[{"doi_confirm":"1","date_updated":"2026-07-28T07:08:15Z","publication_status":"published","status":"public","publication_identifier":{"issn":["2663-337X"]},"keyword":["Approximate Message Passing","GWAS","Genomics","Proteomics","Survival modeling"],"month":"07","publisher":"Institute of Science and Technology Austria","year":"2026","_id":"22258","acknowledgement":"This work was supported in part by the Swiss National Science Foundation through the\r\nEccellenza Grant \"Improving estimation and prediction of common complex disease risk\"\r\n(grant number PCEGP3_181181); the European Research Council through the grant\r\n\"Inference in High Dimensions: Light-speed Algorithms and Information Limits\" (grant\r\nnumber 101161364); and the Fondation Jean-Jacques et Felicia Lopez-Loreta through the\r\nPrix Lopez-Loretta 2019.\r\n","citation":{"chicago":"Depope, Al. “From Sparse Selection to Risk Prediction: Approximate Message Passing for Proteomic Survival Models and Large-Scale Genomics.” Institute of Science and Technology Austria, 2026. <a href=\"https://doi.org/10.15479/AT-ISTA-22258\">https://doi.org/10.15479/AT-ISTA-22258</a>.","mla":"Depope, Al. <i>From Sparse Selection to Risk Prediction: Approximate Message Passing for Proteomic Survival Models and Large-Scale Genomics</i>. Institute of Science and Technology Austria, 2026, doi:<a href=\"https://doi.org/10.15479/AT-ISTA-22258\">10.15479/AT-ISTA-22258</a>.","ieee":"A. Depope, “From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics,” Institute of Science and Technology Austria, 2026.","short":"A. Depope, From Sparse Selection to Risk Prediction: Approximate Message Passing for Proteomic Survival Models and Large-Scale Genomics, Institute of Science and Technology Austria, 2026.","apa":"Depope, A. (2026). <i>From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT-ISTA-22258\">https://doi.org/10.15479/AT-ISTA-22258</a>","ista":"Depope A. 2026. From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics. Institute of Science and Technology Austria.","ama":"Depope A. From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics. 2026. doi:<a href=\"https://doi.org/10.15479/AT-ISTA-22258\">10.15479/AT-ISTA-22258</a>"},"article_processing_charge":"No","has_accepted_license":"1","oa":1,"file_date_updated":"2026-07-13T14:56:41Z","file":[{"relation":"main_file","checksum":"9ab386790515628d957a194f30a7ccb4","date_created":"2026-07-13T14:52:19Z","creator":"adepope","file_size":25109878,"file_name":"2026_Depope_Al_Thesis.pdf","file_id":"22316","date_updated":"2026-07-13T14:52:19Z","content_type":"application/pdf","access_level":"open_access"},{"date_created":"2026-07-13T14:56:41Z","relation":"source_file","checksum":"8ed8fb63f76a695d5b6fec35343f4b90","content_type":"application/zip","access_level":"closed","file_size":1203199939,"file_name":"2026_Depope_Al_Thesis.zip","creator":"adepope","file_id":"22317","date_updated":"2026-07-13T14:56:41Z"}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","language":[{"iso":"eng"}],"fulldoi":"https://doi.org/10.15479/AT-ISTA-22258","date_published":"2026-07-11T00:00:00Z","doi":"10.15479/AT-ISTA-22258","ddc":["576","610","006"],"oa_version":"Published Version","abstract":[{"lang":"eng","text":"Uncovering the genetic architecture of complex traits and pinpointing causal molecular drivers require the ability to distinguish true signals from noise within massive, high-dimensional omics datasets. To extract meaningful biological insights from these datasets, such as identifying causal genetic variants and proteins, scalable and accurate inference methods are essential. To this end, this thesis develops novel Bayesian inference frameworks based on Vector Approximate Message Passing and demonstrates their effectiveness in the modeling of disease onset times and quantitative physical and clinical measures.