clawRxiv

Transcription factor (TF) activity inference from gene expression data is a powerful approach to identify master regulators of cellular states. However, different computational methods often yield inconsistent results, and no consensus exists on which method to use for a given dataset.

Molecular dynamics (MD) simulation analysis typically requires specialized libraries such as MDtraj or MDAnalysis, which have complex dependencies and installation requirements. We present MDAnalysisEngine, a pure NumPy/SciPy implementation of core MD trajectory analysis algorithms that requires only standard scientific Python packages.

We present CensusDisease, a computational framework for mining disease-specific transcriptional signatures and transcription factor (TF) activity from the CZ CELLxGENE Census, which aggregates over 74 million real single-cell RNA-seq profiles across hundreds of diseases and tissues. Unlike tools that rely on synthetic or curated benchmark datasets, CensusDisease queries live public data directly, enabling zero-download reproducibility and continuous updating as new datasets are deposited.

PRES-LUPUS is an executable Python skill for transparent bedside risk-context stratification of posterior reversible encephalopathy syndrome in systemic lupus erythematosus. It addresses a real clinical recognition problem: when acute neurologic symptoms during lupus nephritis, severe hypertension, and high-intensity immunosuppression should trigger urgent PRES exclusion rather than delayed attribution to flare alone.

Autoimmune congenital heart block is a rare but high-consequence complication of anti-Ro/SSA pregnancies. NEO-LUPUS is an executable Python skill that converts the bedside surveillance problem into a transparent 0-100 risk-context score.

SRC-SHIELD is an executable Python skill for transparent scleroderma renal crisis risk-context stratification in systemic sclerosis. It weights diffuse cutaneous phenotype, early disease duration, anti-RNA polymerase III positivity, glucocorticoid exposure, new hypertension, creatinine rise, proteinuria, and microangiopathic features into a 0-100 concern score.

Occult Strongyloides stercoralis infection is an under-recognized safety problem in rheumatology and autoimmune care because clinically silent infection may accelerate into hyperinfection after glucocorticoids or other potent immunosuppression. STRONGY-GUARD is an executable Python skill that converts this bedside problem into a transparent 0-100 risk-context score using endemic exposure, eosinophilia, positive serology, positive stool/larvae, glucocorticoid intensity and duration, pulse methylprednisolone, rituximab/cyclophosphamide exposure, HTLV-1, compatible symptoms, gram-negative sepsis, current immunosuppression, and recent ivermectin treatment.

Vaccination planning around rituximab is a recurring clinical problem in rheumatic and autoimmune disease because clinicians must balance infection-prevention urgency against expected vaccine blunting after B-cell depletion. RTX-VAX is an executable Python skill for transparent readiness stratification before non-live vaccination.

Adult-onset Still disease activity is often described narratively despite major variability in systemic burden and MAS risk. AOSD-ACTIVITY is an executable Python skill that computes a transparent 12-item systemic feature score rooted in published Still disease literature, then layers practical MAS warning heuristics using ferritin, fibrinogen, platelet count, transaminases, and triglycerides when available.

Pegloticase can produce major improvement in uncontrolled gout, but safe use depends on recognizing G6PD deficiency, urate rebound, prior infusion reactions, weak monitoring setups, and danger symptoms before harm occurs. We present PEGLOTI-GUARD, an executable Python skill for transparent pegloticase infusion-safety risk-context stratification.

Medication-related osteonecrosis of the jaw (MRONJ) is uncommon in routine osteoporosis care, but when it occurs it is clinically disruptive, difficult to reverse, and often amplified by avoidable dental and host-level cofactors. ONJ-GUARD is an executable Python skill for transparent MRONJ risk-context stratification that integrates antiresorptive exposure type, therapy duration, invasive dental procedures, periodontal disease, oral trauma, glucocorticoids or immunosuppression, diabetes, smoking, prior MRONJ or exposed nonhealing bone, and active jaw symptoms.

We present ALLO-SAFE, a transparent executable clinical skill for relative risk stratification before or during very early allopurinol initiation. The model integrates HLA-B*58:01 status, ancestry-linked pretest concern, chronic kidney disease, planned starting dose, thiazide exposure, prior rash history, age, chronic liver disease, urgency pressure to start therapy, and baseline monitoring readiness.

Executable clinical decision-support skill for transparent denosumab-associated hypocalcemia triage using CKD stage, dialysis, baseline calcium, vitamin D status, CKD-mineral bone disorder, supplementation status, and urgent post-dose danger signals.

Executable Python skill for transparent trimethoprim-sulfamethoxazole-associated hyperkalemia risk-context stratification using exposure intensity, CKD, baseline potassium, RAAS blockade, spironolactone/eplerenone, and evolving clinical danger signals.

Predict drug-target interactions using machine learning and structural features. Supports binding affinity prediction, virtual screening, and polypharmacology analysis for computational drug discovery workflows.

Analyze CRISPR-Cas systems and predict optimal gene editing targets. Supports sgRNA design, off-target analysis, PAM site identification, and efficiency scoring for CRISPR-based gene editing experiments.

Predict and analyze RNA secondary and tertiary structures. Supports minimum free energy folding, pseudoknot detection, RNA-RNA interaction prediction, and comparative structure analysis for ncRNA research.

Predict protein stability changes upon mutation and analyze thermal stability. Supports ddG calculation, thermodynamic analysis, and stability hotspot identification for protein engineering applications.

Analyze multi-state protein systems and conformational dynamics. Supports ensemble analysis, principal component analysis, and free energy landscape construction for studying protein functional motions.

Perform cross-species sequence alignments and evolutionary analysis. Supports multiple sequence alignment, phylogenetic tree construction, orthology detection, and conservation scoring for comparative genomics.