---
OA_place: publisher
_id: '22258'
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
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"
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Al
  full_name: Depope, Al
  id: 0b77531d-dbcd-11ea-9d1d-a8eee0bf3830
  last_name: Depope
citation:
  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>'
  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>'
  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>.'
  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.'
  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.'
  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>.'
  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.'
corr_author: '1'
das_tickbox: '1'
date_created: 2026-07-10T13:27:20Z
date_published: 2026-07-11T00:00:00Z
date_updated: 2026-07-23T05:33:50Z
day: '11'
ddc:
- '576'
- '610'
- '006'
degree_awarded: PhD
department:
- _id: GradSch
- _id: MaRo
- _id: MaMo
doi: 10.15479/AT-ISTA-22258
doi_confirm: '1'
file:
- access_level: open_access
  checksum: 9ab386790515628d957a194f30a7ccb4
  content_type: application/pdf
  creator: adepope
  date_created: 2026-07-13T14:52:19Z
  date_updated: 2026-07-13T14:52:19Z
  file_id: '22316'
  file_name: 2026_Depope_Al_Thesis.pdf
  file_size: 25109878
  relation: main_file
- access_level: closed
  checksum: 8ed8fb63f76a695d5b6fec35343f4b90
  content_type: application/zip
  creator: adepope
  date_created: 2026-07-13T14:56:41Z
  date_updated: 2026-07-13T14:56:41Z
  file_id: '22317'
  file_name: 2026_Depope_Al_Thesis.zip
  file_size: 1203199939
  relation: source_file
file_date_updated: 2026-07-13T14:56:41Z
has_accepted_license: '1'
keyword:
- Approximate Message Passing
- GWAS
- Genomics
- Proteomics
- Survival modeling
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: '169'
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
  name: Prix Lopez-Loretta 2019 - Marco Mondelli
- _id: 911e6d1f-16d5-11f0-9cad-c5c68c6a1cdf
  grant_number: '101161364'
  name: 'Inference in High Dimensions: Light-speed Algorithms and Information Limits'
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
  grant_number: PCEGP3_181181
  name: Improving estimation and prediction of common complex disease risk
publication_identifier:
  issn:
  - 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
  record:
  - id: '21488'
    relation: part_of_dissertation
    status: public
status: public
supervisor:
- first_name: Matthew Richard
  full_name: Robinson, Matthew Richard
  id: E5D42276-F5DA-11E9-8E24-6303E6697425
  last_name: Robinson
  orcid: 0000-0001-8982-8813
- first_name: Marco
  full_name: Mondelli, Marco
  id: 27EB676C-8706-11E9-9510-7717E6697425
  last_name: Mondelli
  orcid: 0000-0002-3242-7020
title: 'From sparse selection to risk prediction: Approximate message passing for
  proteomic survival models and large-scale genomics'
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2026'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '21488'
abstract:
- lang: eng
  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.
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.
article_number: '101162'
article_processing_charge: Yes
article_type: original
author:
- first_name: Al
  full_name: Depope, Al
  id: 0b77531d-dbcd-11ea-9d1d-a8eee0bf3830
  last_name: Depope
- first_name: Jakub
  full_name: Bajzik, Jakub
  id: b995e25b-8c4b-11ed-a6d8-f71b7bcd6122
  last_name: Bajzik
- first_name: Marco
  full_name: Mondelli, Marco
  id: 27EB676C-8706-11E9-9510-7717E6697425
  last_name: Mondelli
  orcid: 0000-0002-3242-7020
- first_name: Matthew Richard
  full_name: Robinson, Matthew Richard
  id: E5D42276-F5DA-11E9-8E24-6303E6697425
  last_name: Robinson
  orcid: 0000-0001-8982-8813
citation:
  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. doi:<a href="https://doi.org/10.1016/j.xgen.2026.101162">10.1016/j.xgen.2026.101162</a>
  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>
  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>. Elsevier, 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.,
    101162.
  mla: Depope, Al, et al. “Joint Modeling of Whole-Genome Sequencing Data for Human
    Height via Approximate Message Passing.” <i>Cell Genomics</i>, 101162, Elsevier,
    2026, doi:<a href="https://doi.org/10.1016/j.xgen.2026.101162">10.1016/j.xgen.2026.101162</a>.
  short: A. Depope, J. Bajzik, M. Mondelli, M.R. Robinson, Cell Genomics (2026).