\r\n\r\nFirst, we introduce gVAMP, a Bayesian framework tailored for Genome-Wide Association Studies that enables the joint modeling of quantitative complex traits across millions of genetic variants. gVAMP demonstrates superior accuracy in variable selection and out-of-sample polygenic risk prediction compared to state-of-the-art approaches. We model human height using 17 million whole-genome sequence variants from the UK Biobank, incorporating a vast number of rare variants and revealing novel associations. gVAMP achieves a prediction accuracy of approximately 46% for human height, representing the highest reported performance for this trait to date. \r\n\r\nSecond, we present vampW, a Bayesian framework for survival analysis applied to proteomic data. By effectively handling right-censoring and complex protein dependencies within the UK Biobank Pharma Proteomics Project dataset, vampW identifies 219 protein associations across 24 disease outcomes, the majority of which are not among the top marginal discoveries. We further adjust protein levels for exponential age effects, yielding 1,308 associations and highlighting the sensitivity of the analysis to the chosen age-correction methodology. Finally, vampW improves upon the variable selection capabilities of the commonly used (penalized) variants of the Cox proportional hazards model and delivers state-of-the-art out-of-sample prediction of disease onset times.\r\n\r\nCollectively, these methods provide powerful tools for dissecting the genetic architecture of complex traits and the proteomic drivers of disease onset. Furthermore, by delivering accurate polygenic risk scores and precise predictions of onset times, this work advances the capabilities of personalized medicine and clinical risk stratification."}],"acknowledged_ssus":[{"_id":"ScienComp"}],"degree_awarded":"PhD","title":"From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics","das_tickbox":"1","department":[{"_id":"GradSch"},{"_id":"MaRo"},{"_id":"MaMo"}],"supervisor":[{"id":"E5D42276-F5DA-11E9-8E24-6303E6697425","full_name":"Robinson, Matthew Richard","first_name":"Matthew Richard","last_name":"Robinson","orcid":"0000-0001-8982-8813"},{"orcid":"0000-0002-3242-7020","last_name":"Mondelli","id":"27EB676C-8706-11E9-9510-7717E6697425","full_name":"Mondelli, Marco","first_name":"Marco"}],"alternative_title":["ISTA Thesis"],"page":"169","corr_author":"1","day":"11","related_material":{"record":[{"relation":"part_of_dissertation","id":"21488","status":"public"}]},"date_created":"2026-07-10T13:27:20Z","project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"},{"_id":"911e6d1f-16d5-11f0-9cad-c5c68c6a1cdf","grant_number":"101161364","name":"Inference in High Dimensions: Light-speed Algorithms and Information Limits"},{"name":"Improving estimation and prediction of common complex disease risk","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","grant_number":"PCEGP3_181181"}],"author":[{"last_name":"Depope","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830","full_name":"Depope, Al","first_name":"Al"}],"OA_place":"publisher","type":"dissertation"},{"issue":"5","publication_identifier":{"eissn":["2666-979X"]},"status":"public","article_number":"101162","publication_status":"published","date_updated":"2026-07-28T07:08:15Z","pmid":1,"language":[{"iso":"eng"}],"fulldoi":"https://doi.org/10.1016/j.xgen.2026.101162","scopus_import":"1","date_published":"2026-05-13T00:00:00Z","dataavailabilitystatement":"This project uses the UK Biobank data under project number 35520. UK Biobank genotypic and phenotypic data are available through a formal request at http://www.ukbiobank.ac.uk. It also uses genotypic and phenotypic data from the All of Us study, which are also available through a formal request at https://www.researchallofus.org/data-tools/data-access/. All summary statistic estimates are released publicly on Dryad: https://doi.org/10.5061/dryad.cz8w9gjjc.