Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914859594022912 |
|---|---|
| author | Feng, Tianshu Gnanaolivu, Rohan Safikhani, Abolfazl Liu, Yuanhang Jiang, Jun Chia, Nicholas Partin, Alexander Vasanthakumari, Priyanka Zhu, Yitan Wang, Chen |
| author_facet | Feng, Tianshu Gnanaolivu, Rohan Safikhani, Abolfazl Liu, Yuanhang Jiang, Jun Chia, Nicholas Partin, Alexander Vasanthakumari, Priyanka Zhu, Yitan Wang, Chen |
| contents | Human cancers present a significant public health challenge and require the discovery of novel drugs through translational research. Transcriptomics profiling data that describes molecular activities in tumors and cancer cell lines are widely utilized for predicting anti-cancer drug responses. However, existing AI models face challenges due to noise in transcriptomics data and lack of biological interpretability. To overcome these limitations, we introduce VETE (Variational and Explanatory Transcriptomics Encoder), a novel neural network framework that incorporates a variational component to mitigate noise effects and integrates traceable gene ontology into the neural network architecture for encoding cancer transcriptomics data. Key innovations include a local interpretability-guided method for identifying ontology paths, a visualization tool to elucidate biological mechanisms of drug responses, and the application of centralized large scale hyperparameter optimization. VETE demonstrated robust accuracy in cancer cell line classification and drug response prediction. Additionally, it provided traceable biological explanations for both tasks and offers insights into the mechanisms underlying its predictions. VETE bridges the gap between AI-driven predictions and biologically meaningful insights in cancer research, which represents a promising advancement in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04486 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses Feng, Tianshu Gnanaolivu, Rohan Safikhani, Abolfazl Liu, Yuanhang Jiang, Jun Chia, Nicholas Partin, Alexander Vasanthakumari, Priyanka Zhu, Yitan Wang, Chen Quantitative Methods Artificial Intelligence Human cancers present a significant public health challenge and require the discovery of novel drugs through translational research. Transcriptomics profiling data that describes molecular activities in tumors and cancer cell lines are widely utilized for predicting anti-cancer drug responses. However, existing AI models face challenges due to noise in transcriptomics data and lack of biological interpretability. To overcome these limitations, we introduce VETE (Variational and Explanatory Transcriptomics Encoder), a novel neural network framework that incorporates a variational component to mitigate noise effects and integrates traceable gene ontology into the neural network architecture for encoding cancer transcriptomics data. Key innovations include a local interpretability-guided method for identifying ontology paths, a visualization tool to elucidate biological mechanisms of drug responses, and the application of centralized large scale hyperparameter optimization. VETE demonstrated robust accuracy in cancer cell line classification and drug response prediction. Additionally, it provided traceable biological explanations for both tasks and offers insights into the mechanisms underlying its predictions. VETE bridges the gap between AI-driven predictions and biologically meaningful insights in cancer research, which represents a promising advancement in the field. |
| title | Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses |
| topic | Quantitative Methods Artificial Intelligence |
| url | https://arxiv.org/abs/2407.04486 |