## Machine Learning in Biomedical Sciences: Enhancing the Power of Data with Domain Knowledge### Informed Machine Learning vs. Uninformed Machine LearningMachine learning (ML) has become indispensable in biomedical research, enabling the analysis of complex data to address critical health challenges. However, issues can arise when applying ML to medical data. To address these challenges, informed machine learning (IML) has emerged, incorporating domain knowledge to enhance model accuracy and interpretability. In contrast, uninformed machine learning (UML) treats all variables equally, ignoring specific biomedical knowledge.### Eight Guidelines for Effective Informed Machine Learning**1. Acknowledge Limitations of Domain Knowledge**IML utilizes knowledge, but this knowledge may have limitations. It is crucial to be aware of these limitations and accurately assess the potential impact on the model.**2. Understand IML Implementation Methods**IML can be implemented in three main ways: pre-processing (preparing data and modifying input), in-processing (integrating knowledge directly into the learning process), and post-processing (adjusting model outputs to align with domain knowledge). Understanding these methods is key to effective IML.**3. Utilize Pre-processing Knowledge Appropriately**Pre-processing plays a vital role in IML by regulating the input fed to ML models, guiding model selection, and enriching data accuracy. By modifying inputs (cleaning, engineering, and selecting), modifying data (selecting and enriching), and guiding model selection, researchers can maximize the potential of IML.**4. Integrate In-processing Knowledge Effectively**In-processing involves incorporating domain-specific laws and principles into the learning process. This can be achieved by penalizing deviations from expected behaviors or developing surrogate models for complex systems. By adhering closely to known scientific knowledge, in-processing enhances model fidelity and reliability.**5. Utilize Post-processing Knowledge Appropriately**Post-processing is crucial for aligning model outputs with domain knowledge and ensuring practical implementation. Adjustments can be made to ensure consistency and conform to established protocols. This step not only enhances the model's reliability but also improves its acceptance among practitioners.**6. Involve Biomedical Experts**Collaboration with biomedical experts is pivotal in IML. Their insights provide a strong foundation for the initial scientific question and the interpretation of the final results. Involving experts during both the early and late stages of the project is highly recommended.**7. Evaluate and Compare Different Approaches**Adopting multiple methods to analyze data is a best practice in computational projects. Applying IML, UML, and knowledge-based approaches can provide valuable insights into the data and the methods employed. This comparative analysis can lead to discoveries about data characteristics or methodological advantages.**8. Follow Open Science Practices**Open science principles, such as open source software code, open data release, and open access publication, promote transparency and reproducibility in research. By adhering to open science practices, researchers can maximize their study's impact and foster collaboration within the scientific community.