Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS

Joseph Geraci; Ravi Bhargava; Bessi Qorri; Paul Leonchyk; Douglas Cook; Moses Cook; Fanny Sie; Luca Pani

doi:10.3389/fncom.2023.1199736

Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS

Front Comput Neurosci. 2024 Jan 4:17:1199736. doi: 10.3389/fncom.2023.1199736. eCollection 2023.

Authors

Joseph Geraci^#^{1

2

3

4}, Ravi Bhargava^#^{5

6}, Bessi Qorri^#¹, Paul Leonchyk¹, Douglas Cook^{1

7}, Moses Cook^#⁸, Fanny Sie⁶, Luca Pani^{1

9

10}

Affiliations

¹ NetraMark Corp, Toronto, ON, Canada.
² Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada.
³ Centre for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA, United States.
⁴ Arthur C. Clarke Center for Human Imagination, School of Physical Sciences, University of California San Diego, San Diego, CA, United States.
⁵ Department of Biomedical and Molecular Science, Queens University, Kingston, ON, Canada.
⁶ Science and Research, Roche Integrated Informatics, F. Hoffmann La-Roche, Toronto, ON, Canada.
⁷ Department of Surgery, Queen's University, Kingston, ON, Canada.
⁸ Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
⁹ Department of Psychiatry and Behavioral Sciences, Leonard M. Miller School of Medicine, University of Miami, Coral Gables, FL, United States.
¹⁰ Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.

^# Contributed equally.

Abstract

Introduction: Advances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are necessary to tackle the plethora of known disease states.

Motivation: In addition to providing much needed insights into potential therapeutic protein targets, we also aim to demonstrate that small patient datasets have the potential to provide insights that usually require many samples (>5,000). There are many such datasets available and novel advancements in ML can provide valuable insights from these patient datasets.

Problem statement: Using a public dataset made available by patient advocacy group AnswerALS and a multidisciplinary Open Science approach with a systems biology augmented ML technology, we aim to validate previously reported drug targets in ALS and provide novel insights about ALS subpopulations and potential drug targets using a unique combination of ML methods and graph theory.

Methodology: We use NetraAI to generate hypotheses about specific patient subpopulations, which were then refined and validated through a combination of ML techniques, systems biology methods, and expert input.

Results: We extracted 8 target classes, each comprising of several genes that shed light into ALS pathophysiology and represent new avenues for treatment. These target classes are broadly categorized as inflammation, epigenetic, heat shock, neuromuscular junction, autophagy, apoptosis, axonal transport, and excitotoxicity. These findings are not mutually exclusive, and instead represent a systematic view of ALS pathophysiology. Based on these findings, we suggest that simultaneous targeting of ALS has the potential to mitigate ALS progression, with the plausibility of maintaining and sustaining an improved quality of life (QoL) for ALS patients. Even further, we identified subpopulations based on disease onset.

Conclusion: In the spirit of Open Science, this work aims to bridge the knowledge gap in ALS pathophysiology to aid in diagnostic, prognostic, and therapeutic strategies and pave the way for the development of personalized treatments tailored to the individual's needs.

Keywords: ALS; Open Science; artificial intelligence; augmented intelligence; collaboration; combination therapy; machine learning; targeted therapy.

Grants and funding

The authors declare that this study received funding from NetraMark Holdings and provided compensation to authors JG, BQ, PL, DC, and LP. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.