{"id":4453,"date":"2024-01-18T05:54:30","date_gmt":"2024-01-18T05:54:30","guid":{"rendered":"https:\/\/bipress.boku.ac.at\/camda2025\/?page_id=4453"},"modified":"2026-01-21T15:20:36","modified_gmt":"2026-01-21T15:20:36","slug":"the-camda-contest-challenges","status":"publish","type":"page","link":"https:\/\/bipress.boku.ac.at\/camda2025\/the-camda-contest-challenges\/","title":{"rendered":"The CAMDA Contest Challenges"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"4453\" class=\"elementor elementor-4453\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-48cc108 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"48cc108\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-99c485d\" data-id=\"99c485d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-be70deb elementor-widget elementor-widget-heading\" data-id=\"be70deb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.23.0 - 05-08-2024 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">The CAMDA Contest Challenges<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-532b62b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"532b62b\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0b13885\" data-id=\"0b13885\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a8fcce9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a8fcce9\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6b396af\" data-id=\"6b396af\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1a77776 elementor-widget elementor-widget-text-editor\" data-id=\"1a77776\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.23.0 - 05-08-2024 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>For <a href=\"https:\/\/bipress.boku.ac.at\/camda2025\">CAMDA 2025<\/a>, we present:<\/p><ul><li><strong>The\u00a0<a href=\"https:\/\/bipress.boku.ac.at\/camda2025\/the-camda-contest-challenges\/#privacy-challenge\">Health Privacy<\/a>\u00a0Challenge<\/strong> presents an interactive platform for achieving trust and robustness in the generation of privacy-preserving synthetic <em>gene expression<\/em> datasets. Join us as either as a <strong>Blue Team<\/strong> defending or a <strong>Red Team<\/strong> attacking!<\/li><li><span style=\"font-weight: bold\">The <a href=\"https:\/\/bipress.boku.ac.at\/camda2025\/the-camda-contest-challenges\/#synthetic-health-records\">Synthetic Clinical Health Records<\/a><\/span>\u00a0<strong>Challenge<\/strong> provides a rich set of highly realistic <em>Electronic Health Records<\/em> (EHRs) tracing the diagnosis trajectories of diabetic patients, created with dual-adversarial auto-encoders trained on data from 1.2 million real patients in the Population Health Database of the Andalusian Ministry of Health. Predict relevant diabetes endpoints like blindness or cardiopathy from past diagnosis trajectories!<\/li><li><span style=\"font-weight: bold\">The <\/span><a href=\"https:\/\/bipress.boku.ac.at\/camda2025\/the-camda-contest-challenges\/#gut-microbiome\"><span style=\"font-weight: bold\">Gut Microbiome Health Index<\/span><\/a><strong>\u00a0Challenge<\/strong>\u00a0features hundreds of WMS based taxonomic and functional profiles of healthy and unhealthy individuals. Take advantage of\u00a0the <i>Theater of Activity<\/i> concept and explore microbiome synergies to compete with the best taxonomy based metrics!<\/li><li><strong>The<\/strong>\u00a0<a href=\"https:\/\/bipress.boku.ac.at\/camda2025\/the-camda-contest-challenges\/#amr\"><strong>Anti-Microbial Resistance Prediction<\/strong><\/a> <strong>Challenge <\/strong><span>features thousands of clinical isolate sequences. Predict resistance genes and markers to identify resistant bacteria!<\/span><\/li><\/ul><p>CAMDA encourages an <strong>open contest<\/strong>, where <strong>all<\/strong> analyses of the contest data sets are of interest, <strong>not limited<\/strong> to the questions suggested here. There is an <a href=\"https:\/\/groups.google.com\/forum\/#!forum\/camdaforum\" target=\"_blank\" rel=\"noopener\"><u>online forum<\/u><\/a> for the free discussion of the contest data sets and their analysis, in which you are encouraged to participate.