Traceable Claims in Bioinformatics

 Quick Analysis Plan Copied

 Long Analysis Plan

Core Bioinformatics/Coding/Analysis Plan

The evidence from eight recent studies supports a staged pipeline for deep learning in cell dynamics: (1) weakly supervised segmentation using LACSS with image-level annotations and LOIs, achieving AP50 ≈0.93 on fluorescence data and 0.82–0.93 on most LIVECell lines, but degrading to 0.43 for SHSY5Y morphology LACSS weak annotation segmentation; (2) multimodal foundation modeling — CAPTAIN integrates 4.2M co-assayed RNA/protein cells with dual-encoder Transformers and cross-attention for zero-shot protein inference CAPTAIN multimodal foundation model, while scPair accelerates RNA↔ATAC mapping via implicit feature selection scPair multimodal cell mapping; (3) label-free dynamic imaging — holographic image flow cytometry reveals a measurable accuracy–speed Pareto frontier Holographic imaging flow cytometry accuracy-speed trade-off.

For survival/trajectory modeling, SCMO (SELU architecture, 512-512-1024-256, dropout 0.15) achieves C-index 0.762 with AUC 0.752–0.869 across horizons, outperforming RSF, SVM, GBM, CoxBoost, and DeepOmix SCMO CRC survival prediction.

Challenges and Blindspots

• Generalization: SHSY5Y segmentation drops AP50 from 0.61 to 0.43 under weak supervision SHSY5Y weak supervision gap; HIFC results use only 2 cell lines HIFC cell line limitation.
• Cross-species gaps: Mouse-Geneformer (21M profiles) shows human-model transfer limitations Mouse-Geneformer cross-species.
• Clinical translation: Label-free drug-resistance classification reaches >80% accuracy but lacks prospective clinical validation Label-free drug resistance morphology DL.

Recommended workflow: LACSS → CellProfiler/scanpy QC → CAPTAIN or scPair multimodal integration → TMEnet/label-free classification → SCMO-style prognostic modeling, with explicit LOU cross-validation and Pareto-front evaluation at each stage.