← Back to Research Radar
Academic Publication Academic Publication

Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development

551
Citations
November 1, 2024
Published Date

Research Abstract & Technology Focus

AbstractDespite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this issue by providing a practical guide to SHapley Additive exPlanations (SHAP), a popular feature‐based interpretability method, which can be seamlessly integrated into supervised ML models to gain a deeper understanding of their predictions, thereby enhancing their transparency and trustworthiness. This tutorial focuses on the application of SHAP analysis to standard ML black‐box models for regression and classification problems. We provide an overview of various visualization plots and their interpretation, available software for implementing SHAP, and highlight best practices, as well as special considerations, when dealing with binary endpoints and time‐series models. To enhance the reader's understanding for the method, we also apply it to inherently explainable regression models. Finally, we discuss the limitations and ongoing advancements aimed at tackling the current drawbacks of the method.
practical guide analysis explaining supervised machine
Read Full Literature

AI Semantic Synergy Context

Connecting this academic literature to real-world market discussions and products.

crossref.org › academic paper
100%
🔥

Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development

AbstractDespite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, whic...
crossref.org › academic paper
0%

Advancing water quality assessment and prediction using machine learning models, coupled with explainable artificial intelligence (XAI) techniques like shapley additive explanations (SHAP) for interpreting the black-box nature

No description provided.
crossref.org › academic paper
0%

Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer’s disease detection

AbstractExplainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep learning ...
crossref.org › academic paper
0%

Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning

AbstractEstimating protein targets of compounds based on the similarity principle—similar molecules are likely to show comparable bioactivity—is a long-standing strategy in drug research. Having pr...
crossref.org › academic paper
0%

A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME

eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods help to communicate how the model ...

Frequently Asked Questions (FAQ)

Curated market intelligence mapped to this research.

What is the core focus of the research titled 'Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development'?

This literature focuses on: AbstractDespite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We ad...

Are there open-source GitHub repositories related to Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development?

Yes, open-source projects like coleam00/excalidraw-diagram-skill (Skill to give Claude Code (and any coding agent) the ability to generate beautiful and practical Excalidraw diagrams.) are actively building upon these concepts.

Which startups are commercializing the technology behind Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development?

Products like Clicky are bringing this to market. Their focus is: AI buddy next to your cursor on Mac—sees, guides, helps you!.

What other academic literature is closely related to 'Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development'?

Yes, highly correlated activity was mapped. An entry titled 'Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development' discusses this: AbstractDespite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interp...

Cite this Market Intelligence Report

Reference our AI-mapped synergy between this research and the commercial market to instantly build authority.

Commercial Realization

Startups and Open Source tools heavily associated with the concepts explored in this paper.

Associated Media Narrative