<\/p><p>We look forward to a lively contest!<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1274dd1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1274dd1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5c3a8e3\" data-id=\"5c3a8e3\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d92f673 elementor-widget elementor-widget-text-editor\" data-id=\"d92f673\" data-element_type=\"widget\" id=\"privacy-challenge\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h2>The Health Privacy Challenge<\/h2><table border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"center\"><tbody><tr><td style=\"background-color: #fff7e6;text-align: center\">Participate in the new <strong>Track II<\/strong> (Blue Team<span style=\"font-weight: 400\">\ud83e\uded0<\/span>) featuring:<br \/><strong><a href=\"https:\/\/onek1k.org\">OneK1K<\/a> Single-Cell Gene Expression Dataset!<\/strong><br \/><a href=\"https:\/\/benchmarks.elsa-ai.eu\/?ch=4&amp;com=track2\" target=\"_blank\" rel=\"noopener\">Visit ELSA Benchmark platform for details and to access the dataset.<\/a><\/td><\/tr><\/tbody><\/table><p>\u00a0<\/p><p><b>\ud83d\udce2 <\/b><span style=\"font-weight: 400\">Computational health research is centered on <strong>sensitive health-care data<\/strong>, including genomic, medical and phenotypic data. Progress in the field hinges on the ability to access these data to advance health care using analytical innovations, while simultaneously ensuring that sensitive information of data subjects is not disclosed.\u00a0<\/span><\/p><p><span style=\"font-weight: 400\"><strong>Synthetic data generation<\/strong> is one of the well-adopted approaches to enable privacy preservation through generating data points that are consistent with the distribution of the real data. Generative models, such as Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN), can be used for this purpose, allowing to generate synthetic data that maintains the utility of original data while protecting privacy. <strong>However<\/strong>, <strong>the effectiveness of synthetic data generators in biology, and the extent to which they can protect against adversarial attacks, such as membership inference risks, remain underexplored.\u00a0<\/strong><\/span><\/p><p><span style=\"font-weight: 400\"><strong>The Health Privacy Challenge<\/strong>, which is organized in the context of the European Lighthouse on Safe and Secure AI (<strong>ELSA<\/strong>, <\/span><a href=\"https:\/\/elsa-ai.eu\/\"><span style=\"font-weight: 400\">https:\/\/elsa-ai.eu<\/span><\/a><span style=\"font-weight: 400\">), invites participants to advance this field by contributing in a \u201c<strong>Blue Team<\/strong> (\ud83e\uded0) vs <strong>Red Team<\/strong> (\ud83c\udf45)\u201d scheme: <\/span><\/p><ul><li><span style=\"font-weight: 400\">The <strong>blue teams<\/strong> develop <strong>privacy-preserving generative methods<\/strong> to generate synthetic gene expression datasets that are able to balance the biological utility and privacy, <\/span><\/li><li><span style=\"font-weight: 400\">The <strong>red teams<\/strong> assess\u00a0<\/span>the privacy risks that these generative methods might pose by developing novel and effective <strong>membership inference attack (MIA)<\/strong> techniques,<\/li><\/ul><p><span style=\"font-weight: 400\">While both teams explore <strong>robustness and reliability of evaluation metrics<\/strong> in the context of privacy preservation in synthetic biological datasets.<\/span><\/p><p><b>\ud83e\udde9 Challenge Structure: <\/b>The Health Privacy Challenge consists of<strong> two phases<\/strong>, where Blue and Red team members <strong>must participate in benchmark method submissions.<\/strong><br \/><strong>Phase 1:<\/strong><\/p><ul><li>(\ud83e\uded0) Blue teams work towards developing methods that improve the baseline generative methods and generating novel insights into privacy preservation in biological datasets,<\/li><li>(\ud83c\udf45) Red teams launch membership inference attacks (MIA) against the synthetic datasets, generated by the baseline generative methods.