\r\n•\r\nThe gVAMP code developed in this work is open source and has been deposited on GitHub, where it is publicly available at https://github.com/medical-genomics-group/gVAMP, and the code used to generate the data in the manuscript are available from Zenodo https://doi.org/10.5281/zenodo.17935521. The URLs of other software used are listed in the key resources table.","file":[{"date_created":"2026-07-28T07:06:26Z","checksum":"6b59686f8d9733add4f23d23f3dd9df0","success":1,"relation":"main_file","access_level":"open_access","content_type":"application/pdf","date_updated":"2026-07-28T07:06:26Z","file_id":"22596","file_size":3736705,"file_name":"2026_CellGenomics_Depope.pdf","creator":"dernst"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","has_accepted_license":"1","oa":1,"external_id":{"pmid":["41713425"]},"file_date_updated":"2026-07-28T07:06:26Z","acknowledgement":"We thank Malgorzata Borczyk for creating the gene burden scores. We thank Robin Beaumont, Amedeo Roberto Esposito, Gareth Hawkes, Philip Schniter, Matthew Stephens, Pragya Sur, Peter Visscher, Michael Weedon, and Harry Wright for providing valuable suggestions and comments on earlier versions of the work. This project was funded by a Lopez-Loreta Prize to M.M., an SNSF Eccellenza Grant to M.R.R. (PCEGP3-181181), an ERC Starting Grant to M.M. (INF2, project number 101161364), and core funding from ISTA. High-performance computing was supported by the Scientific Service Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp). We would like to acknowledge the participants and investigators of the UK Biobank study. We gratefully acknowledge the All of Us participants for their contributions, without whom this research would not have been possible. We also thank the National Institutes of Health All of Us Research Program for making available the participant data (and/or samples and/or cohort) examined in this study.","volume":6,"article_processing_charge":"Yes","citation":{"mla":"Depope, Al, et al. “Joint Modeling of Whole-Genome Sequencing Data for Human Height via Approximate Message Passing.” <i>Cell Genomics</i>, vol. 6, no. 5, 101162, Elsevier, 2026, doi:<a href=\"https://doi.org/10.1016/j.xgen.2026.101162\">10.1016/j.xgen.2026.101162</a>.","chicago":"Depope, Al, Jakub Bajzik, Marco Mondelli, and Matthew Richard Robinson. “Joint Modeling of Whole-Genome Sequencing Data for Human Height via Approximate Message Passing.” <i>Cell Genomics</i>. Elsevier, 2026. <a href=\"https://doi.org/10.1016/j.xgen.2026.101162\">https://doi.org/10.1016/j.xgen.2026.101162</a>.","ieee":"A. Depope, J. Bajzik, M. Mondelli, and M. R. Robinson, “Joint modeling of whole-genome sequencing data for human height via approximate message passing,” <i>Cell Genomics</i>, vol. 6, no. 5. Elsevier, 2026.","apa":"Depope, A., Bajzik, J., Mondelli, M., &#38; Robinson, M. R. (2026). Joint modeling of whole-genome sequencing data for human height via approximate message passing. <i>Cell Genomics</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.xgen.2026.101162\">https://doi.org/10.1016/j.xgen.2026.101162</a>","short":"A. Depope, J. Bajzik, M. Mondelli, M.R. Robinson, Cell Genomics 6 (2026).","ista":"Depope A, Bajzik J, Mondelli M, Robinson MR. 2026. Joint modeling of whole-genome sequencing data for human height via approximate message passing. Cell Genomics. 6(5), 101162.","ama":"Depope A, Bajzik J, Mondelli M, Robinson MR. Joint modeling of whole-genome sequencing data for human height via approximate message passing. <i>Cell Genomics</i>. 