[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118696-en":3,"doc-seo-118696-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118696,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Machine learning for multi-source data integration","This thesis develops statistical methods for integrating data from multiple sources, targeting applications in gene regulation and co-expression inference. Combining heterogeneous datasets often violates the independent and identically distributed assumption, limiting conventional analysis. The work advances computational tools for heterogeneous biological data integration, including generative models for multi-view latent processes and robust co-expression network inference. It also introduces sequential testing frameworks using e-values to enable continuous monitoring, efficient evidence aggregation, and strong type I error control without multiple-testing corrections.","UvA-DARE (Digital Academic Repository)  \nMachine learning for multi-source data integration  \nPandeva, T. P.  \nPublication date  \n2025  \nDocument Version  \nFinal published version  \nLink to publication  \nCitation for published version (APA):  \nPandeva, T. P. (2025) . Machine learning for multi-source data integration. [Thesis, fully internal, Universiteit van Amsterdam] .  \nGeneral rights  \nIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) .  \nDisclaimer/Complaints regulations  \nIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, P.O. Box 19185, 1000 GD Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl))  \nDownload date:27 Apr 2026  \nMACHINE LEARNING FOR MULTI-SOURCE DATA INTEGRATION TEODORA PANDEVA  \nMachine Learning for Multi-Source Data Integration  \nTeodora Pandeva  \nMACHINE LEARNING FOR MULTI-SOURCE DATA INTEGRATION  \nteodora pandeva  \nCopyright © 2025 Teodora Pandeva  \nAll rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise, without the prior written permission of the author.  \nThe thesis was printed by Ridderprint. Thank you.  \nMachine Learning for Multi-Source Data Integration  \nACADEMISCH PROEFSCHRIFT  \nter verkrijging van de graad van doctor  \naan de Universiteit van Amsterdam  \nop gezag van de Rector Magnificus  \n[prof. dr. ir. P.P.C.C. Verbeek](prof. dr. ir. P.P.C.C. Verbeek)  \nten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel op donderdag 26 juni 2025, te 13.00 uur  \ndoor Teodora Plamenova Pandeva  \ngeboren te Sofia  \nPromotiecommissie  \nPromotores:  \nCopromotores:  \nOverige leden:  \ndr. P.D. Forré  \nprof. dr. L.W. Hamoen  \nprof. dr. J.M. Mooij dr. M.J. Jonker  \nprof. dr. P.D. Grünwald prof. dr. A.D.J. van Dijk dr. E.T. Nalisnick prof. dr. F.P. Pijpers dr. V. Krzhizhanovskaya prof. dr. A.J. Hyvärinen  \nUniversiteit van Amsterdam  \nUniversiteit van Amsterdam  \nUniversiteit van Amsterdam  \nUniversiteit van Amsterdam  \nUniversiteit Leiden  \nUniversiteit van Amsterdam Johns Hopkins University Universiteit van Amsterdam  \nUniversiteit van Amsterdam University of Helsinki  \nFaculteit der Natuurwetenschappen, Wiskunde en Informatica  \nIn the memory of my loving grandmother, Evdokiya Milanova.  \n1941–2021  \nSUMMARY  \nThis thesis presents statistical methods for data integration from multiple sources with applications to gene regulation/co-expression inference. When combining multiple sources to extract deeper insights, the practitioner often encounters challenges that break the independent and identically distributed assumption of the data. This thesis addresses several of these obstacles by advancing computational methods for integrating heterogeneous biological data and developing robust, flexible sequential testing frameworks that can adapt to complex, real-world datasets.  \nThe first part of this thesis proposes generative models designed to extract meaningful biological information from diverse datasets:  \n• Multi-view Independent Component Analysis with Shared and Individual Sources  \nWe propose a framework wher","cbCaitjnN4zBUjLj","https://ap.wps.com/l/cbCaitjnN4zBUjLj","pdf",8792633,1,200,"English","en",105,"# Summary\n## Generative models for biological data integration\n## Robust multi-view co-expression network inference\n## Sequential hypothesis testing with e-values\n## E-valuating classifier two-sample tests\n## Deep anytime-valid hypothesis testing","[{\"question\":\"What main problem does the thesis address?\",\"answer\":\"It addresses statistical challenges in integrating data from multiple sources, especially when the i.i.d. assumption is broken in real-world heterogeneous datasets.\"},{\"question\":\"Which biological applications are covered?\",\"answer\":\"The methods are applied to gene regulation and gene co-expression inference, including reconstruction of co-expression networks across studies.\"},{\"question\":\"How does the thesis handle sequential testing?\",\"answer\":\"It uses e-values to build sequential hypothesis tests that support continuous monitoring of data streams, efficient aggregation of evidence against the null, and tight type I error control without multiple-testing corrections.\"}]","Machine learning for multi-source data integration | 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