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전체BioinformaticsProteomicsBiomarker ResearchNetwork Biology바이오마커바이오인포매틱스네트워크 생물학Bioinformatics ToolsTranscriptomics취업/면접문제해결도구/소프트웨어취업/커리어프로테오믹스AI/MLCareerClinical ResearchComputational BiologySystems Biology유전체학AI/신약개발시스템 생물학
Bioinformatics2026년 6월 5일· 8 min read

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.

#flux balance analysis#antibiotic synergy#ESKAPE#genome-scale metabolic model
Bioinformatics2026년 6월 2일· 6 min read

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.

#RNA-seq#transcriptomics#large language models#DESeq2
Bioinformatics2026년 6월 1일· 10 min read

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.

#MOGONET#multi-omics#graph neural network#GCN
Proteomics2026년 5월 27일· 10 min read

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.

#limma#DEqMS#proteomics statistics#moderated t-test
Proteomics2026년 5월 27일· 11 min read

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.

#missing value imputation#proteomics#MNAR#MCAR
Proteomics2026년 5월 23일· 10 min read

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.

#Ensembl BioMart#ortholog#Sus scrofa#Mus musculus
Proteomics2026년 5월 23일· 9 min read

Why You Must NOT Merge Species FASTA Databases in Cross-Species Proteomics (Shared Peptide Problem)

In cross-species LC-MS/MS proteomics (Porcine ECM vs Mouse Matrigel, human vs mouse cells, host-pathogen, etc.) merging species FASTA into one search database breaks protein quantification. This is the shared-peptide ambiguity problem. Here's why it happens, how to set up species-separated searches in MaxQuant and DIA-NN, and how to handle peptides that genuinely match both species.

#cross-species proteomics#MaxQuant#DIA-NN#FASTA database
Proteomics2026년 5월 23일· 11 min read

Reanalyzing PRIDE PXD023694 — Matrigel Nuclear Contaminants (EWSR1, RUVBL2) You'll Find in Real Data

A practical reanalysis of PRIDE dataset PXD023694 (Cho lab cross-species ECM) — what proteins actually come out of mouse Matrigel vs porcine ECM, the basement membrane signature (LAMB1/NID1/LAMC1/HSPG2) that replicates Park et al. 2026, and the contamination patterns (Matrigel nuclear proteins EWSR1/RUVBL2, porcine smooth muscle remnants TPM4/MYL9) that simulations miss but real data shows.

#PRIDE#PXD023694#Matrigel#EWSR1
Proteomics2026년 5월 22일· 14 min read

From DIA-NN Output to Paper Draft: A Complete AI-Assisted Proteomics Workflow (2026)

An honest, end-to-end guide to using AI across the full DIA-NN proteomics pipeline — from report.tsv quantification and downstream statistics through figure interpretation, Discussion writing, and a manuscript draft. Includes real prompts, where AI fails (with caught examples), verification checklists, and 2026 journal disclosure requirements.

#DIA-NN#proteomics#AI-assisted research#MaxLFQ
Proteomics2026년 5월 19일· 13 min read

Reproducing Park et al. 2026: Three Iterations of a Cross-Species ECM Proteomics Pipeline

A working record of reproducing the Park et al. 2026 cross-species extracellular matrix proteomics workflow (Porcine esophagus EEM vs Mouse Matrigel) — including three simulation iteration failures (circular logic, pseudocount artifact, gene-symbol vs Ensembl BioMart ortholog mapping) and independent PRIDE PXD023694 validation. Practical lessons for MaxQuant + riBAQ + DEP analysis.

#proteomics#cross-species#ECM#Matrigel
Proteomics2026년 5월 19일· 20 min read

공동연구자 의뢰로 Park et al. 2026을 재현하다 — 종간 ECM 프로테오믹스 분석에서 3번 반복하며 잡은 것들

Porcine 식도 EEM과 Mouse Matrigel을 비교하는 cross-species 프로테오믹스 분석 — Park et al. 2026 방법론을 재현하던 중 v1 시뮬레이션의 순환 논리, v2의 pseudocount artifact, BioMart 1:1 ortholog 검증 단계까지의 실제 워크플로 기록. PRIDE PXD023694 실데이터로 독립 검증한 결과까지.

#프로테오믹스#cross-species#ECM#Matrigel
Biomarker Research2026년 3월 30일· 27 min read

Biomarker Discovery and Validation: A Comprehensive Guide to the Workflow and Methods (2026)

Complete biomarker discovery and validation workflow — biomarker discovery methods (proteomics, metabolomics, genomics), study design, statistical pitfalls, regulatory pathway (FDA/CE-IVD/MFDS), and validation steps that separate publishable findings from clinically useful tests. Includes REMARK/TRIPOD checklists and real failure case studies.

#biomarker discovery#biomarker validation#biomarker discovery workflow#biomarker discovery methods
Biomarker Research2026년 3월 30일· 20 min read

Proteomics-Based Biomarker Discovery in the AI Era: A Critical Review of What's Working and What Isn't

A frank look at where proteomics biomarker discovery stands in 2026 — the genuine advances from AI/ML integration, the persistent failure modes that have plagued the field for 20 years, and the real-world workflows that are starting to produce clinically translatable results.

#proteomics biomarker discovery#AI biomarker#machine learning proteomics#clinical biomarkers