Matlab: To use, type in the terminal CBCR(input, curricula, alpha, gamma_1, gamma_2, learning_rate, max_iter, final_iter)

Parameters

• input: A string for the path of the input file.
• curricula: Integer. The number of curricula to be trained.
• alpha: Number between 0 and 1. The scaling factor for the trained data (values close to .5 are recommended).
• gamma_1: Number between 0 and 1. The scaling factor for the first moment estimator in the adam optimizer (.9 is recommended).
• gamma_2: Number between 0 and 1. The scaling factor for the second moment estimator in the adam optimizer (.999 is recommended).
• learning_rate: Learning rate for the optimizer (values less than .15 are recommended).
• max_iter: The maximumum iterations for training on the individual curricula.
• final_iter: The maximum iterations for training on the whole data set after completion of curriculum training.

There are two possible input formats.

1. Tuple Input format(preferred) : A hi-C contact file, each line contains 3 numbers (separated by a space) of a contact, position_1 position_2 interaction_frequencies
2. Square Matrix Input format: The square matrix is a comma seperated N by N intra-chromosomal contact matrix derived from Hi-C data, where N is the number of equal-sized regions of a chromosome.

All outputs will be saved in a folder called Scores in the working directory.

• output.log: A .log file that displays the optimal conversion factor for the trained structure along with the corresponding root mean-squared error, Pearson correlation distance, and Spearman correlation distance.
• name_CONVERT_FACTOR=cfN=n.pdb: A .pdb file that shows the name of the imput data (name), the conversion factor for the corresponding structure (cf), and the number of curricula (n).