Guides · ISEF category
Computational Biology and Bioinformatics
ISEF category COMP · difficulty for high-school students: medium
Applying computational and statistical methods to biological data: genomics, proteomics, phylogenetics, structural biology, systems biology, drug discovery algorithms.
How it's judged
ISEF scores 100 points across five criteria:
- Creative Ability: 30 pts
- Scientific Thought: 30 pts
- Thoroughness: 15 pts
- Skill: 15 pts
- Clarity: 10 pts
What wins
- Novel algorithm for genome assembly, variant calling, or sequence alignment
- Machine learning model predicting protein function from sequence with novel features
- Phylogenetic analysis revealing unexpected evolutionary relationships
- Network analysis of protein-protein interaction graphs identifying hub genes in disease
- Single-cell RNA-seq re-analysis of public data with novel clustering or trajectory approach
Common mistakes
- Training ML models on same-distribution test sets (data leakage)
- Using BLAST without understanding E-value thresholds
- Not validating computational predictions with wet-lab or literature data
- Ignoring batch effects in multi-dataset genomics analysis
What you'll need
Laptop with Python/R/Bioconductor. High-memory tasks may require free cloud compute (Google Colab, AWS Educate). No wet lab required.
Ethics & approvals
Human-subjects (IRB) approval is usually not required for this category. Vertebrate-animal (IACUC) approval is usually not required.
The novelty bar
A new algorithm, a new biological question answered with existing tools, or a novel dataset combination. Applying a standard pipeline to a new organism is borderline — you need a novel biological insight too.
Build a competitive Computational Biology and Bioinformatics project.
Finalia takes you from idea to a defensible Computational Biology and Bioinformatics paper — phases 1–4 free, founding rate $99/mo.
Start free →