What is it about?
This paper discusses the use of advanced machine learning techniques, such as network embeddings, to understand gene-disease relationships from large databases. While these methods have shown promise, they are often complex and require significant computational resources. The paper argues for integrating these advanced techniques with more traditional, explainable machine learning methods, inspired by natural biological processes, to make the results more understandable and reliable. By comparing the shapes of proteins linked to diseases with healthy ones, the paper suggests that we can gain clearer insights into genetic diseases and improve the accuracy and robustness of predictive models.
Featured Image
Why is it important?
The paper highlights a critical gap in current bioinformatics approaches: while advanced algorithms like network embeddings offer powerful solutions, they often operate as "black boxes," making it difficult to understand their decision-making processes. By advocating for a blend of classical and explainable machine learning techniques, inspired by biological processes, the paper aims to make these models more interpretable and practical. This approach could lead to more accurate and reliable predictions of gene-disease relationships, ultimately enhancing our ability to diagnose and treat genetic disorders.
Perspectives
From my perspective, this paper presents a compelling argument for revisiting and refining traditional machine learning approaches by integrating insights from computational biology. The emphasis on explainability and robustness is crucial, given the high stakes involved in genetic research and medical applications. The proposed method of comparing protein geometries offers a novel way to bridge the gap between complex machine learning models and practical, understandable insights. This approach not only enhances our understanding of genetic diseases but also underscores the importance of making advanced algorithms more transparent and accessible.
Houcemeddine Turki
Universite de Sfax
Read the Original
This page is a summary of: Developing intuitive and explainable algorithms through inspiration from human physiology and computational biology, Briefings in Bioinformatics, April 2021, Oxford University Press (OUP),
DOI: 10.1093/bib/bbab081.
You can read the full text:
Contributors
The following have contributed to this page
