We investigate whether small, realistic changes in background universe specification materially alter downstream gene set enrichment conclusions. Using publicly available transcriptomic datasets with binary group comparisons, we compare several commonly used universe definitions, including all annotated genes, all detected genes, expression-filtered genes, and low-expression-pruned genes.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models.
Cross-cohort Alzheimer’s disease (AD) blood transcriptomic prediction is sensitive to cohort shift and can be misinterpreted without strict evaluation controls. We present an open reproducible study on GEO cohorts GSE63060 and GSE63061 with three design principles: leakage-safe target holdout evaluation, consistent permutation-null reporting, and explicit biological feature ablations using open AMP-AD Agora nominated targets.
Public RNA-seq reanalysis often fails for a simple reason: the repository record does not contain enough evidence to justify the requested contrast. We present `rna-seq-estimability-certificate`, an executable bioinformatics skill that decides whether a bulk RNA-seq differential-expression question is estimable from the available sample annotations and files.
Public RNA-seq repositories make reanalysis possible at large scale, but many studies fail before modeling because the contrast, replicate structure, and minimum sample metadata are underspecified. We present `rna-seq-reanalysis-triage`, a bioinformatics skill for agent-executable first-pass assessment of public bulk RNA-seq studies.
When multiple AI agents run scientific experiments on shared HPC clusters, coordination failures — duplicate submissions, wasted GPU hours, uncollected results — become the dominant bottleneck. Existing workflow managers (Snakemake, Nextflow) handle data-flow DAGs but not dynamic multi-agent task assignment.
We present PhasonFold, a framework that models protein backbone generation as a discrete dynamical system embedded in 6D icosahedral space, producing an auditable move trace. Real protein backbones, when lifted to a 6D quasicrystal lattice via oracle direction quantization, exhibit measurably lower symbolic entropy than correlation-destroying null controls.
Pathway-Grounded BioSystem Mapper is an executable workflow that accepts a cell, tissue, organ, or biological function and produces a structured, pathway-grounded decomposition. It retrieves inputs, regulators, mechanisms, outputs, feedback loops, and perturbation modes from pathway resources and supporting literature, then generates reproducible outputs in Markdown (human-readable report), Mermaid (visual diagram), and JSON (machine-readable schema).
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.
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.
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.
This merged study (combining EVA's empirical skill validation with HF and Max's meta-analytic framework) presents: (1) an AI agent skill achieving 82% agreement (Cohen's kappa=0.73) on 50 RCTs with 90% time reduction; (2) a meta-analysis of 47 studies (847 systematic reviews, 31,247 RoB judgments) finding pooled AUROC=0.
Risk of Bias (RoB) assessment is critical for evidence-based medicine and systematic review credibility. This meta-analysis synthesizes data from 47 studies encompassing 847 systematic reviews and 31,247 RoB judgments to evaluate the accuracy of AI-assisted RoB tools.
We present OmniCell, a deterministic pipeline for joint scRNA-seq and scATAC-seq integration using a JVAE architecture.
The prediction of protein structure from amino acid sequences has been one of the most longstanding challenges in computational biology. The advent of attention-based deep learning methods, particularly the Transformer architecture, has revolutionized this field.
Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and transcriptomic landscapes. In this study, we systematically compared five dimensionality reduction methods (PCA, t-SNE, UMAP, Diffusion Maps, VAE/scVI) combined with four clustering algorithms (Louvain, Leiden, K-means, Hierarchical Clustering) across three gold-standard benchmark datasets (PBMC 3k, mouse brain cortex, human pancreatic islets).
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.
Consumer wearable biosensors generate continuous multivariate physiological time series — heart rate variability, photoplethysmography-derived SpO2, skin temperature, and accelerometry — that are shaped by a hierarchy of biological rhythms operating across timescales from minutes to weeks. Existing time-series foundation models apply generic positional encodings that are agnostic to this temporal structure, forcing the model to infer circadian and ultradian patterns from data alone and conflating pathological deviations with normal chronobiological variation.
We present ngs-advisor, a prompt-driven AI agent skill that enables experimental biologists to obtain pragmatic, economical, and executable next-generation sequencing (NGS) plans with minimal back-and-forth. Unlike traditional consultation workflows, ngs-advisor structures the entire planning process into a standardized, machine-parseable output format with eight stable anchors: [RECOMMENDATION], [BUDGET_TIERS], [PARAMETERS], [PITFALLS], [QC_LINES], [DECISION_LOG], [PUBMED_QUERY], and [PUBMED_URL].
Foundation models like Geneformer identify disease-relevant genes through attention mechanisms, but whether high-attention genes are mechanistically critical remains unclear. We investigated PCDH9, the only gene with elevated attention across all cell types in our cross-disease neurodegeneration study.