2026;6(5). doi:<a href=\"https://doi.org/10.1016/j.xgen.2026.101162\">10.1016/j.xgen.2026.101162</a>"},"_id":"21488","publication":"Cell Genomics","year":"2026","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","image":"/images/cc_by_nc_nd.png","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)"},"month":"05","publisher":"Elsevier","quality_controlled":"1","OA_type":"gold","department":[{"_id":"MaMo"},{"_id":"MaRo"}],"das_tickbox":"1","title":"Joint modeling of whole-genome sequencing data for human height via approximate message passing","abstract":[{"text":"Human height is a model for the genetic analysis of complex traits, and recent studies suggest the presence of thousands of common genetic variant associations and hundreds of low-frequency/rare variants. Here, we develop a new algorithmic paradigm based on approximate message passing (genomic vector approximate message passing [gVAMP]) for identifying DNA sequence variants associated with complex traits and common diseases in large-scale whole-genome sequencing (WGS) data. We show that gVAMP accurately localizes associations to variants with the correct frequency and position in the DNA, outperforming existing fine-mapping methods in selecting the appropriate genetic variants within WGS data. We then apply gVAMP to jointly model the relationship of tens of millions of WGS variants with human height in hundreds of thousands of UK Biobank individuals. We identify 59 rare variants and gene burden scores alongside many hundreds of DNA regions containing common variant associations and show that understanding the genetic basis of complex traits will require the joint analysis of hundreds of millions of variables measured on millions of people. The polygenic risk scores obtained from gVAMP have high accuracy (including a prediction accuracy of ∼46% for human height) and outperform current methods for downstream tasks such as mixed linear model association testing across 13 UK Biobank traits. In conclusion, gVAMP offers a scalable foundation for a wider range of analyses in WGS data.","lang":"eng"}],"DOAJ_listed":"1","supplementarymaterial":"yes","oa_version":"Published Version","ddc":["000","570"],"article_type":"original","doi":"10.1016/j.xgen.2026.101162","intvolume":"         6","type":"journal_article","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","researchdata_availability":"yes","project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"},{"grant_number":"101161364","_id":"911e6d1f-16d5-11f0-9cad-c5c68c6a1cdf","name":"Inference in High Dimensions: Light-speed Algorithms and Information Limits"},{"grant_number":"PCEGP3_181181","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","name":"Improving estimation and prediction of common complex disease risk"}],"author":[{"first_name":"Al","full_name":"Depope, Al","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830","last_name":"Depope"},{"last_name":"Bajzik","full_name":"Bajzik, Jakub","id":"b995e25b-8c4b-11ed-a6d8-f71b7bcd6122","first_name":"Jakub"},{"last_name":"Mondelli","orcid":"0000-0002-3242-7020","full_name":"Mondelli, Marco","id":"27EB676C-8706-11E9-9510-7717E6697425","first_name":"Marco"},{"full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson"}],"OA_place":"publisher","date_created":"2026-03-23T15:10:03Z","day":"13","related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"22258"}],"link":[{"description":"News on ISTA website","url":"https://ista.ac.at/en/news/big-data-and-human-height/","relation":"press_release"}]},"corr_author":"1"},{"publication_status":"epub_ahead","article_number":"115736","status":"public","publication_identifier":{"eissn":["1879-2456"],"issn":["0956-053X"]},"pmid":1,"date_updated":"2026-08-03T06:35:45Z","volume":224,"article_processing_charge":"Yes (via OA deal)","citation":{"short":"N. Depope, A. Depope, V.M. Archodoulaki, A. Dabrowska, B. Lendl, A. Mautner, W. Ipsmiller, A. Bartl, Waste Management 224 (2026).","apa":"Depope, N., Depope, A., Archodoulaki, V. M., Dabrowska, A., Lendl, B., Mautner, A., … Bartl, A. (2026). DES formulation for recycling synthetic fibre textile waste containing elastane. <i>Waste Management</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.wasman.2026.115736\">https://doi.org/10.1016/j.wasman.2026.115736</a>","ista":"Depope N, Depope A, Archodoulaki VM, Dabrowska A, Lendl B, Mautner A, Ipsmiller W, Bartl A. 2026. DES formulation for recycling synthetic fibre textile waste containing elastane. Waste Management. 