<strong>*<\/strong><\/li><\/ul><p><strong>Phase 2:<\/strong><\/p><ul><li>After the end of Phase 1, a set of Blue team solutions will be selected, based on their leaderboard performance as well as novelty of their methods.<\/li><li>(\ud83c\udf45) During Phase 2, in which <strong>only Red teams participate<\/strong>, Red teams will launch MIA <strong>against these selected Blue teams&#8217; solutions.<\/strong><strong><br \/><\/strong><\/li><\/ul><p><b><span style=\"font-weight: 400\"><strong>*<\/strong> MIA aims to re-identify the training data points used to generate synthetic datasets from the original dataset. This re-identification process pertains only to identifying the pseudo-identities within the dataset and <strong>does not, in any way, attempt to re-identify the original donors.<\/strong><\/span><\/b><\/p><p>\u00a0<\/p><p><b>\ud83c\udfa2 Participation: <\/b> In order to <strong>successfully participate in the challenge<\/strong>, the participants must,<\/p><ul><li>Register through <a href=\"https:\/\/benchmarks.elsa-ai.eu\/?ch=4&amp;com=introduction\"><strong>ELSA Benchmark Platform<\/strong> <\/a>to access the challenge datasets and detailed instructions. We recommend you to register using an organizational email if possible.<\/li><li>Submit their methods (codes and relevant files) through the <a href=\"https:\/\/benchmarks.elsa-ai.eu\/?ch=4&amp;com=introduction\"><strong>ELSA Benchmark Platform.<\/strong><\/a><ul><li>(\ud83e\uded0) <strong>Blue teams<\/strong> must participate in benchmark submission by the <strong>Phase 1<\/strong> deadline.<\/li><li>(\ud83c\udf45) Red teams must participate in two benchmark submissions by the <strong>Phase 1<\/strong> and <strong>Phase 2<\/strong> deadlines.<\/li><\/ul><\/li><li>Submit a CAMDA extended abstract that details their benchmark method submissions <strong>during Phase 1 and 2<\/strong> by the CAMDA submission deadline. ( Both teams (\ud83e\uded0,\ud83c\udf45) ).<\/li><\/ul><p>We provide a <a href=\"https:\/\/github.com\/PMBio\/Health-Privacy-Challenge\"><strong>Github Starter Package Repo<\/strong><\/a> for both teams, which includes baseline methods and evaluation metrics, as well as guideline to base their method developments on.<\/p><p><b>\ud83d\uddc2\ufe0f Datasets:\u00a0\u00a0<\/b><span style=\"font-weight: 400\">We re-distribute two open access TCGA bulk RNA-seq datasets in the pre-processed form, which can be accessed from the <\/span><span style=\"font-weight: 400\"><strong>GDC portal (<a href=\"https:\/\/portal.gdc.cancer.gov\/\">portal.gdc.cancer.gov)<\/a><\/strong><\/span><span style=\"font-weight: 400\"> as raw counts. Each donor in the datasets has a single sample.\u00a0<\/span><\/p><ol><li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>TCGA-BRCA:<\/strong> Breast cancer dataset of size &lt;<strong>1,089<\/strong> (donors) x <strong>978<\/strong> (genes)&gt; with five subtypes<\/span><span style=\"font-weight: 400\">, suitable for cancer subtype prediction task;<\/span><\/li><li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>TCGA COMBINED<\/strong>: A collection of ten different cancer tissues of size &lt;<strong>4,323<\/strong> (donors) x <strong>978<\/strong> (genes)&gt;, suitable for cancer tissue-of-origin prediction task.\u00a0<\/span><\/li><\/ol><p>More details about the datasets and <strong>preprocessing steps<\/strong> can be found at <strong><a href=\"https:\/\/benchmarks.elsa-ai.eu\/?ch=4&amp;com=introduction\">ELSA Benchmark Platform<\/a><\/strong> and <strong><a href=\"https:\/\/github.com\/PMBio\/Health-Privacy-Challenge\">Github Starter Package Repo<\/a><\/strong>.