BicTric: Learning prognostic models using a mixture of biclustering and triclustering

Introduction

Getting Started

These instructions will get you how to run the BicTric classifier.

Prerequisites

To run the BicTric classifier you need to have Python 3.4 or above installed as well as the following packages:

• scikit-learn
• scipy
• numpy
• pandas
• sortedcontainers

Config File (YAML)

BicTric runs using parameters defined in a yaml config file.

Example:

DATA_FILE: <path_to_data_file>
TOP_FOLDER: <path_to_outputs_top_folder>
TARGET: <TARGET class>
CS_STRATEGY: <"strict" OR "flexible">
DISCRETIZATION: False
PATTERNS_3D: False
STAGES: False
FEATURE_SELECTION: False
STATIC_FEATURES: {
                  'Gender':'categorical',
                  'Age_onset':'continuos',
                  'ALS_familiar_history':'categorical',
                  'UMNvsLMN':'categorical',
                  'Onset':'categorical',
                  'C9orf72':'categorical'
                }
TEMPORAL_FEATURES: {
                  'ALSFRSb':'categorical',
                  'ALSFRSsUL':'categorical',
                  'ALSFRSsLL':'categorical',
                  'R':'categorical',
                  'ALSFRS-R':'categorical',
                  '%FVC':'continuos',
                  'MITOS-stage':'categorical'
                  }

CS_STRATEGY: Strategy to be used in the process of creating the sets of snapshots. Flexible if we are using a minimum lenght or Strict if we want strict lenght of sets.

How to run

1. Generate longitudinal tables

$ python3 longitudinal_tables_strat.py <n> <config_file_name>

2. Run BicTric pipeline

$ python3 bictric.py <n> <config_file> [<cr_point>] [<tw>] [<group>]

Citing the Paper 📑

If you use the BicTric classifier in your research, please cite our paper:

Soares, D. F., Henriques, R., Gromicho, M., de Carvalho, M., & C Madeira, S. (2022) Learning prognostic models using a mixture of biclustering and triclustering: Predicting the need for non-invasive ventilation in Amyotrophic Lateral Sclerosis. Journal of Biomedical Informatics, 134, 104172 https://doi.org/10.1016/j.jbi.2022.104172