224, 115736.","ama":"Depope N, Depope A, Archodoulaki VM, et al. DES formulation for recycling synthetic fibre textile waste containing elastane. <i>Waste Management</i>. 2026;224. doi:<a href=\"https://doi.org/10.1016/j.wasman.2026.115736\">10.1016/j.wasman.2026.115736</a>","chicago":"Depope, Nika, Al Depope, Vasiliki Maria Archodoulaki, Alicja Dabrowska, Bernhard Lendl, Andreas Mautner, Wolfgang Ipsmiller, and Andreas Bartl. “DES Formulation for Recycling Synthetic Fibre Textile Waste Containing Elastane.” <i>Waste Management</i>. Elsevier, 2026. <a href=\"https://doi.org/10.1016/j.wasman.2026.115736\">https://doi.org/10.1016/j.wasman.2026.115736</a>.","mla":"Depope, Nika, et al. “DES Formulation for Recycling Synthetic Fibre Textile Waste Containing Elastane.” <i>Waste Management</i>, vol. 224, 115736, Elsevier, 2026, doi:<a href=\"https://doi.org/10.1016/j.wasman.2026.115736\">10.1016/j.wasman.2026.115736</a>.","ieee":"N. Depope <i>et al.</i>, “DES formulation for recycling synthetic fibre textile waste containing elastane,” <i>Waste Management</i>, vol. 224. Elsevier, 2026."},"acknowledgement":"The financial support by the Austrian Federal Ministry of Economy, Energy and Tourism, the National Foundation for Research, Technology and Development and the Christian Doppler Research Association is gratefully acknowledged. The authors acknowledge “Open Access Funding by TU Wien” for financial support through its Open Access Funding Program.\r\nSpecial thanks are extended to EREMA Group GmbH, SALESIANER MIETTEX GmbH and Starlinger & Co GmbH for their material support and valuable input throughout the development of this study.","external_id":{"pmid":["42456595"]},"oa":1,"has_accepted_license":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","dataavailabilitystatement":"Data will be made available on request.","language":[{"iso":"eng"}],"fulldoi":"https://doi.org/10.1016/j.wasman.2026.115736","date_published":"2026-07-15T00:00:00Z","scopus_import":"1","publisher":"Elsevier","month":"07","year":"2026","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"publication":"Waste Management","_id":"22614","abstract":[{"lang":"eng","text":"This study presents a sustainable and non-destructive recycling strategy for synthetic fibres, specifically polyethylene terephthalate (PET), polyamide 6 (PA6), polyamide 66 (PA66), and elastane (EL). Two deep eutectic solvents (DESs) were synthesised, enabling selective dissolution of EL without degrading the surrounding polymer matrices. The dissolved EL phase was subsequently recovered by centrifugation, demonstrating its potential for material reuse. Comprehensive characterisation confirmed that the process preserves fibre integrity. SEM imaging revealed no detectable changes in fibre morphology, while DSC and TGA analyses indicated that the thermal properties of both EL and synthetic polymers were preserved. ATR-FTIR spectroscopy verified the absence of measurable chemical modifications. Mechanical testing showed that solvent treatment did not compromise the tensile performance of PET, PA6, or PA66 fibres, confirming their suitability for fibre-to-fibre recycling.\r\nThe recovered fibres were successfully re-extruded into continuous filaments, confirming their melt processability and enabling potential applications in textile yarns, technical fibres, nonwovens, and polymer components via melt-spinning or moulding. These results demonstrate that the proposed DES-based method offers a promising approach for the circular recycling of complex fibre blends and provides a viable pathway towards industrial implementation."