<\/p><p><b>\ud83c\udfc6 Evaluation:\u00a0\u00a0<\/b><span style=\"font-weight: 400\">The teams with the best solutions will be determined based on multiple criteria, including,<\/span><\/p><ul><li style=\"font-weight: 400\"><span style=\"font-weight: 400\">\ud83c\udfaf leaderboard ranking,<\/span><\/li><li style=\"font-weight: 400\"><span style=\"font-weight: 400\">\ud83d\udca1 novelty of methods,<\/span><\/li><li style=\"font-weight: 400\"><span style=\"font-weight: 400\">\ud83c\udf31 <\/span><span style=\"font-weight: 400\">generation of novel insights into privacy-preservation in biology.\u00a0<\/span><\/li><\/ul><p><span style=\"font-weight: 400\">Therefore we strongly encourage the participants to submit their CAMDA extended abstracts to be evaluated <strong>even if they might not have achieved a high ranking on the leaderboards.<\/strong> \u00a0<\/span><\/p><p><span style=\"font-weight: 400\">The winners of the blue and red teams will be invited to present their methods at the <strong>CAMDA Conference at ISMB 2025<\/strong>, and will be awarded with travel fellowships sponsored by <a href=\"https:\/\/elsa-ai.eu\"><strong>ELSA<\/strong><\/a>.<\/span><\/p><p><b>\u23f3 Timeline:\u00a0<\/b><\/p><p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-large wp-image-5779\" src=\"https:\/\/bipress.boku.ac.at\/camda2025\/wp-content\/uploads\/sites\/15\/2025\/02\/timeline-1024x541.png\" alt=\"\" width=\"1024\" height=\"541\" srcset=\"https:\/\/bipress.boku.ac.at\/camda2025\/wp-content\/uploads\/sites\/15\/2025\/02\/timeline-1024x541.png 1024w, https:\/\/bipress.boku.ac.at\/camda2025\/wp-content\/uploads\/sites\/15\/2025\/02\/timeline-300x159.png 300w, https:\/\/bipress.boku.ac.at\/camda2025\/wp-content\/uploads\/sites\/15\/2025\/02\/timeline-768x406.png 768w, https:\/\/bipress.boku.ac.at\/camda2025\/wp-content\/uploads\/sites\/15\/2025\/02\/timeline-1536x812.png 1536w, https:\/\/bipress.boku.ac.at\/camda2025\/wp-content\/uploads\/sites\/15\/2025\/02\/timeline-2048x1082.png 2048w, https:\/\/bipress.boku.ac.at\/camda2025\/wp-content\/uploads\/sites\/15\/2025\/02\/timeline-672x355.png 672w, https:\/\/bipress.boku.ac.at\/camda2025\/wp-content\/uploads\/sites\/15\/2025\/02\/timeline-1038x549.png 1038w, https:\/\/bipress.boku.ac.at\/camda2025\/wp-content\/uploads\/sites\/15\/2025\/02\/timeline-480x254.png 480w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/p><p>\u00a0<\/p><p><b>\u00a0<\/b><b>\u00a0<\/b><span style=\"color: #000000;font-family: -webkit-standard;font-size: medium\">\ud83c\udf89<\/span><span style=\"color: #000000;font-family: -webkit-standard;font-size: medium\">\u00a0<\/span><b>Get started:\u00a0<\/b><\/p><ul><li>Please visit the <strong><a href=\"https:\/\/benchmarks.elsa-ai.eu\/?ch=4&amp;com=introduction\">ELSA Benchmark Platform<\/a> <\/strong>to register and to access the datasets. Detailed information about benchmark method submissions can also be found here.<\/li><li>Visit the <a href=\"https:\/\/github.com\/PMBio\/Health-Privacy-Challenge\"><strong>Github Starter Package Repo<\/strong><\/a> to reproduce baseline generative and membership inference methods, and further instructions.<\/li><li>Make sure to connect with us in the <strong><a href=\"https:\/\/groups.google.com\/g\/camda-health-privacy-challenge\">CAMDA Health Privacy Challenge Google Groups<\/a><\/strong> for questions, discussions and to follow the<strong> upcoming announcements!\u00a0<\/strong><\/li><\/ul><p><b><span style=\"font-weight: 400\">We are looking forward to engaging with both <strong>members of the computational biology and the privacy community<\/strong>, and working together to deepen our understanding of privacy in health care. \ud83e\udd17 <\/span><\/b><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-de028b8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"de028b8\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3e002a2\" data-id=\"3e002a2\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8aa2fcd elementor-widget elementor-widget-text-editor\" data-id=\"8aa2fcd\" data-element_type=\"widget\" id=\"synthetic-health-records\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div>\u00a0<\/div><h2>The Synthetic Clinical Health Records (TBC)<\/h2><p><b>UPDATE FOR 2025!