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A deep tabular data...
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Nasimian, AhmadLund University,Lunds universitet,Avdelningen för translationell cancerforskning,Institutionen för laboratoriemedicin,Medicinska fakulteten,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Division of Translational Cancer Research,Department of Laboratory Medicine,Faculty of Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments
(författare)
A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer
- Artikel/kapitelEngelska2023
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LIBRIS-ID:oai:lup.lub.lu.se:0aa73ee6-43b0-4a7b-8b22-c629b686df36
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https://lup.lub.lu.se/record/0aa73ee6-43b0-4a7b-8b22-c629b686df36URI
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https://doi.org/10.1016/j.csbj.2023.01.020DOI
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Språk:engelska
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Sammanfattning på:engelska
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Cisplatin, a platinum-based chemotherapeutic agent, is widely used as a front-line treatment for several malignancies. However, treatment outcomes vary widely due to intrinsic and acquired resistance. In this study, cisplatin-perturbed gene expression and pathway enrichment were used to define a gene signature, which was further utilized to develop a cisplatin sensitivity prediction model using the TabNet algorithm. The TabNet model performed better (>80 % accuracy) than all other machine learning models when compared to a wide range of machine learning algorithms. Moreover, by using feature importance and comparing predicted ovarian cancer patient samples, BCL2L1 was identified as an important gene contributing to cisplatin resistance. Furthermore, the pharmacological inhibition of BCL2L1 was found to synergistically increase cisplatin efficacy. Collectively, this study developed a tool to predict cisplatin sensitivity using cisplatin-perturbed gene expression and pathway enrichment knowledge and identified BCL2L1 as an important gene in this setting.
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Ahmed, MehreenLund University,Lunds universitet,Avdelningen för translationell cancerforskning,Institutionen för laboratoriemedicin,Medicinska fakulteten,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Division of Translational Cancer Research,Department of Laboratory Medicine,Faculty of Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments(Swepub:lu)me0785ah
(författare)
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Hedenfalk, IngridLund University,Lunds universitet,Bröst- och ovarialcancer,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Bröst/ovarialcancer,Sektion I,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Breast and Ovarian Cancer Genomics,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Breast/ovarian cancer,Section I,Department of Clinical Sciences, Lund,Faculty of Medicine,Skåne University Hospital(Swepub:lu)onk-ifa
(författare)
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Kazi, Julhash U.Lund University,Lunds universitet,Avdelningen för translationell cancerforskning,Institutionen för laboratoriemedicin,Medicinska fakulteten,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Division of Translational Cancer Research,Department of Laboratory Medicine,Faculty of Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments(Swepub:lu)med-kui
(författare)
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Avdelningen för translationell cancerforskningInstitutionen för laboratoriemedicin
(creator_code:org_t)
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Ingår i:Computational and Structural Biotechnology Journal: Elsevier BV21, s. 956-9642001-0370
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