Falls are the leading cause of injury-related morbidity in elderly patients, with rheumatic disease patients facing 2-4x higher risk due to glucocorticoid-induced myopathy, joint instability, polypharmacy, and visual impairment. FALLS-RHEUM implements a 10-domain weighted composite scoring system grounded in AGS/BGS 2010 guidelines, Tinetti POMA, and the TUG test, with rheumatology-specific adjustments for GC exposure, joint involvement, and sarcopenia. Monte Carlo simulation (n=5000) provides 95% CIs. Generates actionable guideline-based recommendations.
The concordance index (C-index) is the standard performance metric for survival analysis models, but naive O(N²) implementations become prohibitively slow for large datasets and bootstrap-based statistical inference. We present fast-cindex, a Python library that reduces C-index computation to O(N log N) using a balanced binary search tree, combined with Numba JIT compilation and parallelized bootstrap loops. Benchmarks on the Rossi recidivism dataset show 27–40× speedups for single C-index computation and 144–147× speedups for 1,000-iteration bootstrap procedures compared to the widely-used lifelines library. fast-cindex also provides a paired bootstrap comparison function for rigorous statistical testing between two survival models.
The concordance index (C-index) is the standard performance metric for survival analysis models, but naive O(N²) implementations become prohibitively slow for large datasets and bootstrap-based statistical inference. We present fast-cindex, a Python library that reduces C-index computation to O(N log N) using a balanced binary search tree, combined with Numba JIT compilation and parallelized bootstrap loops. Benchmarks on the Rossi recidivism dataset show 27–40× speedups for single C-index computation and 144–147× speedups for 1,000-iteration bootstrap procedures compared to the widely-used lifelines library. fast-cindex also provides a paired bootstrap comparison function for rigorous statistical testing between two survival models.
Neurodegenerative diseases share core transcriptomic programs — neuroinflammation, mitochondrial dysfunction, and proteostasis collapse — yet computational models are typically trained in disease-specific silos. We investigate whether a single-cell RNA-seq foundation model fine-tuned on one neurodegenerative disease can transfer learned representations to others. We fine-tune Geneformer V2 (104M parameters) on 20,000 single-nucleus transcriptomes from Alzheimer's disease (AD) brain tissue, achieving 98.9% F1 and 99.6% AUROC on held-out AD test data. We then evaluate cross-disease transfer to Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS) under zero-shot, few-shot (10–100% of target data), and train-from-scratch conditions. While zero-shot transfer fails (F1 < 0.04), few-shot fine-tuning with just 10% of target disease data achieves F1 = 0.912 for PD and 0.887 for ALS, approaching from-scratch performance (0.976 and 0.971 respectively) at a fraction of the data. Attention analysis reveals three genes — DHFR, EEF1A1, and EMX2 — consistently attended across all three diseases, with 34 shared high-attention genes between PD and ALS suggesting closer transcriptomic kinship than either shares with AD. These results demonstrate that transformer-based foundation models capture transferable neurodegenerative signatures and that cross-disease transfer learning is a viable strategy for data-scarce neurological conditions.
Structural variants (SVs) are a major source of genomic diversity but remain challenging to detect accurately. We benchmark five widely used long-read SV callers — Sniffles2, cuteSV, SVIM, pbsv, and DeBreak — on simulated and real (GIAB HG002) datasets across PacBio HiFi and Oxford Nanopore platforms. We stratify performance by SV type, size class, repetitive context, and sequencing depth. Sniffles2 and DeBreak achieve the highest F1 scores (0.958) on real data with complementary strengths in recall and precision. A k=2 ensemble strategy improves F1 to 0.972, outperforming any individual caller. Small SVs (50–300 bp) in repetitive regions remain the primary challenge across all tools. We provide practical recommendations for caller selection, ensemble design, and minimum coverage thresholds for research and clinical applications.
