Hi everyone,

I am an undergraduate student trying to understand the working of Apta-MCTS (https://pmc.ncbi.nlm.nih.gov/articles/PMC8232527/). I believe that initially, I have to run the preprocess.py file first and then classifier.py for RNA aptamer classification.

Problem 1: I assumed that preprocess.py would generate files called train.json and test.json, which are required to run classifier.py, but preprocess.py does not seem to generate any output files.

Problem 2: I tried to convert the data from excel files referenced by the authors into .json files using the template provided in their GitHub (https://github.com/leekh7411/Apta-MCTS). (Just to check the working of classifier.py)

I have two Excel files containing information about proteins and aptamers and I need to structure the JSON output as follows:

{

    "targets": {

        "<protein_name>":{

            "model": {

                "method" : "Lee_and_Han_2019|Apta-MCTS",

                "score_function" : "<path of the weights of the pre-trained API classifer>",

                "k"      : "<number of top scored candidates>",

                "bp"     : "<length of candidate RNA-aptamer sequences>",

                "n_iter" : "<number of iterations for each base when method is Apta-MCTS>"

            },

            "protein": {

                "seq" : "<target protein sequence>"

            },

            "aptamer": {

                "name"      : [],

                "seq"       : []

            },

            "candidate-aptamer": {

                "score"    : [],

                "seq"      : [],

                "ss"       : [],

                "mfe"      : []

            },

            "protein-specificity": {

                "name" : "<list of name of proteins that do not want to bind>",

                "seq"  : "<list of sequence of proteins that do not want to bind>"

            }

        }

    },

    "n_jobs" : "<number of available cores for the multiprocessing tasks>"

}

However, the resulting JSON does not match the expected format, causing classifier.py to throw a KeyError: 'protein-seq'.

Input: python3 classifier.py -dataset_dir=datasets/li2014 -tag=rf-iCTF-li2014 -min_trees=35 -max_trees=200 -n_jobs=20 -num_models=1000

Error:

dataset_dir=datasets/li2014 -tag=rf-iCTF-li2014 -min_trees=35 -max_trees=200 -n_jobs=20 -num_models=1000

Traceback (most recent call last):

  File "/home/cake13/Apta-MCTS/paper_version/classifier.py", line 131, in <module>
    fire.Fire(main)

  File "/home/cake13/ViennaRNA-2.7.0/vienna_env/lib/python3.12/site-packages/fire/core.py", line 135, in Fire

    component_trace = _Fire(component, args, parsed_flag_args, context, name)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "/home/cake13/ViennaRNA-2.7.0/vienna_env/lib/python3.12/site-packages/fire/core.py", line 468, in _Fire
    component, remaining_args = _CallAndUpdateTrace(
                                ^^^^^^^^^^^^^^^^^^^^

  File "/home/cake13/ViennaRNA-2.7.0/vienna_env/lib/python3.12/site-packages/fire/core.py", line 684, in _CallAndUpdateTrace
    component = fn(*varargs, **kwargs)
                ^^^^^^^^^^^^^^^^^^^^^^

  File "/home/cake13/Apta-MCTS/paper_version/classifier.py", line 119, in main
    trainset = load_benchmark_dataset(train_json_path)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "/home/cake13/Apta-MCTS/paper_version/preprocess.py", line 243, in load_benchmark_dataset
    pseqs  = d["protein-seq"]
             ~^^^^^^^^^^^^^^^

KeyError: 'protein-seq'

Questions:

1. Could there be an issue with how I structured the JSON from Excel?
2. Are there any best practices for formatting Excel-to-JSON conversions? Is that something that can be done or is my understanding of a json file wrong?
3. Any suggestions for debugging where the JSON format might be incorrect?
4. Do I need any additional files that need to be created or sourced from somewhere apart from what is provided by the authors in their GitHub (https://github.com/leekh7411/Apta-MCTS)?

Thanks in advance for any help!