README.md

CM4AI_LLM_Challenge

set up environment

conda create -n llm_challenge python=3.11.5
conda activate llm_challenge
pip install -r requirements.txt

Toy example:

Located in ./data/toy_example_w_contaminated.csv

Column	Description
GO	Gene Ontology Term ID
Genes	Genes assigned to this GO term
Gene Count	Number of genes
Term_Description	GO term name
50perc_contaminated_Genes	Contaminated gene set with 50% real and 50% random
100perc_contaminated_Genes	Complete random gene set

Task 1 run the pipeline in bash script

mkdir -p logs

# adjust URL to location below to where ollama service is hosted
export LOCAL_MODEL_HOST=http://localhost:11434/api/chat

bash ./run_LLM_annotation_pipeline.sh

Task 2 compare semantic similarity with GO names

bash ./execute_semantic_similarity_calculation.sh

To visualize the result run:

python ./compare_semantic_similarity.py

Task 3 compare confidence scores

python ./compare_confidence_scores.py

Optional Task 4 run on gene sets of interest from VNN interpretation

example run command line

# run in the command line  
input_file='data/GO_term_analysis/toy_example.csv' # replace with your file
config='./jsonFiles/llama2_run_VNN_sample.json' #check this example config and make a new config for your own task 
set_index='GO' # update this to your index of choice
gene_column='Genes' # update to the gene column
gene_sep=' ' # update based on what separator you have
start=0
end=11 # update the number for the length of the file  
out_file = 'data/LLM_processed_VNN_genesets_llama2'
python query_llm_for_analysis.py --config $config \
            --initialize \ # initialize the analysis column with None
            --input $input_file \
            --input_sep  ','\ # update to '\t' if tsv
            --set_index $set_index \
            --gene_column $gene_column\
            --gene_sep ' ' \
            --start $start \
            --end $end \
            --output_file $out_file