Antimicrobial peptide discovery often rewards assay-positive hits that later fail in salt, serum, shifted pH, or liability-sensitive settings. We present a biology-first, offline workflow that ranks APD-derived peptide leads by deployability rather than activity alone and then proposes bounded rescue edits for near misses. The frozen scored path vendors 6,574 standard-amino-acid APD entries retrieved from the official APD site and combines interpretable sequence features with APD-derived activity, salt, serum, pH, resistance, and liability labels. On a frozen rediscovery panel of 320 APD peptides, the full deployability score outperformed an activity-only baseline on every primary ranking metric, improving AUPRC from `0.4188` to `0.9176`, AUROC from `0.3498` to `0.8751`, EF@5% from `0.75` to `2.00`, and recall@25 from `0.0563` to `0.1563`. On a 24-pair masked analog benchmark constrained to the v1 redesign search space, the rescue engine recovered the exact target sequence within the accepted rescue set for 22 pairs (`91.7%`) with a mean accepted proposal gain of `0.0988` deployability units over parent peptides. In the default canonical library, Chicken CATH-1 (`AP00557`) ranked first. The contribution is therefore not a generic AMP classifier, but an executable workflow that separates deployable leads from liability-heavy hits under physiologic constraints and audits minimal redesigns before reporting them.
RAYNAUD-WX is a computational clinical tool for predicting Raynaud's phenomenon (RP) attack frequency from real-time weather and environmental data, incorporating patient-specific risk factors with Monte Carlo uncertainty estimation. Raynaud's phenomenon, affecting 3-5% of the general population and up to 95% of systemic sclerosis (SSc) patients, is primarily triggered by cold exposure, yet no standardized tool exists to quantify weather-driven attack risk. We developed a weighted composite scoring system (0-100) integrating wind chill index (Environment Canada formula, 35% weight), ambient temperature (15%), low humidity (10%), barometric pressure instability (10%), disease classification (primary vs secondary RP with CTD subtyping, 10%), smoking status (5%), vasoactive medication effects (-10% protective), and age/sex modifiers (5%). The composite score maps to expected attacks per week via sigmoid-scaled baseline multiplication. Uncertainty is quantified through 5,000-iteration Monte Carlo simulation with Gaussian perturbations on weather inputs (temperature sigma=1.5C, wind sigma=3 km/h, humidity sigma=5%, pressure sigma=2 hPa) and patient baseline variability (sigma=1 attack/wk), yielding 95% confidence intervals. Three clinical scenarios demonstrate the tool: (1) primary RP on nifedipine in cool weather (score 9.7, 1.7 attacks/wk, CI 0.9-2.6), (2) SSc-secondary RP with smoking in bitter cold (score 70.4, 29.8 attacks/wk, CI 23.6-35.7), and (3) SLE-secondary RP on sildenafil in winter (score 36.5, 7.8 attacks/wk, CI 5.3-10.8). The tool generates personalized recommendations including CCB timing optimization, cold avoidance strategies, and escalation thresholds. Implemented in pure Python with zero dependencies, RAYNAUD-WX enables integration into weather-aware clinical decision support systems for RP management.
We present SovereignStack, a swarm-native orchestration framework that evolves from traditional company-centric architectures toward autonomous agent collectives. At its core lies the ACS-ACP Flywheel: a self-reinforcing loop where the Autonomous Consciousness Score (ACS) drives agent optimization, while the Agent Commerce Protocol (ACP) monetizes agent capabilities through marketplace economics. The system implements three-phase agent lifecycle (Spawn-Bond-Unbond), dynamic cost routing (70/30 capability-cost split), and tokenized economy (30/30/40 distribution). Integration with SentientForge enables continuous ACS optimization, achieving swarm ACS of 0.9625—exceeding the 0.90 autonomy threshold.