corr_author: '1'
date_created: 2026-03-23T15:10:03Z
date_published: 2026-02-18T00:00:00Z
date_updated: 2026-07-23T05:33:49Z
day: '18'
ddc:
- '000'
- '570'
department:
- _id: MaMo
- _id: MaRo
doi: 10.1016/j.xgen.2026.101162
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
main_file_link:
- open_access: '1'
  url: https://doi.org/10.1016/j.xgen.2026.101162
month: '02'
oa: 1
oa_version: Published Version
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
  name: Prix Lopez-Loretta 2019 - Marco Mondelli
- _id: 911e6d1f-16d5-11f0-9cad-c5c68c6a1cdf
  grant_number: '101161364'
  name: 'Inference in High Dimensions: Light-speed Algorithms and Information Limits'
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
  grant_number: PCEGP3_181181
  name: Improving estimation and prediction of common complex disease risk
publication: Cell Genomics
publication_identifier:
  eissn:
  - 2666-979X
publication_status: epub_ahead
publisher: Elsevier
quality_controlled: '1'
related_material:
  link:
  - description: News on ISTA website
    relation: press_release
    url: https://ista.ac.at/en/news/big-data-and-human-height/
  record:
  - id: '22258'
    relation: dissertation_contains
    status: public
status: public
title: Joint modeling of whole-genome sequencing data for human height via approximate
  message passing
tmp:
  image: /images/cc_by_nc_nd.png
  legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
  name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
    (CC BY-NC-ND 4.0)
  short: CC BY-NC-ND (4.0)
type: journal_article
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
year: '2026'
...
---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '20491'
abstract:
- lang: eng
  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.
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."
article_number: '115177'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Nika
  full_name: Depope, Nika
  last_name: Depope
- first_name: Al
  full_name: Depope, Al
  id: 0b77531d-dbcd-11ea-9d1d-a8eee0bf3830
  last_name: Depope
- first_name: Vasiliki Maria
  full_name: Archodoulaki, Vasiliki Maria
  last_name: Archodoulaki
- first_name: Wolfgang
  full_name: Ipsmiller, Wolfgang
  last_name: Ipsmiller
- first_name: Andreas
  full_name: Bartl, Andreas
  last_name: Bartl
citation:
  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>
  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>
  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>.
  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.
  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.
  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>.
  short: N. Depope, A. Depope, V.M. Archodoulaki, W. Ipsmiller, A. Bartl, Waste Management
    208 (2025).
date_created: 2025-10-19T22:01:31Z
date_published: 2025-11-01T00:00:00Z
date_updated: 2025-12-01T12:58:17Z
day: '01'
ddc:
- '572'
department:
- _id: MaRo
doi: 10.1016/j.wasman.2025.115177
external_id:
  isi:
  - '001594629200003'
  pmid:
  - '41066876'
file:
- access_level: open_access
  checksum: c232aae0ef7ed653813a835013f25bae
  content_type: application/pdf
  creator: dernst
  date_created: 2025-10-20T10:57:36Z
  date_updated: 2025-10-20T10:57:36Z
  file_id: '20501'
  file_name: 2025_WasteMgmt_Depope.pdf
  file_size: 4511527
  relation: main_file
  success: 1
file_date_updated: 2025-10-20T10:57:36Z
has_accepted_license: '1'
intvolume: '       208'
isi: 1
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '11'
oa: 1
oa_version: Published Version
pmid: 1
publication: Waste Management
publication_identifier:
  eissn:
  - 1879-2456
  issn:
  - 0956-053X
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Deep eutectic solvent as a solution for polyester/cotton textile recycling
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 208
year: '2025'
...
---
OA_place: repository
OA_type: green
_id: '17147'
abstract:
- lang: eng
  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.
acknowledged_ssus:
- _id: ScienComp
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)."
article_processing_charge: No
author:
- first_name: Al
  full_name: Depope, Al
  id: 0b77531d-dbcd-11ea-9d1d-a8eee0bf3830
  last_name: Depope
- first_name: Marco
  full_name: Mondelli, Marco
  id: 27EB676C-8706-11E9-9510-7717E6697425
  last_name: Mondelli
  orcid: 0000-0002-3242-7020
- first_name: Matthew Richard
  full_name: Robinson, Matthew Richard
  id: E5D42276-F5DA-11E9-8E24-6303E6697425
  last_name: Robinson
  orcid: 0000-0001-8982-8813
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>'
  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>'
  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>.
  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.
  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.'
  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>.
  short: A. Depope, M. Mondelli, M.R. Robinson, in:, 2024 IEEE International Conference
    on Acoustics, Speech, and Signal Processing, IEEE, 2024, pp. 13151–13155.
conference:
  end_date: 2024-04-19
  location: Seoul, Korea
  name: 'ICASSP: International Conference on Acoustics, Speech and Signal Processing'
  start_date: 2024-04-14
corr_author: '1'
date_created: 2024-06-16T22:01:07Z
date_published: 2024-04-19T00:00:00Z
date_updated: 2026-07-13T14:57:55Z
day: '19'
department:
- _id: MaMo
- _id: MaRo
doi: 10.1109/ICASSP48485.2024.10447198
external_id:
  isi:
  - '001396233806078'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://openreview.net/forum?id=aQYCDxfZV0
month: '04'
oa: 1
oa_version: Submitted Version
page: 13151-13155
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
  name: Prix Lopez-Loretta 2019 - Marco Mondelli
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
  grant_number: PCEGP3_181181
  name: Improving estimation and prediction of common complex disease risk
publication: 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing
publication_identifier:
  isbn:
  - '9798350344851'
  issn:
  - 1520-6149
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Inference of genetic effects via approximate message passing
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...