Exploration of Neuroprotective Therapy

Features	Machine learning	Deep learning
Data setting	Structured	Not structured
Learning	By operator	Intrinsic
Database	< 1 × 10⁸ data points	> 1 × 10⁸ data points
Algorithm	Variable	Artificial neural network
Problem solving	By partitioning	End-to-end approach
Execution time	Long model test time	Short model test time
Application field	Routine operations	Complex systems
Hardware	Low-end machine	High-end machine
Suitability	Simple problems	Complex problems

Declarations

Acknowledgments

I thank Dr. Matteo R. Ronchi (Caltech, Pasadena, USA) and Dr. Joseph L. Marino (DeepMind, London, UK) for inspiring this work, critically reading the text, and, most of all, for providing insightful discussions that deepened and broadened my understanding of machine learning.

Author contributions

The author contributed solely to the work.

Conflicts of interest

The author declares that she has no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

The author’s research is supported by FATALSDRUG Project [Progetti di Ricerca@CNR SAC.AD002.173.058] from National Research Council, Italy. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright

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