SBM Lab
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It Almost Worked: Four Ways a Multi-Omics Pipeline Nearly Reported a False Success
A consensus biomarker pipeline on renal cell carcinoma nearly replicated its own results by counting the same 110 patients twice. Then the replication guard turned out to pass when nothing had replicated. A record of four failures that each looked exactly like success.
The Number That Survived a Full Day of Review: What Two Agents Couldn't See
Two AI agents with deliberately different access caught six errors in one working day. A seventh survived both reviews — a p-value with no source anywhere in the run record. The six were caught across a gap; the seventh arrived as prose.
Can Flux Balance Analysis Predict Antibiotic Synergy? 107,296 Simulations Say No — and What to Do Instead
A companion to two ESKAPE preprints: standard LP-based flux balance analysis structurally cannot detect synergy between essential gene pairs (107,296 simulations across 3 pathogens, zero synergy found). The fix is to stop using FBA as a synergy calculator and use it as a feature generator — partial-inhibition simulations plus ML to curate drug targets, fully open-source.
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Can an LLM Run an RNA-seq Analysis on Its Own? Building ARIA, a Decision-Aware Transcriptome Framework
A companion to our preprint: reproducible pipelines run RNA-seq steps reliably, but the decisions between steps still need an expert. ARIA puts an LLM in that reasoning seat across 8 decision points — and on 4 public datasets it recovered paired designs, technical covariates, and known biology, with cross-method agreement r > 0.99.
A MOGONET-Style Multi-Omics Biomarker Pipeline: Why a Near-Random Graph Net Still Earns Its Place
Honest engineering write-up of a MOGONET-style multi-omics consensus biomarker pipeline. On a small synthetic cohort the graph network scores near-random in leak-free cross-validation (AUC 0.53) — yet the 5-evidence consensus puts known markers in 9 of its top 10. Here is the architecture, the real results, and why a weak single model is still a useful voter.
FragPipe vs MaxQuant — 2026 Real Speed Benchmark (Same Data, Same Output Quality)
Detailed 2026 speed comparison: FragPipe (MSFragger engine) vs MaxQuant on identical DDA datasets, identical FASTA, identical FDR settings. Where FragPipe's 10-50× speedup comes from (fragment ion indexing), where it matches MaxQuant in protein ID counts, and the specific cases where MaxQuant is still better.
limma vs DEqMS for Proteomics — When to Use Which (n=3 to n=20+ Comparison)
Both limma and DEqMS provide moderated t-statistics for proteomics differential expression. limma was built for microarrays; DEqMS adds a proteomics-specific variance prior tied to peptide count. This guide compares them on small-n (n=3) and larger (n=10, n=20) proteomics designs with real recommendations on when DEqMS's extra step is worth it.
Imputing Missing Values in Proteomics — knn vs minDet vs MNAR — What Actually Works
Proteomics datasets are full of NAs, and how you handle them can flip your DEP list. This guide compares the four real-world options — leave NAs alone, k-Nearest Neighbors (knn), minimum detected (minDet), and explicit MNAR (Missing Not At Random) imputation — with practical recommendations for DIA-NN and MaxQuant outputs.
BioMart Pig ↔ Mouse 1:1 Ortholog Mapping for Cross-Species Proteomics (R + Python Tutorial)
Step-by-step tutorial: download the Ensembl BioMart Release 111 Sus scrofa ↔ Mus musculus 1:1 ortholog table and join it to your per-species protein quantification in R (biomaRt) and Python (pandas). Why gene-symbol matching is fragile, how to handle one-to-many and many-to-many orthologs, and how to validate your mapping against UniProt.