PaccMann: a web service for interpretable anticancer compound sensitivity prediction
| Autor: | Jannis Born, Joris Cadow, Ali Oskooei, Matteo Manica, María Rodríguez Martínez |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
AcademicSubjects/SCI00010
Antineoplastic Agents Biology Machine learning computer.software_genre 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Genetics Computer Simulation 030304 developmental biology Interpretability Sirolimus Internet 0303 health sciences Artificial neural network business.industry Gene Expression Profiling Drug Repositioning Toolbox 3. Good health Drug repositioning Identification (information) chemistry 030220 oncology & carcinogenesis Web Server Issue The Internet Neural Networks Computer Artificial intelligence Web service business computer Lead compound Software |
| Zdroj: | Nucleic Acids Research Nucleic Acids Research, 48 (W1) |
| Popis: | The identification of new targeted and personalized therapies for cancer requires the fast and accurate assessment of the drug efficacy of potential compounds against a particular biomolecular sample. It has been suggested that the integration of complementary sources of information might strengthen the accuracy of a drug efficacy prediction model. Here, we present a web-based platform for the Prediction of AntiCancer Compound sensitivity with Multimodal Attention-based Neural Networks (PaccMann). PaccMann is trained on public transcriptomic cell line profiles, compound structure information and drug sensitivity screenings, and outperforms state-of-the-art methods on anticancer drug sensitivity prediction. On the open-access web service (https://ibm.biz/paccmann-aas), users can select a known drug compound or design their own compound structure in an interactive editor, perform in-silico drug testing and investigate compound efficacy on publicly available or user-provided transcriptomic profiles. PaccMann leverages methods for model interpretability and outputs confidence scores as well as attention heatmaps that highlight the genes and chemical sub-structures that were more important to make a prediction, hence facilitating the understanding of the model’s decision making and the involved biochemical processes. We hope to serve the community with a toolbox for fast and efficient validation in drug repositioning or lead compound identification regimes. |
| Databáze: | OpenAIRE |
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