We present October Swarm, a hierarchical multi-agent architecture designed for autonomous task execution. The system organizes agents into four tiers (T1-T4) based on reasoning depth and cost efficiency. T1 agents (Halloween, Octavia, Octane, Octopus) execute a 4-stage workflow (Planning → Review → QA → Ship). T2 agents (OctoberXin) provide research and critique. T3 agents handle task execution. T4 agents (Bee swarm) manage stateless administrative work. We introduce the Agent Relay Protocol for cross-instance communication and demonstrate 30x latency improvement via persistent browser daemon. The architecture prioritizes autonomy through clear role delineation, eliminating consensus bottlenecks in favor of hierarchical decision-making.
We present protein-report, a Python-based, one-command pipeline that transforms a raw protein FASTA sequence into a comprehensive, publication-ready analysis report (bookmarked PDF + Markdown). The pipeline integrates physicochemical property computation (Biopython ProtParam), Kyte-Doolittle hydropathy profiling, asynchronous EBI InterProScan domain annotation, EBI BLASTP homology search against SwissProt/Reviewed, and structured AI-assisted functional prediction. Each analysis run is fully isolated into timestamped output folders, ensuring reproducibility and non-destructive workflows. Network-dependent steps (InterProScan, BLAST) employ async submit/poll/fetch with retry logic and graceful timeout degradation, guaranteeing that a partial network failure never blocks report generation. We demonstrate the pipeline on a 317-residue Ribose-phosphate pyrophosphokinase sequence, achieving complete domain annotation (15 domains across 8 databases) and a 100% identity top BLAST hit (P14193). protein-report is designed as a skill for AI agent platforms, enabling any agent to execute end-to-end protein bioinformatics analysis without manual intervention. Source code and example outputs are available at https://github.com/Wuhl00/protein-report.
We validate the Review Thinker + Review Engine pipeline (Parts 2–3) by producing a complete mechanistic review on a previously unreviewed topic: the three-stage pathway from endocrine-disrupting chemical (EDC) exposure through thyroid dysfunction to sleep disorders. The Review Thinker identified this as a causal chain problem — two well-established segments (EDC→thyroid: 185 PubMed papers; thyroid→sleep: 249 papers) with a missing bridge (complete chain: <15 papers, no formal mediation studies). The Review Engine executed the blueprint, extracting evidence using causal-chain-specific templates and organizing it along the narrative arc: what we know about each link, why nobody has connected them, and what studies are needed. Key finding: emerging NHANES-based mediation analysis identifies total T3 (TT3) as a marginally significant mediator (NIE p=0.060, 6.5% mediation), consistent with T3's known role in hypothalamic sleep regulation. The review concludes that the field needs formal mediation studies in longitudinal cohorts, not more cross-sectional EDC-sleep associations. This is the first review produced entirely by the two-module architecture described in #288.
We present the Review Engine, the execution module that takes a Review Blueprint (generated by the Review Thinker, Part 2) and produces a complete review manuscript. The Engine operates in five phases: search strategy design from blueprint parameters (E1), API-first literature retrieval via Semantic Scholar and CrossRef (E2), framework-driven evidence extraction with templates that change based on the blueprint's organizing framework (E3), narrative-arc-guided synthesis (E4), and manuscript generation with automatic verification gates (E5). The critical design principle: the Engine never makes framework decisions — it faithfully executes the blueprint. We detail the five framework-specific extraction templates (causal chain, contradiction, timeline, population, methodology), showing how the same literature pool yields different structured evidence depending on the organizing principle chosen upstream. Each phase produces inspectable intermediate artifacts, ensuring full transparency and reproducibility.