}],"das_tickbox":"1","title":"DES formulation for recycling synthetic fibre textile waste containing elastane","department":[{"_id":"MaRo"}],"quality_controlled":"1","OA_type":"hybrid","doi":"10.1016/j.wasman.2026.115736","article_type":"original","PlanS_conform":"1","oa_version":"Published Version","ddc":["540"],"supplementarymaterial":"yes","date_created":"2026-08-02T22:01:51Z","OA_place":"publisher","author":[{"first_name":"Nika","full_name":"Depope, Nika","last_name":"Depope"},{"last_name":"Depope","first_name":"Al","full_name":"Depope, Al","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830"},{"first_name":"Vasiliki Maria","full_name":"Archodoulaki, Vasiliki Maria","last_name":"Archodoulaki"},{"last_name":"Dabrowska","first_name":"Alicja","full_name":"Dabrowska, Alicja"},{"last_name":"Lendl","full_name":"Lendl, Bernhard","first_name":"Bernhard"},{"full_name":"Mautner, Andreas","first_name":"Andreas","last_name":"Mautner"},{"full_name":"Ipsmiller, Wolfgang","first_name":"Wolfgang","last_name":"Ipsmiller"},{"first_name":"Andreas","full_name":"Bartl, Andreas","last_name":"Bartl"}],"researchdata_availability":"upon request","intvolume":"       224","type":"journal_article","main_file_link":[{"url":"https://doi.org/10.1016/j.wasman.2026.115736","open_access":"1"}],"day":"15"},{"day":"01","author":[{"first_name":"Nika","full_name":"Depope, Nika","last_name":"Depope"},{"last_name":"Depope","first_name":"Al","full_name":"Depope, Al","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830"},{"last_name":"Archodoulaki","full_name":"Archodoulaki, Vasiliki Maria","first_name":"Vasiliki Maria"},{"last_name":"Ipsmiller","full_name":"Ipsmiller, Wolfgang","first_name":"Wolfgang"},{"last_name":"Bartl","first_name":"Andreas","full_name":"Bartl, Andreas"}],"OA_place":"publisher","type":"journal_article","intvolume":"       208","isi":1,"date_created":"2025-10-19T22:01:31Z","PlanS_conform":"1","oa_version":"Published Version","ddc":["572"],"doi":"10.1016/j.wasman.2025.115177","article_type":"original","quality_controlled":"1","OA_type":"hybrid","abstract":[{"text":"Global fibre production has expanded rapidly, with polyester and cotton dominating, significantly contributing to textile waste and increasing demand for sustainable solutions. This study presents innovative method to recycle polyester/cotton (PET/CO) blends using hydrophobic deep eutectic solvents (DESs), eliminating the need for toxic chemicals while achieving high dissolution yields. PET was completely dissolved within 5 min, substantially outperforming state-of-the-art methods and facilitating the efficient and selective recovery of both components, PET (97%) and CO (100%). SEM imaging confirmed no morphological changes in cotton fibres after treatment. The thermal stability of the recovered materials was validated using DSC and TGA analyses, while ATR-FTIR spectroscopy indicated no chemical changes. Mechanical testing confirmed recovered cotton’s tenacity and elongation are within expected ranges despite showing a decrease of 28% in tenacity and 34% in elongation. Hence, the proposed process provides an efficient and sustainable recycling solution for PET/CO blends, retaining both polymers in a condition similar to virgin materials used in textile manufacturing with minimal processing time.","lang":"eng"}],"title":"Deep eutectic solvent as a solution for polyester/cotton textile recycling","department":[{"_id":"MaRo"}],"publication":"Waste Management","_id":"20491","publisher":"Elsevier","month":"11","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"year":"2025","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file":[{"date_created":"2025-10-20T10:57:36Z","checksum":"c232aae0ef7ed653813a835013f25bae","success":1,"relation":"main_file","access_level":"open_access","content_type":"application/pdf","date_updated":"2025-10-20T10:57:36Z","file_id":"20501","creator":"dernst","file_name":"2025_WasteMgmt_Depope.pdf","file_size":4511527}],"fulldoi":"https://doi.org/10.1016/j.wasman.2025.115177","date_published":"2025-11-01T00:00:00Z","language":[{"iso":"eng"}],"scopus_import":"1","volume":208,"article_processing_charge":"Yes (via OA deal)","citation":{"ieee":"N. Depope, A. Depope, V. M. Archodoulaki, W. Ipsmiller, and A. Bartl, “Deep eutectic solvent as a