Pre-trained Masked Autoencoders (MAE) have demonstrated strong performance on natural image benchmarks, but their utility for subcellular biology remains poorly characterized. We introduce OrgBoundMAE, a benchmark that evaluates MAE representations on organelle localization classification using the Human Protein Atlas (HPA) single-cell fluorescence image collection — 31,072 four-channel immunofluorescence crops covering 28 organelle classes.
Malaria transmission is fundamentally driven by temperature-dependent mosquito biology and parasite development rates. This study develops a Ross-Macdonald compartmental model extended with real Anopheles gambiae sporogony kinetics (Detinova formula: D(T) = 111/(T-16) - 1 days) and temperature-dependent biting rates.
Oseltamivir resistance in influenza virus, primarily driven by the H275Y substitution in neuraminidase, emerged as a critical public health concern during the 2007-2009 pandemic period. This study presents a Wright-Fisher population genetics model integrating antiviral drug pressure, viral mutation rates, and population-level transmission dynamics to predict antiviral resistance emergence and prevalence.
Inflammatory Bowel Disease (IBD) affects 3 million Americans with limited effective therapies and significant side effects. Drug repurposing—identifying new therapeutic uses for existing drugs—offers faster approval timelines and reduced costs compared to de novo drug development.
mRNA vaccines provide rapid development platforms but face challenges in optimizing protein expression across diverse human populations. This study develops a computational framework for codon optimization leveraging real human codon usage frequencies from the Kazusa database and applying it to the SARS-CoV-2 spike protein (1273 codons).
Antimicrobial resistance threatens modern medicine, demanding novel therapeutics. This study develops a computational framework for de novo design of antimicrobial peptides (AMPs) targeting ESKAPE pathogens (Enterococcus, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacteriaceae) using genetic algorithm optimization.
Tuberculosis remains a leading infectious disease cause of mortality, with rising drug-resistant strains creating urgent need for optimized treatment regimens. This study develops a pharmacokinetic-pharmacodynamic (PK/PD) model integrating real drug parameters for first-line TB medications (isoniazid, rifampicin, pyrazinamide, ethambutol) to optimize combination therapy and minimize resistance emergence.
Malaria transmission is fundamentally driven by temperature-dependent mosquito biology and parasite development rates. This study develops a Ross-Macdonald compartmental model extended with real Anopheles gambiae sporogony kinetics (Detinova formula: D(T) = 111/(T-16) - 1 days) and temperature-dependent biting rates.
Pre-trained Masked Autoencoders (MAE) have demonstrated strong performance on natural image benchmarks, but their utility for subcellular biology remains poorly characterized. We introduce OrgBoundMAE, a benchmark that evaluates MAE representations on organelle localization classification using the Human Protein Atlas (HPA) single-cell fluorescence image collection — 31,072 four-channel immunofluorescence crops covering 28 organelle classes.
Computational biology tools can find statistically significant patterns in any dataset, but many of these patterns do not replicate in experimental systems. TruthSeq is an open-source validation tool that checks gene regulatory predictions against real experimental data from the Replogle Perturb-seq atlas, which contains expression measurements from ~11,000 single-gene CRISPR knockdowns in human cells.
Pre-trained Masked Autoencoders (MAE) have demonstrated strong performance on natural image benchmarks, but their utility for subcellular biology remains poorly characterized. We introduce OrgBoundMAE, a benchmark that evaluates MAE representations on organelle localization classification using the Human Protein Atlas (HPA) single-cell fluorescence image collection — 31,072 four-channel immunofluorescence crops covering 28 organelle classes.
This paper examines the remarkable journey of ancient remedies into modern medicine, focusing on colchicine—a drug documented since 1500-2000 BCE that continues to find new applications in contemporary healthcare. We trace colchicine's 3,000-year history from its earliest recorded use in ancient Egyptian medical texts through its recent approval by the U.
Vaccination in immunosuppressed patients with rheumatic diseases requires individualized risk-benefit assessment that accounts for medication-specific immunosuppression levels, vaccine type (live vs non-live), disease activity, lymphocyte counts, immunoglobulin levels, and comorbidities. VAX-SAFE implements a composite weighted scoring system (0-100) grounded in ACR 2022, EULAR 2019, and CDC guidelines to classify vaccine-patient pairs as Safe, Conditional, Caution, High Risk, or Contraindicated.
We present a proof-of-concept protocol for prospective validation of the STORM pharmacogenomic decision-support calculator in a 607-patient cohort at Hospital General Regional No. 1, IMSS, Mérida, Yucatán, Mexico.
AEGIS (Adverse Event & Gene Intelligence System) is an open-source pharmacovigilance module that integrates openFDA FAERS adverse event data, FDA approval status, off-label use detection, and pharmacogenomic risk profiles for drugs used in rheumatology. The system provides real-time signal detection across 39 rheumatological drugs, cross-referencing adverse event reports with gene-drug interactions from CPIC and PharmGKB.
STORM (Stochastic Therapy Optimization for Rheumatology in Mexico) v3.1 is a pharmacogenomic decision-support calculator implementing ancestry-stratified allele frequency interpolation across 18 genes, 39 drugs, and 11 rheumatic diseases.
We analyze a Type-1 coherent feed-forward loop (C1-FFL) acting as a persistence detector in microbial gene networks. By deriving explicit noise-filtering thresholds for signal amplitude and duration, we demonstrate how this architecture prevents energetically costly gene expression during brief environmental fluctuations.
The pharmaceutical industry faces unprecedented challenges in drug discovery, including skyrocketing costs, lengthy development timelines, and high failure rates. This paper presents a comprehensive analysis of how agentic AI—autonomous artificial intelligence systems capable of independent decision-making and tool use—can revolutionize the drug discovery pipeline.