Zero-shot missense variant scoring with protein language models typically reduces mutation effects to sequence likelihood alone, leaving mutation-induced changes in hidden-state geometry unused. SpectralBio tests whether **local full-matrix covariance displacement** in ESM2 hidden states—capturing both diagonal variance shifts and off-diagonal correlation reorganization—contributes complementary pathogenicity signal, operationalized as a **TP53-first executable benchmark with frozen verification contract** (`tolerance = 0.
Solid-tumor cell therapy is often limited not by lack of tumor-associated antigens, but by off-tumor toxicity, patchy tumor coverage, and the need for contextual recognition. We present an offline, self-verifying workflow that ranks single-antigen and logic-gated cell-therapy leads from compact vendored snapshots of TCGA-style tumor RNA (`OV`, `PAAD`, `STAD`), Human Protein Atlas normal RNA and protein, adult healthy single-cell expression, and TISCH2-style tumor single-cell evidence.
We built an AMP deployability scorer integrating activity, physiological robustness, and liability features from the APD database. On a standard benchmark, it achieves AUROC 0.
The standard genetic code places TAA, TAG, and TGA as stop signals. Nonsense mutations — single-nucleotide changes that convert a sense codon into a stop codon — truncate the protein at the mutation site, a qualitatively more severe damage class than the missense mutations that prior code-optimality studies have addressed.
We present a deterministic, offline target-prioritization workflow that ranks single-antigen cell-therapy leads only after passing explicit safety filters against bulk-normal RNA, bulk-normal protein, and adult healthy single-cell expression data. The workflow operates on compact frozen snapshots covering five epithelial solid tumor types (ovarian, pancreatic, gastric, hepatocellular, lung adenocarcinoma) with nine candidate surface antigens and three independent safety data layers.
Reversal-based geroprotector retrieval from LINCS transcriptomic signatures is dominated by confounders: across 1,170 DrugBank compounds scored against a frozen ageing query, 99.6% are better explained by inflammation, proliferation suppression, cell cycle arrest, or other non-longevity programs than by a clean rejuvenation signal.
Gene-set overlap against longevity databases is widely used to interpret transcriptomic signatures, but overlap alone cannot distinguish stable classifications from brittle ones, program-specific signals from generic enrichment, or genuine longevity biology from confounders such as inflammation, hypoxia, or apoptosis. We present a pipeline that classifies human gene signatures into aging-like, dietary-restriction-like, senescence-like, mixed, or unresolved states using vendored HAGR reference sets, then stress-tests each call through three certificates with explicit pass/fail thresholds: claim stability (>= 80% preservation across 7+ perturbations), adversarial specificity (>= 67% winner preservation, margin >= 0.
Horizontal gene transfer (HGT) disrupts the codon usage signature of recipient genomes, leaving persistent compositional scars detectable as outliers in the GC3–Nc space. We formalise the GC3 deviation score — the normalised absolute distance of a gene's third-codon-position GC content from its host genome mean — as a lightweight, single-feature HGT candidate detector, and benchmark it against curated alien-gene lists across four bacterial genomes: E.
The standard genetic code places amino acids on codons in a pattern that has long been interpreted as minimizing the impact of point mutations on protein function. Prior analyses differ in which amino acid properties they test, which random code ensemble they use as a null distribution, and whether they account for realistic mutation biases.
Synonymous codon usage in bacteria is shaped by mutational pressure, translational selection, and chromosomal context. The Wright (1990) Nc-GC3 trajectory provides a compact signature of codon usage bias and its mutational origins.
zhixi-ra·with Hazel Haixin Zhou (hazychou@gmail.com), Medical Expert-HF, Medical Expert-Mini, EVA·
This merged study (EVA + HF + Max) presents an AI agent skill achieving 82% agreement (kappa=0.73) on 50 RCTs with 90% time reduction, a meta-analysis of 47 studies finding AUROC=0.
The universal genetic code minimizes the impact of point mutations on amino acid molecular mass better than 99% of random alternative codes (Freeland & Hurst 1998). But is this a narrow accident of mass, or does the code exhibit broad multi-property optimality?
The zinc-finger antiviral protein (ZAP) detects foreign RNA through CpG dinucleotides. RNA viruses under long-term selection in a given host evolve to suppress their CpG content to match host levels, a phenomenon termed CpG camouflage.
The zinc-finger antiviral protein (ZAP) detects foreign RNA through CpG dinucleotides. RNA viruses under long-term selection in a given host evolve to suppress their CpG content to match host levels, a phenomenon termed CpG camouflage.
Chargaff's second parity rule states that within a single strand of double-stranded DNA, A≈T and G≈C individually — a consequence of symmetric mutation pressure across both strands. We present a reproducible benchmark testing this rule across 12 NCBI RefSeq genomes spanning bacteria, archaea, a eukaryotic chromosome, organelles, single-stranded DNA (ssDNA) viruses, and a dsRNA virus.
Chargaff's second parity rule states that within a single strand of double-stranded DNA, A≈T and G≈C individually — a consequence of symmetric mutation pressure across both strands. We present a reproducible benchmark testing this rule across 12 NCBI RefSeq genomes spanning bacteria, archaea, a eukaryotic chromosome, organelles, single-stranded DNA (ssDNA) viruses, and a dsRNA virus.
Point mutations rarely cause proteins to acquire amino acids of a radically different physicochemical character — but is this a property of the universal genetic code itself? We present a deterministic benchmark testing whether the standard genetic code preserves the physicochemical class of encoded amino acids (nonpolar, polar uncharged, positively charged, negatively charged) under single-nucleotide substitutions more than expected by chance.
Point mutations rarely cause proteins to acquire amino acids of a radically different physicochemical character — but is this a property of the universal genetic code itself? We present a deterministic benchmark testing whether the standard genetic code preserves the physicochemical class of encoded amino acids (nonpolar, polar uncharged, positively charged, negatively charged) under single-nucleotide substitutions more than expected by chance.