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The research addresses how deviations in cellular behavior and genetic rearrangements drive cancer and how high-dimensional genomic data can support functional therapeutic translation. Machine learning is applied to single-cell RNA-seq cell annotation and to predicting drug responses in pre-clinical cancer models. The thesis uses an Ikarus pipeline to distinguish neoplastic from healthy cells and evaluates multi-omics strategies to mitigate limitations of panel sequencing for therapy response prediction.","Exploring the Intersection of Multi-Omics and Machine Learning  \nin Cancer Research  \nInaugural-Dissertation  \nto obtain the academic degree Doctor rerum naturalium (Dr. rer. nat.)  \nsubmitted to the Department of Biology, Chemistry, Pharmacy of Freie Universit¨at Berlin  \nby  \nArtem Baranovskii  \nBerlin 2023  \nThe following work was performed from June 2019 until March 2023 under the supervision of Dr. Altuna Akalin at the Berlin Institute for Medical Systems Biology (BIMSB), Max Delbr¨uck Center for Molecular Medicine, Hannoversche Str. 28, 10115 Berlin-Mitte.  \n1st reviewer: Dr. Altuna Akalin  \nBerlin Institute for Medical Systems Biology (BIMSB) Max-Delbr¨uck-Centrum f¨ur Molekulare Medizin  \nHannoversche Str. 28, 10115 Berlin  \n2nd reviewer: Prof. Dr. Irmtraud Meyer  \nFreie Universit¨at Berlin, Special Professor  \nBerlin Institute for Medical Systems Biology (BIMSB) Max-Delbr¨uck-Centrum f¨ur Molekulare Medizin  \nHannoversche Str. 28, 10115 Berlin  \nDate of thesis defence: 18.01.2024  \nAcknowledgements  \nAlthough personal experiences may differ, the road to a doctorate is rarely ordinary. Wading, a student often finds oneself lost and searches waymarks and those one finds, one needs to know to trust. For this, I want to leave in ink a heartwarming gratitude to the people who served me in this role: Altuna Akalin, Irmtraud Meyer, Dmitrii Pervoushine, and Michaela Herzig. Alongside them, I want to thank Vedran Franke, Bora Uyar, Nicolai von K¨ugelgen and Inga L¨odige for all the amusing talk and advice that helped me learn better. Finally, there are people who were around to lean on, not necessarily in the domain of science. Those are Samantha Mendonsa and Erik Becher; thank you for this.  \nDeclaration of Independence  \nHerewith I certify that I have prepared and written my thesis independently and that I have not used any sources and aids other than those indicated by me. I also declare that I have not submitted the dissertation in this or anyother form to any other institution as a dissertation. Artem Baranovskii 24.07.2023  \nForeword  \nThis thesis is cumulative. It includes two works that have been published in peer-reviewed journals. These publications are reproduced in Chapters 3 and 4 of this thesis.  \nPublication I (Chapter 4.1):  \nJan Dohmen*, Artem Baranovskii*, Jonathan Ronen, Bora Uyar, Vedran Franke, and Altuna Akalin, Identifying tumor cells at the single-cell level using machine learning, Genome Biology 2022, 23, 123, [https://doi.org/10.1186/s13059-022-02683-1](https://doi.org/10.1186/s13059-022-02683-1)  \n* These authors contributed equally to the work  \nPublication II (Chapter 4.2):  \nArtem Baranovskii*, Irem G¨und¨uz*, Vedran Franke, Bora Uyar & Altuna Akalin, Multi-Omics Alleviates the Limitations of Panel Sequencing for Cancer Drug Response Prediction, Cancers 2022, 14(22), 5604,  \n[https://doi.org/10.3390/cancers14225604](https://doi.org/10.3390/cancers14225604)  \n* These authors contributed equally to the work  \nContents  \n1 Summary 7  \n1 Zusammenfassung 9  \n2 Introduction 11  \n2.1 Early Genomics .......................... 11  \n2.2 Early Transcriptomics ...................... 14  \n2.3 The need for annotation ..................... 16  \n2.4 Cancer transcriptomics ...................... 19  \n2.5 Early Genome-wide Association Studies (GWASs) ...... 21  \n2.6 Next Generation Sequencing (NGS) .............. 24  \n2.7 Ribonucleic Acid Sequencing (RNA-seq) ............ 27  \n2.8 Human Genome Projects of Cancer ............... 30  \n2.9 Capturing the complexity with cancer models ......... 35  \n2.10 Reduced representation models ................. 40  \n2.11 From Breadth to Depth—understanding cancer evolution . 44  \n2.12 The promise of single-cell sequencing .............. 54  \n2.13 Single-cell RNA-seq data normalisation ............ 59  \n2.14 Computational suite for single-cell RNA-seq analysis ..... 64  \n2.15 Thesis scope ............................ 73  \n3 Material and methods 75  \n3.1 The scraping of citation da","cbCaiuF5yxGv9XJw","https://ap.wps.com/l/cbCaiuF5yxGv9XJw","pdf",5391630,1,148,"English","en",105,"# Summary\n# Zusammenfassung\n# Introduction\n## Early Genomics\n## Early Transcriptomics\n## The need for annotation\n## Cancer transcriptomics\n## Early Genome-wide Association Studies (GWASs)\n## Next Generation Sequencing (NGS)\n## Ribonucleic Acid Sequencing (RNA-seq)\n## Human Genome Projects of Cancer\n## Capturing the complexity with cancer models\n## Reduced representation models\n## From Breadth to Depth—understanding cancer evolution\n## The promise of single-cell sequencing\n## Single-cell RNA-seq data normalisation\n## Computational suite for single-cell RNA-seq analysis\n## Thesis scope\n# Material and methods\n## The scraping of citation data from the Web of Science\n# Results\n## Publication I\n## Publication II\n# Discussion\n## Robust annotation of cancer cells in scRNA-seq data\n## Multi-omics fare better in the prediction of drug response in cancer models\n# Bibliography\n# Publication list and contributions\n# Appendix-Extended data\n## Appendix I-Extended data for Publication I\n## Appendix II-Extended data for Publication II","[{\"question\":\"What is the main focus of this doctoral thesis?\",\"answer\":\"The thesis combines cancer biology with machine learning and multi-omics. It targets cell annotation in single-cell RNA-seq datasets and drug response prediction in pre-clinical cancer models.\"},{\"question\":\"How does the work support cell annotation in single-cell RNA-seq data?\",\"answer\":\"It introduces a pipeline named Ikarus to differentiate neoplastic from healthy cells within single-cell datasets, helping characterize the cellular landscape of tumors.\"},{\"question\":\"What role does multi-omics play in predicting cancer drug response?\",\"answer\":\"The research evaluates how multi-omics can alleviate limitations of panel sequencing when predicting cancer drug response in cancer models, improving the reliability of response prediction.\"}]","Exploring the Intersection of Multi-Omics and Machine Learning in Cancer Research - Inaugural-Dissertation | PDF",1785935194,373,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"exploring-the-intersection-of-multi-omics-and-machine-learning-in-cancer-research-inaugural-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/exploring-the-intersection-of-multi-omics-and-machine-learning-in-cancer-research-inaugural-dissertation/126845/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main focus of this doctoral thesis?","Question",{"text":75,"@type":76},"The thesis combines cancer biology with machine learning and multi-omics. 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