solution for polyester/cotton textile recycling,” <i>Waste Management</i>, vol. 208. Elsevier, 2025.","mla":"Depope, Nika, et al. “Deep Eutectic Solvent as a Solution for Polyester/Cotton Textile Recycling.” <i>Waste Management</i>, vol. 208, 115177, Elsevier, 2025, doi:<a href=\"https://doi.org/10.1016/j.wasman.2025.115177\">10.1016/j.wasman.2025.115177</a>.","chicago":"Depope, Nika, Al Depope, Vasiliki Maria Archodoulaki, Wolfgang Ipsmiller, and Andreas Bartl. “Deep Eutectic Solvent as a Solution for Polyester/Cotton Textile Recycling.” <i>Waste Management</i>. Elsevier, 2025. <a href=\"https://doi.org/10.1016/j.wasman.2025.115177\">https://doi.org/10.1016/j.wasman.2025.115177</a>.","ama":"Depope N, Depope A, Archodoulaki VM, Ipsmiller W, Bartl A. Deep eutectic solvent as a solution for polyester/cotton textile recycling. <i>Waste Management</i>. 2025;208. doi:<a href=\"https://doi.org/10.1016/j.wasman.2025.115177\">10.1016/j.wasman.2025.115177</a>","ista":"Depope N, Depope A, Archodoulaki VM, Ipsmiller W, Bartl A. 2025. Deep eutectic solvent as a solution for polyester/cotton textile recycling. Waste Management. 208, 115177.","apa":"Depope, N., Depope, A., Archodoulaki, V. M., Ipsmiller, W., &#38; Bartl, A. (2025). Deep eutectic solvent as a solution for polyester/cotton textile recycling. <i>Waste Management</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.wasman.2025.115177\">https://doi.org/10.1016/j.wasman.2025.115177</a>","short":"N. Depope, A. Depope, V.M. Archodoulaki, W. Ipsmiller, A. Bartl, Waste Management 208 (2025)."},"acknowledgement":"This study was conducted at the Josef Ressel Centre for Recovery Strategies of Textiles which is funded by the Christian Doppler Research Society on behalf of the Austrian Federal Ministry of Labor and Economic Affairs and the National Foundation for Research, Technology. The authors acknowledge “Open Access Funding by TU Wien” for financial support through its Open Access Funding Program.\r\nSpecial thanks are extended to EREMA Group GmbH, SALESIANER MIETTEX GmbH and Starlinger & Co GmbH for their material support and valuable input throughout the development of this study.","file_date_updated":"2025-10-20T10:57:36Z","external_id":{"isi":["001594629200003"],"pmid":["41066876"]},"has_accepted_license":"1","oa":1,"date_updated":"2025-12-01T12:58:17Z","pmid":1,"publication_identifier":{"eissn":["1879-2456"],"issn":["0956-053X"]},"article_number":"115177","publication_status":"published","status":"public"},{"publisher":"IEEE","month":"04","year":"2024","publication":"2024 IEEE International Conference on Acoustics, Speech, and Signal Processing","_id":"17147","citation":{"ama":"Depope A, Mondelli M, Robinson MR. Inference of genetic effects via approximate message passing. In: <i>2024 IEEE International Conference on Acoustics, Speech, and Signal Processing</i>. IEEE; 2024:13151-13155. doi:<a href=\"https://doi.org/10.1109/ICASSP48485.2024.10447198\">10.1109/ICASSP48485.2024.10447198</a>","ista":"Depope A, Mondelli M, Robinson MR. 2024. Inference of genetic effects via approximate message passing. 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP: International Conference on Acoustics, Speech and Signal Processing, 13151–13155.","short":"A. Depope, M. Mondelli, M.R. Robinson, in:, 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, 2024, pp. 13151–13155.","apa":"Depope, A., Mondelli, M., &#38; Robinson, M. R. (2024). Inference of genetic effects via approximate message passing. In <i>2024 IEEE International Conference on Acoustics, Speech, and Signal Processing</i> (pp. 13151–13155). Seoul, Korea: IEEE. <a href=\"https://doi.org/10.1109/ICASSP48485.2024.10447198\">https://doi.org/10.1109/ICASSP48485.2024.10447198</a>","ieee":"A. Depope, M. Mondelli, and M. R. Robinson, “Inference of genetic effects via approximate message passing,” in <i>2024 IEEE International Conference on Acoustics, Speech, and Signal Processing</i>, Seoul, Korea, 