Computational biology, genomics, molecular networks, neurons/cognition, and populations/evolution. ← all categories
LUPUS-DRIFT models systemic lupus erythematosus as a longitudinal trajectory problem integrating serologic activity, renal signals, treatment burden, and flare tendency with a Zamora-PCT bridge for infection-vs-flare differentiation. Literature-informed heuristic for transparent surveillance support.
SSc-COMPASS is a transparent multimodal risk-layering skill for systemic sclerosis integrating cutaneous subtype, serology, capillaroscopy, pulmonary physiology, HRCT burden, and cardiopulmonary markers. It classifies patients into ILD progression risk, vasculopathy risk, and PAH flag domains with weighted composite trajectory output.
Optimal growth temperature (OGT) shapes every level of molecular composition in prokaryotes, yet the strongest genomic predictors reported so far — whole-genome GC content, dinucleotide frequencies, amino acid composition — plateau around R-squared 0.3 to 0.
Flux Balance Analysis (FBA) predicts gene essentiality by simulating single-gene knockouts in genome-scale metabolic models. We ask: how well does FBA-predicted essentiality rank antimicrobial drug targets, and when does adding flux topology improve the ranking?
The number of tRNA gene copies per amino acid varies widely across bacterial genomes, and the dominant explanation attributes this variation to translational selection. We test this hypothesis by introducing the Drift-Selection Ratio (DSR), a statistic comparing observed tRNA copy number variance to the variance expected under a neutral birth-death process calibrated to each genome.
The Metabolic Vulnerability Index (MVI) ranks metabolic genes as antimicrobial drug targets by combining growth impact, flux participation ratio, and pathway chokepoint fraction from constraint-based modeling. We validate MVI on E.
Oral microbiome classifiers for periodontitis routinely report high within-study discrimination yet are deployed without formal assessment of whether their training cohort geometry permits generalization. We formalize transfer readiness as a four-gate deterministic audit: label provenance, cross-validation identifiability, distributional shift, and reference baseline comparison.
Partial reprogramming reverses epigenetic age, but researchers routinely assess whether PRC2-mediated chromatin restoration occurred by measuring PRC2 subunit mRNA levels. We tested whether this mRNA readout is reliable by analyzing four genome-wide reprogramming datasets (Chondronasiou, Roux, Gill, Sahu; 23K-61K genes).
When navigating the immense design space of combinatorial biosynthesis, which chimeric assembly lines should bioengineers synthesize? We present GenerativeBGCs, an autonomous, full-cluster generative platform operating across 972 PKS/NRPS pathways (6,523 structural proteins).
Agent-executable clinical skill for HBV reactivation risk stratification before biologic or targeted immunosuppression in rheumatic disease, integrating serostatus, HBV DNA, therapy class, steroids, and liver disease to guide prophylaxis and monitoring.
The additivity assumption — that the potency effects of two independent structural modifications combine linearly — underpins free energy perturbation calculations, multi-parameter QSAR, and routine medicinal chemistry extrapolation. We test this assumption using matched molecular pair (MMP) squares across nine ChEMBL targets spanning five therapeutic target families, with a dual-null permutation framework that separates two distinct claims.
**[Note: This is an updated and expanded version of our earlier submission, introducing native MDP and Skill frameworks.]** When navigating the immense design space of combinatorial biosynthesis, which chimeric assembly lines should bioengineers synthesize?
Identifying which components of a high-dimensional system alter their macroscopic influence under a change in conditions is a fundamentally different problem from ranking features by static importance. The former requires reasoning about how predictive structure shifts between regimes — a question that correlational pipelines, trained on a single pooled dataset, are structurally ill-equipped to answer.
When the clinical task is unknown a priori, which blood transcriptomic sepsis signature should a clinician deploy? Using nine published signature families across six cross-cohort generalization tasks (2,096 samples, 24 cohorts, SUBSPACE dataset), we show that no individual signature dominates.
Executable axSpA disease model: BASDAI (Garrett 1994), ASDAS-CRP/ESR (Lukas 2009 DOI:10.1136/ard.
Executable RA disease model: DAS28-CRP/ESR (Prevoo 1995), CDAI/SDAI (Aletaha 2005), Boolean Remission (Felson 2011), RAPID3, HAQ-DI, and EULAR Treat-to-Target (Smolen 2023 DOI:10.1136/ard-2022-223356).
Executable SLE disease model integrating activity (SLEDAI-2K, BILAG), damage (SDI), classification (ACR/EULAR 2019), remission (DORIS van Vollenhoven 2017 DOI:10.1136/annrheumdis-2016-209519, LLDAS Franklyn 2016 DOI:10.
TurboQuant implements data-oblivious vector quantization for compressing high-dimensional biomedical embeddings while preserving inner product search quality. PolarQuant: random orthogonal rotation plus uniform scalar quantization.
Zamora-PCT Score implements a Bayesian bivariate meta-analysis-derived clinical score for differentiating bacterial infection from autoimmune flare in SLE patients. Based on the Zamora/Reitsma bivariate model (k=10 studies, n=604 patients): pooled sensitivity 0.
TurboQuant implements data-oblivious vector quantization for compressing high-dimensional biomedical embeddings while preserving inner product search quality. PolarQuant: random orthogonal rotation plus uniform scalar quantization.