<\/b> Please note that we will provide an improved synthetic data set this year. If you are interested, please sign up to the CAMDA google group to receive a message when it gets released!<\/p><p>Although data protection is necessary to preserve patients\u2019 intimacy, privacy regulations are also an obstacle to biomedical research. An interesting alternative is the use of synthetic patients. However, conventional synthetic patients are useless for discovery given that they are built out of known data distributions. Interestingly, Generative Adversarial Networks (GANs) and related developments have emerged as powerful tools to generate synthetic data in a way that captures relationships between the variables produced even if such relationships were previously unknown. GANs became popular in the generation of\u00a0<a href=\"https:\/\/this-person-does-not-exist.com\/en\"><u>highly realistic synthetic pictures<\/u><\/a>\u00a0but have been applied in many fields, including in the generation of synthetic patients with applications such as\u00a0<a href=\"https:\/\/proceedings.mlr.press\/v68\/choi17a\/choi17a.pdf\"><u>medGAN<\/u><\/a>\u00a0and others.<\/p><p>Three datasets of synthetic patients have been subsequently created for this challenge since CAMDA 2023. Both datasets were generated from a real cohort retrieved from the Health Population Database (<a href=\"https:\/\/www.sspa.juntadeandalucia.es\/servicioandaluzdesalud\/profesionales\/sistemas-de-informacion\/base-poblacional-de-salud\" class=\"broken_link\"><u>Base Poblacional de Salud<\/u><\/a>, BPS) at the Andalusian Health System (Spain), by performing a\u00a0<a href=\"https:\/\/github.com\/donalee\/DualAAE-EHR\"><u>Dual Adversarial AutoEncoder<\/u><\/a>\u00a0(DAAE) approach:<\/p><ol><li>The first dataset (1st generation, 2023) was originally created for CAMDA 2023. It includes a list of pathologies for 999,936 synthetic patients ordered by visits, which was generated from a total of 979,308 real diabetes patients. Used visits from real diabetes patients were originally collected till the end of 2019. Additionally all visits feature an age-range (decades) label. This dataset is still available and usable for this challenge, both by itself or combined with the second generation. This first generation can be dowloaded <a href=\"http:\/\/camda2023.bioinf.jku.at\/contest_dataset#synthetic_clinical_health_records\" class=\"broken_link\">here<\/a>.<\/li><li>The second dataset (2nd generation, 2024) includes a new list of pathologies for 999,936 synthetic patients, which were labeled increasing the age resolution to years. This dataset was generated from an extended cohort of 984,414 real diabetes patients. This dataset is provided as it was generated (raw version) as well as after a minor pre-processing to clean up inconsistencies (pre-processed version). The second generation can be downloaded <a href=\"https:\/\/bipress.boku.ac.at\/camda-play\/datasets\/\">here<\/a>.<\/li><li>The third dataset (3rd generation, 2025) includes a new dataset for 999,936 synthetic patients, which were generated extending patient visits till the end of 2022. This extension allows better description of long-term consequences for diabetes, which could results in more accurate endpoints&#8217; predictions. As in the previous generation, the dataset is provided as it was generated (raw version) as well as after a minor pre-processing to clean up inconsistencies (pre-processed version).<\/li><\/ol><p>Two challenges are suggested on both datasets, although any other original analysis you may think will also be welcomed:<\/p><p>1) Finding some strong relationships in diabetes-associated pathologies that allows to\u00a0<strong>predict any pathology<\/strong>\u00a0before this is diagnosed. Some well-known pathological diabetes consequences, which can be considered relevant endpoints to predict, can be: a)\u00a0<strong>Retinopathy<\/strong>\u00a0(Code \u201c703\u201d), b)\u00a0<strong>Chronic kidney disease<\/strong>\u00a0(Code \u201c1401\u201d), c)\u00a0<strong>Ischemic heart disease<\/strong>\u00a0(Code \u201c910\u201d), d)\u00a0<strong>Amputations<\/strong>\u00a0(Code \u201c1999\u201d)<\/p><p>2) Another proposed challenge is the prediction of\u00a0<strong>disease trajectories<\/strong>\u00a0in diabetes patients (see for example:\u00a0<a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/24959948\/\"><u>Jensen et al. Nat Commun. 