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\n  \n 2024\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information.\n \n \n \n \n\n\n \n Jayagopal, A.; Xue, H.; He, Z.; Walsh, R. J.; Hariprasannan, K. K.; Tan, D. S. P.; Tan, T. Z.; Pitt, J. J.; Jeyasekharan, A. D.; and Rajan, V.\n\n\n \n\n\n\n In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, of KDD '24, pages 5138–5149, New York, NY, USA, 2024. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"PersonalisedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3637528.3671652,\nauthor = {Jayagopal, Aishwarya and Xue, Hansheng and He, Ziyang and Walsh, Robert J. and Hariprasannan, Krishna Kumar and Tan, David Shao Peng and Tan, Tuan Zea and Pitt, Jason J. and Jeyasekharan, Anand D. and Rajan, Vaibhav},\ntitle = {Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information},\nyear = {2024},\nisbn = {9798400704901},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3637528.3671652},\ndoi = {10.1145/3637528.3671652},\nabstract = {Cancer remains a global challenge due to its growing clinical and economic burden. Its uniquely personal manifestation, which makes treatment difficult, has fuelled the quest for personalized treatment strategies. Thus, genomic profiling is increasingly becoming part of clinical diagnostic panels. Effective use of such panels requires accurate drug response prediction (DRP) models, which are challenging to build due to limited labelled patient data. Previous methods to address this problem have used various forms of transfer learning. However, they do not explicitly model the variable length sequential structure of the list of mutations in such diagnostic panels. Further, they do not utilize auxiliary information (like patient survival) for model training. We address these limitations through a novel transformer-based method, which surpasses the performance of state-of-the-art DRP models on benchmark data. Code for our method is available at https://github.com/CDAL-SOC/PREDICT-AI.We also present the design of a treatment recommendation system (TRS), which is currently deployed at the National University Hospital, Singapore and is being evaluated in a clinical trial. We discuss why the recommended drugs and their predicted scores alone, obtained from DRP models, are insufficient for treatment planning. Treatment planning for complex cancer cases, in the face of limited clinical validation, requires assessment of many other factors, including several indirect sources of evidence on drug efficacy. We discuss key lessons learnt on model validation and use of indirect supporting evidence to build clinicians' trust and aid their decision making.},\nbooktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},\npages = {5138–5149},\nnumpages = {12},\nkeywords = {auxiliary information, cancer drug response prediction, clinical deployment, personalized treatment recommendation, survival prediction, transformers},\nlocation = {Barcelona, Spain},\nseries = {KDD '24}\n}
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\n Cancer remains a global challenge due to its growing clinical and economic burden. Its uniquely personal manifestation, which makes treatment difficult, has fuelled the quest for personalized treatment strategies. Thus, genomic profiling is increasingly becoming part of clinical diagnostic panels. Effective use of such panels requires accurate drug response prediction (DRP) models, which are challenging to build due to limited labelled patient data. Previous methods to address this problem have used various forms of transfer learning. However, they do not explicitly model the variable length sequential structure of the list of mutations in such diagnostic panels. Further, they do not utilize auxiliary information (like patient survival) for model training. We address these limitations through a novel transformer-based method, which surpasses the performance of state-of-the-art DRP models on benchmark data. Code for our method is available at https://github.com/CDAL-SOC/PREDICT-AI.We also present the design of a treatment recommendation system (TRS), which is currently deployed at the National University Hospital, Singapore and is being evaluated in a clinical trial. We discuss why the recommended drugs and their predicted scores alone, obtained from DRP models, are insufficient for treatment planning. Treatment planning for complex cancer cases, in the face of limited clinical validation, requires assessment of many other factors, including several indirect sources of evidence on drug efficacy. We discuss key lessons learnt on model validation and use of indirect supporting evidence to build clinicians' trust and aid their decision making.\n
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