Preview: Build a Cell-Counting Pipeline Your Group Can Rerun and Check
Level L03
T18
17 min read
Turn a manual Cellpose count into a small pipeline your group can rerun: a script written with an AI coding assistant, settings in one file, a pinned software version and model file, a sample dataset with known-good counts, a one-command check and a README. It passes when a colleague reruns it and gets your counts.
Matching textT18-L03 · AI for lab data · Level 3 Builder · 16-minute read, plus 3 hours of doing Build a Cell-Counting Pipeline Your Group Can Rerun and Check The count only one person can make Three months…
Preview: Predict a Protein Structure and Read Its Confidence Honestly
Level L02
T18
16 min read
Run a structure prediction on the free AlphaFold Server, read pLDDT, PAE, pTM and ipTM for what they do and don't say, cross-check with a BLAST search against the PDB, and write a confidence note that a labmate can check against the same files before anyone designs a construct from it.
Matching textT18-L02 · AI for lab data · Level 2 Power user · 14-minute read, plus 45 minutes of doing Predict a Protein Structure and Read Its Confidence Honestly The ribbon on the group-meeting slide At…
Preview: Count Cells With a Ready-Made Model and Check It by Hand
Level L01
T18
17 min read
Run Cellpose, a free pretrained cell-segmentation model, on a public microscopy image on your own laptop. Check its outlines by eye, compare its count with your own count of one region, and learn the signals that tell you not to trust a count before it goes into your notebook.
Matching textT18-L01 · AI for lab data · Level 1 User · 14-minute read, plus 45 minutes of doing Count Cells With a Ready-Made Model and Check It by Hand Two hundred cells, one afternoon You have twelve fluorescence…
Understand cellular segmentation and pretrained Cellpose-SAM. Not hand-counting protocol; research performance not a guarantee for learner images.
Matching textT18-L01: Understand what Cellpose-SAM does when it outlines cells. Then use the book's counting rule and hand-counted region to judge your own images; benchmark performance cannot validate your…
Preview: Confidence metrics used in the AlphaFold Database multimers
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Read local, positional and interface confidence rather than trust structure images. AFDB multimer context, not Server submission; short-peptide limits require current docs.
Matching textT18-L02: Learn what pLDDT, PAE and interface confidence tell you—and where they mislead—in AlphaFold Database multimers. Start at 8:04; use the book and current Server guidance for submissions…
Preview: cellpose 2.0 tutorial: how to train your own cellular segmentation model
youtube
Load folder images, run batch segmentation and save masks in Python. Historical Cellpose2, not copy-paste Cellpose4.2. Batch-inference section only; not TOML/fingerprints/CSV checks or handover; timestamp unverified.
Matching textT18-L03: Explore using custom Cellpose models in notebooks or Python scripts. Focus on that portion, not training a new model. This is Cellpose 2.0 from 2022, not copy-paste code for 4.2.1.1;…