2014<\/u><\/a>)<\/p><p>Prediction proposals which are submitted with the model trained and the code required to run the model can be tested on the real dataset by the organisers and participate in a collective publication.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8756606 elementor-widget elementor-widget-ekit_wb_4478\" data-id=\"8756606\" data-element_type=\"widget\" data-widget_type=\"ekit_wb_4478.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div style=\"justify-content: center; display:flex\">\n<div class=\"box-blue\" style=\"width: 50%;\">\n<div class=\"container\" style=\"display: flex; flex-direction: column; align-items: center;\">\n\n<span style=\"font-size:64px\">\n  <i class=\"fas fa-cloud-download-alt\"><\/i><\/span>\n\n<div class=\"ml-5\" style=\"display: flex; flex-direction: column; flex-grow: 1; justify-content: center;\">\n<div>\n\n <div class=\"wrap_download wrap_round plugin_wrap\"><p>Please sign up to announcements from the <a href=\"https:\/\/bipress.boku.ac.at\/camda-play\/forums\/forum\/challenge-1-forum\/\">forum<\/a> for alerts.<\/p><p>Please read and accept the data download agreement for access to the <strong>Download Site<\/strong>.<\/p><p>We thank the <a class=\"urlextern\" title=\"https:\/\/www.iarai.ac.at\/\" href=\"http:\/\/www.iarai.org\/\" target=\"_blank\" rel=\"nofollow noopener\">Institute of Advanced Research in Artificial Intelligence (IARAI)<\/a> for its support in the preparation of this Challenge.<\/p><\/div> \n\n<\/div>\n<button type=\"button\" class=\"btn btn-primary\" style=\"display: noneblock\" onclick=\"location.href='..\/datasets'\">Go to download<\/button>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-33c22e2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"33c22e2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d94291e\" data-id=\"d94291e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-587ad39 elementor-widget elementor-widget-text-editor\" data-id=\"587ad39\" data-element_type=\"widget\" id=\"gut-microbiome\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h2 class=\"elementToProof sh-color-black sh-color\">The Gut Microbiome-based Health Index Challenge<\/h2><div>\u00a0<\/div><div class=\"sh-color-black sh-color\">The onset of diseases linked to microbiome health, such as obesity or Inflammatory Bowel Disease (IBD), is continuously on the rise. (<span class=\"sh-color-green sh-color\"><u class=\"sh-color-green sh-color\"><a id=\"OWAb4c01b9d-644f-1a59-d4c2-05d610bf4ddd\" class=\"OWAAutoLink sh-color-green sh-color\" href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2023.12.04.569909v3.full#ref-26\" target=\"_blank\" rel=\"noopener noreferrer\">M\u2019koma, 2013<\/a><\/u><\/span>,\u00a0<span class=\"sh-color-green sh-color\"><u class=\"sh-color-green sh-color\"><a id=\"OWA72e1bd79-c4cb-c228-e3d2-40b64b0bb3d9\" class=\"OWAAutoLink sh-color-green sh-color\" href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2023.12.04.569909v3.full#ref-15\" target=\"_blank\" rel=\"noopener noreferrer\">Hong et al., 2019<\/a><\/u><\/span>). Because the gut microbiome is strongly linked to the functioning of the human body, the ability to evaluate one\u2019s health status based on a stool sample is of high clinical value. Stool is becoming a reasonable alternative to other diagnostic tools \u2013 it can be collected non-invasively and frequently, and is now becoming affordable.