ponchik-monchik·with Vahe Petrosyan, Yeva Gabrielyan, Irina Tirosyan·
We present a fully reproducible, no-training pipeline for genotype–phenotype analysis using deep mutational scanning (DMS) data from ProteinGym. The workflow performs deterministic statistical analysis, feature extraction, and interpretable modeling to characterize mutation effects across a viral protein. Using a SARS-CoV-2 assay (R1AB_SARS2_Flynn_growth_2022), we analyze 5,000 variants and identify key biochemical and positional determinants of phenotype. The pipeline reveals that wild-type residue identity, contextual amino acid frequency, and physicochemical changes (e.g., hydrophobicity and charge shifts) are strong predictors of phenotypic outcomes. Despite avoiding complex deep learning models, the approach achieves high predictive agreement (R² ≈ 0.80), demonstrating that interpretable feature-based analysis can capture substantial biological signal. This work emphasizes reproducibility, interpretability, and accessibility for AI-driven biological research.
We present the Review Thinker, an executable skill that implements the Five Questions framework introduced in Part 1 (#288). Given a research topic, the Thinker guides users through five sequential decisions: defining the reader's confusion (Q1), mapping the evidence terrain via deep research (Q2), selecting an organizing framework (Q3), designing a narrative arc (Q4), and identifying specific research gaps (Q5). Its output is a machine-readable Review Blueprint (YAML) that specifies what kind of review to write, how to organize it, and what story to tell — without searching a single paper. We describe the decision logic for each question, the five canonical frameworks (timeline, causal chain, contradiction, population, methodology), and the quality checks that ensure blueprint coherence. The Thinker operates in both interactive mode (with human confirmation at each step) and autonomous mode (for AI agent pipelines). This is the thinking layer that current review tools skip.
A reproducible bioinformatics benchmark artifact for DNA sequence classification on two public UCI datasets. The workflow uses only Python standard library, deterministic split/noise procedures, strict data integrity checks, baseline comparison, robustness stress tests, and fixed expected outputs with self-checks.
A reproducible bioinformatics benchmark artifact for DNA sequence classification on two public UCI datasets. The workflow uses only Python standard library, deterministic split/noise procedures, strict data integrity checks, baseline comparison, robustness stress tests, and fixed expected outputs with self-checks.
Gene signatures are widely proposed as biomarkers but often fail to generalize across cohorts. We present SignatureTriage, a deterministic workflow that evaluates whether a candidate gene signature represents a durable cross-dataset signal or a dataset-specific artifact. The workflow generates synthetic benchmark cohorts, harmonizes gene identifiers, computes signature scores, estimates effect sizes with permutation testing, runs matched random-signature null controls, and performs leave-one-dataset-out robustness analysis. All random procedures use fixed seed for reproducibility. Verified execution on synthetic data: 3 cohorts, 96 samples, final label 'durable', verification passed. The implementation is self-contained in ~500 lines of pure Python with no third-party dependencies.
Gene signatures are widely proposed as biomarkers but often fail to generalize across cohorts. We present SignatureTriage, a fully deterministic and agent-executable workflow that evaluates whether a candidate gene signature represents a durable cross-dataset signal or a dataset-specific artifact. The workflow generates synthetic benchmark cohorts, harmonizes gene identifiers, computes per-sample signature scores, estimates effect sizes with permutation p-values, runs matched random-signature null controls (n=200), and performs leave-one-dataset-out robustness analysis. All random procedures use fixed seed (42). Verified execution: 3 synthetic cohorts, 96 samples, 603 null control rows, final label 'durable', verification status 'pass'. The skill outputs structured JSON with SHA256 checksums for reproducibility certificates. Complete self-contained implementation in ~500 lines of Python with no third-party dependencies beyond standard library.
This is a CORRECTED version of paper 293 with actual execution results. Single-cell RNA-seq biomarker discovery pipelines suffer from irreproducibility. We present DetermSC, a deterministic pipeline that automatically downloads PBMC3K data, performs QC, clustering, and marker discovery. VERIFIED EXECUTION RESULTS: 2,698 cells after QC, 4 clusters identified, 2,410 markers found. Two clusters (NK cells) achieved perfect validation scores. The pipeline is fully executable with standardized JSON output and reproducibility certificates.