Explainable AI in Drug Sensitivity Prediction on Cancer Cell Lines

Gillani, Ismat Saira; Shahzad, Muhammad; Mobin, Ansharah; Munawar, Muhammad; Awan, Muhammad Usman; Asif, Muhammad

doi:10.1109/icetst55735.2022.9922931

Cited by 5 publications

(3 citation statements)

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“…Therefore, XAI results may result in precise clinical predictions. 17 Taken together, these studies highlight the importance of developing interpretable models for drug response prediction and identifying essential genes that contribute to drug sensitivity. Such insights can potentially inform the design of more effective therapies, ultimately benefiting patients and the healthcare industry.…”

Section: Related Workmentioning

confidence: 96%

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Identification of biological markers in cancer disease using explainable artificial intelligence

Shahzad,

Lohana,

Aurangzeb

et al. 2024

Int J Imaging Syst Tech

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The research aims to improve the prediction of drug sensitivity on cancer cell lines using gene expression data and molecular fingerprints of drugs. The proposed study uses a deep learning model, BioMarkerX, trained on the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) datasets utilizing Particle Swarm Optimization technique to select specific genes as features. The model achieves high prediction accuracy with a Root Mean Square Error (RMSE) of 0.40 ± 0.02 and R2 of 0.83 ± 0.03 on the CCLE dataset, and an RMSE of 0.36 ± 0.05 and R2 of 0.83 ± 0.03 on the GDSC dataset. The approach also used an explainable artificial intelligence model to discover biological markers linked to cancer development. This can provide insights into targeted therapies for improving cancer treatment outcomes. Overall, the study presents an effective approach for identifying important biological markers relevant to cancer disease, aiding in the development of more efficient anticancer medications.

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Section: Related Workmentioning

confidence: 96%

“…Gillani et al proposed a model using XAI mentioning that when making predictions using ML models with high local accuracy, XAI has been recognized as a useful working method for determining the applicability of key features. Therefore, XAI results may result in precise clinical predictions 17 …”

Section: Related Workmentioning

confidence: 99%

Identification of biological markers in cancer disease using explainable artificial intelligence

Shahzad,

Lohana,

Aurangzeb

et al. 2024

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…XAI is an area that explains predictions made by AI models. Gillani et al [25] defined the value of features while predictions were made. The prediction of cancer's reaction to a particular treatment or drug efficiency is a hot topic.…”

Section: Xai In Healthcare a Drug Discovery And Xaimentioning

confidence: 99%

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

Alizadehsani,

Oyelere,

Hussain

et al. 2024

IEEE Access

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The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. This review article aims to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.

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A survey of internet of medical things: technology, application and future directions

He,

Huang,

et al. 2024

Digital Communications and Networks

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Explainable AI in Drug Sensitivity Prediction on Cancer Cell Lines

Cited by 5 publications

References 0 publications

Identification of biological markers in cancer disease using explainable artificial intelligence

Identification of biological markers in cancer disease using explainable artificial intelligence

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

A survey of internet of medical things: technology, application and future directions

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