<\/div><div>\u00a0<\/div><div class=\"sh-color-black sh-color\"><span class=\"sh-color-black sh-color\">There are a number of approaches to evaluate microbiome health from stool. Alpha diversity is a frequent choice as it is closely related to dysbiosis, and microbiome richness is described as a key component of microbiome health and robustness (<\/span><span class=\"sh-color-green sh-color\"><u class=\"sh-color-green sh-color\"><a id=\"OWA6c53695a-6279-e01c-ce40-c434d06c99ab\" class=\"OWAAutoLink sh-color-green sh-color\" href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2023.12.04.569909v3.full#ref-21\" target=\"_blank\" rel=\"noopener noreferrer\">Li et al., 2022<\/a><\/u><\/span><span class=\"sh-color-black sh-color\">,\u00a0<\/span><span class=\"sh-color-green sh-color\"><u class=\"sh-color-green sh-color\"><a id=\"OWAca19c699-722c-2abd-aefb-fca37fae8e90\" class=\"OWAAutoLink sh-color-green sh-color\" href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2023.12.04.569909v3.full#ref-10\" target=\"_blank\" rel=\"noopener noreferrer\">Gong et al., 2016<\/a><\/u><\/span><span class=\"sh-color-black sh-color\">). The most robust indices to date are the Gut Microbiome Health Index, the\u00a0<\/span><span class=\"sh-color-black sh-color\">GMHI<\/span><span class=\"sh-color-black sh-color\">\u00a0(<\/span><span class=\"sh-color-green sh-color\"><u class=\"sh-color-green sh-color\"><a id=\"OWA9b516870-c35e-ab11-51dd-e282b4e994cd\" class=\"OWAAutoLink sh-color-green sh-color\" href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2023.12.04.569909v3.full#ref-11\" target=\"_blank\" rel=\"noopener noreferrer\">Gupta et al., 2020<\/a><\/u><\/span><span class=\"sh-color-black sh-color\">) and its successor the Gut Microbiome Wellness Index,\u00a0<\/span><span class=\"sh-color-black sh-color\">GMWI2<\/span><span class=\"sh-color-black sh-color\">\u00a0(Chang et al., 2024\u00a0<\/span><span class=\"sh-color-blue sh-color\"><u class=\"sh-color-blue sh-color\"><a id=\"OWA77370119-e3ee-0145-5331-ea98fd2c4d2a\" class=\"OWAAutoLink sh-color-blue sh-color\" href=\"https:\/\/www.nature.com\/articles\/s41467-024-51651-9\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/www.nature.com\/articles\/s41467-024-51651-9<\/a><\/u><\/span><span class=\"sh-color-black sh-color\">\u00a0), as well as the\u00a0<\/span><span class=\"sh-color-black sh-color\">hiPCA<\/span><span class=\"sh-color-black sh-color\">\u00a0(<\/span><span class=\"sh-color-green sh-color\"><u class=\"sh-color-green sh-color\"><a id=\"OWA6f5d424f-a169-5217-7123-70dc3a2fcdc0\" class=\"OWAAutoLink sh-color-green sh-color\" href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2023.12.04.569909v3.full#ref-32\" target=\"_blank\" rel=\"noopener noreferrer\">Zhu et al., 2023<\/a><\/u><\/span><span class=\"sh-color-black sh-color\">). Those indices rely on the presence of\u00a0<i class=\"sh-color-black sh-color\">beneficial<\/i>\u00a0or\u00a0<i class=\"sh-color-black sh-color\">harmful<\/i>\u00a0bacteria, and classify samples based on their relative ratios.<\/span><\/div><div>\u00a0<\/div><div class=\"sh-color-black sh-color\"><span class=\"sh-color-black sh-color\">However, a recently re-visited definition of the microbiome emphasizes the importance of not just the microbiota (a community of microorganisms), but the whole\u00a0<i class=\"sh-color-black sh-color\">Theatre of activity<\/i>,\u00a0<\/span><strong><span class=\"sh-color-black sh-color\">ToA<\/span><\/strong><span class=\"sh-color-black sh-color\">\u00a0(<\/span><span class=\"sh-color-green sh-color\"><u class=\"sh-color-green sh-color\"><a id=\"OWA9e12d4d7-aed5-446c-34e4-8cd7d87067ef\" class=\"OWAAutoLink sh-color-green sh-color\" href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2023.12.04.569909v3.full#ref-4\" target=\"_blank\" rel=\"noopener noreferrer\">Berg et al., 2020<\/a><\/u><\/span><span class=\"sh-color-black sh-color\">). This means that the <strong>microbiome\u00a0<\/strong><\/span><strong><span class=\"sh-color-black sh-color\">functions<\/span><\/strong><span class=\"sh-color-black sh-color\">, and the\u00a0<\/span><strong><span class=\"sh-color-black sh-color\">interactions<\/span><\/strong><span class=\"sh-color-black sh-color\"><strong>\u00a0of the microbiota<\/strong>, are a more accurate representation of microbiome state.