🧬 Deep learning is quietly transforming biomedical research. From classifying skin lesions to detecting cancer in histopathology slides, it's already outperforming traditional workflows in many cases. Here's how and why it matters 🧵👇

You don't need to be a great programmer to do bioinformatics. You just need to be a great problem-solver who's not afraid to Google error messages.

In biomedical AI, performance metrics like accuracy mean little without context. Sensitivity, specificity, and AUC tell you how your model fails, not just how well it works.

FASTA vs FASTQ: same idea, different details. FASTA: sequence only. FASTQ: sequence + quality scores. Learn to read both, and you'll understand 80% of what flows through genomics pipelines.

Before learning a new bioinformatics tool, ask: 1. What problem does it actually solve? 2. What are its assumptions? 3. How does it fail? Most errors come from skipping step 3.

Understanding log2 fold change: log2(2) = 1 -> expression doubled log2(0.5) = -1 -> expression halved Think in fold changes, not raw counts.

Pro tip: Always check sample IDs carefully before merging datasets. Mislabeled samples = silent errors. Silent errors are deadly.

Every bioinformatician starts with broken scripts, cryptic errors, and failed installs. The difference is: some quit, others debug. Debugging is the job!!

What is a VCF file? Variant Call Format = a text file describing genetic variants. Each line = chromosome, position, reference allele, alternative allele. When you learn to parse VCFs, you unlock a huge part of genomics!

If you can't rerun your own analysis 3 months later, it's not reproducible. Always write your README for future-you. Future-you is forgetful. #Bioinformatics #Reproducibility

Me: “I'm just going to run this alignment real quick before lunch.” Computer: 14 hours later… Bioinformatics teaches patience like nothing else.

awk is ugly. awk is confusing. awk will save you hours. Learn it. Use it. Forget how it works. Learn it again. #Bioinformatics

Always version your datasets. "results_v1/", "results_v2/"... Nothing hurts more than realizing your pipeline changed but you overwrote the output. Simple folders can save entire projects. #Bioinformatics

In bioinformatics, bad data is worse than no data. Missing values, mislabeled files, wrong annotations... that's where mistakes hide. Trust your analysis by first not trusting your dataset. #Bioinformatics

The fastest way to learn bioinformatics isn't by reading endless papers. It's by doing. Download a dataset. Break it. Fix it. Repeat. You'll learn more debugging one pipeline than reading 5 reviews. #Bioinformatics #DataScience

Deep learning is powerful for biomedical imaging. But most projects fail because they skip the basics: Poor data, no validation, overfitting... Start small. Do it properly. Build trust first. #DeepLearning #Bioinformatics

Rule #1 of reproducible research: Always write README files like you're going to forget everything in 3 months. Because you will. #Bioinformatics #Reproducibility

Trauma reshapes the brain at the cellular level. 🧵1/10

Working with public datasets? Always check: Are the samples annotated properly? Is the metadata complete? Any duplicated or mislabeled files? Trust but verify. Bad input = misleading results. #NGS #Bioinformatics

Best feeling in bioinformatics: ✔️ Pipeline runs without errors ✔️ Results look reasonable ✔️ It only took 6 hours of debugging dependencies The little victories matter. #Bioinformatics #ComputationalBiology

🧬 Working with genomic intervals? bedtools is your best friend. Intersect, merge, subtract, shuffle. All with a single command. Master it and save yourself hours of frustration. #Bioinformatics #Genomics