row or column sums, providing complete results for general matrices and partial results for matrices satisfying detailed balance.
The paper introduces DeeDeeExperiment, a new S4 class within the Bioconductor ecosystem that extends SingleCellExperiment to provide a standardized, reproducible infrastructure for storing, managing, and contextualizing differential expression and functional enrichment analysis results alongside their metadata.
The paper introduces ScNucAdapt, a partial domain adaptation method that enables robust and accurate cross-domain cell type annotation between scRNA-seq and snRNA-seq datasets, outperforming existing approaches by addressing distributional and compositional differences.
This study employs mathematical modeling to demonstrate that viral infectivity and dynamics are often more critical than tumor motility in determining the success of oncolytic virotherapy, offering new insights into infection wave propagation and therapeutic outcomes.