Skill UI

A comprehensive guide designed to assess whether a molecular or translational oncology study meets the high standards of a top-tier journal like Cancer Cell. It helps researchers critically evaluate their work's mechanistic depth and translational relevance, providing actionable steps before investing in full manuscript submission.

Provides comprehensive guidelines for statistical analysis and reporting in biological manuscripts, particularly in cancer research. It teaches researchers how to correctly define 'n' (distinguishing biological from technical replicates), select appropriate statistical tests (ANOVA, survival analysis, etc.), and manage multiple comparisons, ensuring high scientific rigor and reproducibility in published data.

Provides comprehensive guidelines for authors preparing high-impact front matter for leading cancer journals (e.g., Cancer Cell). It details the structure and content requirements for the Summary, Highlights, eTOC blurb, and Graphical Abstract, ensuring mechanistic accuracy, impact, and adherence to publication standards.

A comprehensive guide for planning and auditing highly rigorous biological experimental protocols, particularly for cancer mechanism studies. It emphasizes orthogonal validation across independent systems (in vitro, in vivo, human), detailing requirements for controls, replicates, sample size justification, randomization, and blinding to ensure scientific rigor.

gget is a versatile CLI and Python package designed for rapid, reproducible bioinformatics searches across major genomic reference databases (e.g., Ensembl, UniProt). It allows users to quickly query gene metadata, perform initial BLAST/BLAT style alignments, and explore associated pathways, cancer, or disease modules. It is ideal for initial data exploration and generating reproducible evidence logs before building complex, full-scale local pipelines.

DeepSpot-M is a multimodal foundation model that generates virtual spatial transcriptomics from H&E histology images. It maps 224x224 tiles to spatial gene expression using deep learning, supporting over 19k protein-coding genes. Ideal for research in pathology and cancer genomics, requiring gated model access and Python environment.