Neural Network Attribution for Biological Mechanism Interpretation
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Solution Overview
Problem
Current machine-learning models for predicting biological responses, such as treatment efficacy, often result in 'black box' models that lack interpretability, failing to elucidate the underlying biological mechanisms.
Innovation Solution
A neural network-based modeling framework that integrates recursive feature elimination and scoring, allowing for the identification of key biological inputs and elucidation of underlying mechanisms, thereby providing target deconvolution and improving model interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to predict biological responses, then prediction accuracy is improved, but model interpretability deteriorates resulting in black box models
Solution Approach 1:
The model output is segmented into two distinct components: prediction results and attribution scores. The attribution mechanism divides the black box model into interpretable parts by calculating the contribution of each input feature to the final prediction, allowing users to understand which biological inputs drive the predicted response without sacrificing prediction accuracy
Solution Approach 2:
An attribution mechanism serves as an intermediary layer between the neural network and the user. This intermediary computes attribution scores that translate the internal workings of the black box model into interpretable information about biological mechanisms, enabling both accurate prediction and mechanistic insight
2Reliability
If complex neural networks are used to model biological systems, then predictive power is improved, but understanding of underlying mechanisms deteriorates
Solution Approach 1:
The attribution mechanism provides feedback about the biological mechanisms underlying predictions. By calculating and reporting attribution scores for each input feature, the system feeds back information about which biological pathways and molecules contribute most to the predicted response, enabling researchers to understand and validate the biological plausibility of predictions
Solution Approach 2:
The attribution mechanism acts as a mediator that translates complex neural network computations into biologically interpretable information. It bridges the gap between the black box predictive model and human understanding of biological mechanisms by providing attribution scores that highlight key drivers of the prediction
Data Source
AI summary
Systems and methods for modeling highly complex biological relations in machine-learned models, such as neural networks (e.g., such as deep neural networks (DNNs)), to predict biological outcomes and elucidate underlying mechanisms are described. The systems and methods utilize recursive feature elimination and scoring and can be utilized to prioritize particular compounds or treatments for clinical development and direct new avenues of research and development based on elucidated mechanisms.