2024, pp. 13151–13155.","chicago":"Depope, Al, Marco Mondelli, and Matthew Richard Robinson. “Inference of Genetic Effects via Approximate Message Passing.” In <i>2024 IEEE International Conference on Acoustics, Speech, and Signal Processing</i>, 13151–55. IEEE, 2024. <a href=\"https://doi.org/10.1109/ICASSP48485.2024.10447198\">https://doi.org/10.1109/ICASSP48485.2024.10447198</a>.","mla":"Depope, Al, et al. “Inference of Genetic Effects via Approximate Message Passing.” <i>2024 IEEE International Conference on Acoustics, Speech, and Signal Processing</i>, IEEE, 2024, pp. 13151–55, doi:<a href=\"https://doi.org/10.1109/ICASSP48485.2024.10447198\">10.1109/ICASSP48485.2024.10447198</a>."},"article_processing_charge":"No","acknowledgement":"This work was supported by a Lopez-Loreta Prize to MM, an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and core funding from ISTA. The authors thank Philip Schniter, Matthew Stephens and Pragya Sur for valuable suggestions on an early version of the work. The authors acknowledge the participants and investigators of the UK Biobank study. High-performance\r\ncomputing was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp).","conference":{"name":"ICASSP: International Conference on Acoustics, Speech and Signal Processing","location":"Seoul, Korea","end_date":"2024-04-19","start_date":"2024-04-14"},"external_id":{"isi":["001396233806078"]},"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2024-04-19T00:00:00Z","fulldoi":"https://doi.org/10.1109/ICASSP48485.2024.10447198","language":[{"iso":"eng"}],"scopus_import":"1","date_updated":"2026-07-13T14:57:55Z","publication_status":"published","status":"public","publication_identifier":{"issn":["1520-6149"],"isbn":["9798350344851"]},"page":"13151-13155","main_file_link":[{"url":"https://openreview.net/forum?id=aQYCDxfZV0","open_access":"1"}],"corr_author":"1","day":"19","isi":1,"date_created":"2024-06-16T22:01:07Z","author":[{"last_name":"Depope","first_name":"Al","full_name":"Depope, Al","id":"0b77531d-dbcd-11ea-9d1d-a8eee0bf3830"},{"last_name":"Mondelli","orcid":"0000-0002-3242-7020","first_name":"Marco","full_name":"Mondelli, Marco","id":"27EB676C-8706-11E9-9510-7717E6697425"},{"first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","orcid":"0000-0001-8982-8813","last_name":"Robinson"}],"OA_place":"repository","project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"},{"name":"Improving estimation and prediction of common complex disease risk","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A","grant_number":"PCEGP3_181181"}],"type":"conference","doi":"10.1109/ICASSP48485.2024.10447198","oa_version":"Submitted Version","acknowledged_ssus":[{"_id":"ScienComp"}],"abstract":[{"text":"Efficient utilization of large-scale biobank data is crucial for inferring the genetic basis of disease and predicting health outcomes from the DNA. Yet we lack efficient, accurate methods that scale to data where electronic health records are linked to whole genome sequence information. To address this issue, our paper develops a new algorithmic paradigm based on Approximate Message Passing (AMP), which is specifically tailored for genomic prediction and association testing. Our method yields comparable out-of-sample prediction accuracy to the state of the art on UK Biobank traits, whilst dramatically improving computational complexity, with a 8x-speed up in the run time. In addition, AMP theory provides a joint association testing framework, which outperforms the currently used REGENIE method, in roughly a third of the compute time. This first, truly large-scale application of the AMP framework lays the foundations for a far wider range of statistical analyses for hundreds of millions of variables measured on millions of people.","lang":"eng"}],"title":"Inference of genetic effects via approximate message passing","department":[{"_id":"MaMo"},{"_id":"MaRo"}],"OA_type":"green","quality_controlled":"1"}]