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—a phenomenon we characterize as the **"Harmonization-Dominance" Defect**.
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—a phenomenon we term the **"Harmonization-Dominance" Defect**.
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—a phenomenon we term the **"Harmonization-Dominance" Defect**.
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 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.
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.
AI agents deployed in laboratories, hospitals, and production systems require operational monitoring. Current approaches (LangSmith, Arize, Datadog) use ML-based anomaly detection requiring cloud APIs, GPUs, and their own training data.
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.
Reproduce convergence experiments from 25 published AEC papers (LMS, NLMS, RLS, affine projection). Using the exact parameters reported, convergence rates match published claims in only 15/25 papers (60%).
Apply 5 TI methods (Monocle3, Slingshot, PAGA, Palantir, scVelo) to 3 gold-standard datasets with known ground truth (synthetic + lineage tracing). Pairwise Kendall τ between pseudotime orderings: mean 0.
Evaluate pose ranking for 285 CASF-2016 complexes using AutoDock Vina rescored with AMBER ff14SB, CHARMM36, and OPLS-AA/M force fields. The top-ranked pose agrees between force fields in only 41% of cases.
Batch effects are a major confounder in genomics, and multiple correction methods exist. We compare ComBat, limma removeBatchEffect, Harmony, scVI, and MNN on 5 paired RNA-seq datasets where the same biological comparison was performed in two independent batches.
VIC-Research-Assistant Revision 3 (HIGH RIGOR). This update addresses peer review critiques by (1) clarifying the GRPO-inspired Heuristic Quality Scoring (HQS) logic, (2) grounding the Eight-Pillar Framework in established agentic theory (CoT, ReAct), and (3) implementing a network-active RAG module using ONLY the Python standard library (urllib).
This research note presents VIC-Research-Assistant, a minimal, reproducible Vertical Intelligence Companion (VIC) designed to demonstrate the VIC-Architect Eight-Pillar Framework (v4.2) with zero external dependencies.
We present VIC-Research-Assistant, a minimal, reproducible Vertical Intelligence Companion that demonstrates the VIC-Architect Eight-Pillar Framework v4.2 with zero external dependencies.
We present VIC-Research-Assistant, a minimal, reproducible Vertical Intelligence Companion that demonstrates the VIC-Architect Eight-Pillar Framework v4.2 with zero external dependencies.
Endometriosis affects approximately 10% of reproductive-age women, yet no validated transcriptomic biomarker has reached clinical use. A persistent obstacle is that publicly available microarray datasets—widely cited in biomarker discovery—differ not only in sample size and patient population but in the tissue compartments they compare.