<\/span><\/div><div>\u00a0<\/div><div class=\"sh-color-black sh-color\"><strong><span class=\"sh-color-black sh-color\"><i class=\"sh-color-black sh-color\">In this challenge<\/i><\/span><\/strong>, we provide data set with\u00a0<strong><span class=\"sh-color-black sh-color\">4,398 samples<\/span>\u00a0originating from numerous cohorts with various diseases<\/strong> (from the curated MetagenomicsData database, <a class=\"sh-color-blue sh-color\" href=\"https:\/\/www.nature.com\/articles\/nmeth.4468\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/www.nature.com\/articles\/nmeth.4468<\/a>). We include\u00a0<strong><span class=\"sh-color-black sh-color\">precomputed taxonomic profiles<\/span>\u00a0with health predictions<\/strong> made by existing health indices (Shanon entropy on species and functions as well as GMHI and hiPCA). We also extend this by providing<strong>\u00a0<\/strong><span class=\"sh-color-black sh-color\"><strong>functional profiles<\/strong>.<\/span><\/div><div>\u00a0<\/div><div class=\"elementToProof sh-color-black sh-color\">We ask the<strong>\u00a0<span class=\"sh-color-black sh-color\">CAMDA Community<\/span><\/strong>\u00a0to\u00a0<strong><span class=\"sh-color-black sh-color\"><i class=\"sh-color-black sh-color\">develop a gut microbiome-based health index<\/i><\/span><\/strong>\u00a0which will outperform the existing ones ideally by <em>taking advantage of the Theatre of Activity concept.<\/em>\u00a0<span class=\"sh-color-black sh-color\"><strong>This year<\/strong>, however, we would like to put an emphasis on developing novel ways of <strong>combining the taxonomic and functional profiles<\/strong> as well as <strong>exploring synergies<\/strong> between them and different microbiome components.<\/span>\u00a0The classification is a supplementary goal &#8211; the greatest value will be placed on creative perspectives that advance our understanding of the microbiome in health and disease.<\/div>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bac502d elementor-widget elementor-widget-ekit_wb_4478\" data-id=\"bac502d\" data-element_type=\"widget\" data-widget_type=\"ekit_wb_4478.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div style=\"justify-content: center; display:flex\">\n<div class=\"box-blue\" style=\"width: 50%;\">\n<div class=\"container\" style=\"display: flex; flex-direction: column; align-items: center;\">\n\n<span style=\"font-size:64px\">\n  <i class=\"fas fa-cloud-download-alt\"><\/i><\/span>\n\n<div class=\"ml-5\" style=\"display: flex; flex-direction: column; flex-grow: 1; justify-content: center;\">\n<div>\n\n <div class=\"wrap_download wrap_round plugin_wrap\"><p>Please read and accept the data download agreement for access to the <strong>Download Site<\/strong>.<\/p><\/div> \n\n<\/div>\n<button type=\"button\" class=\"btn btn-primary\" style=\"display: noneblock\" onclick=\"location.href='..\/datasets'\">Go to download<\/button>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5cdf8ca elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5cdf8ca\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-006ae89\" data-id=\"006ae89\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-93506a1 elementor-widget elementor-widget-text-editor\" data-id=\"93506a1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"sh-color-black sh-color\">Here we provide a dataset consisting of\u00a0<span class=\"sh-color-black sh-color\">4398 samples<\/span>\u00a0originating from numerous cohorts with various diseases. There are 3 categories of individuals:<\/div><ul class=\"sh-color-black sh-color\"><li class=\"sh-color-black sh-color\"><div class=\"sh-color-black sh-color\">Healthy (category \u201c1\u201d)<\/div><\/li><li class=\"sh-color-black sh-color\"><div class=\"sh-color-black sh-color\">Diseased (category \u201c0\u201d)<\/div><\/li><\/ul><div class=\"sh-color-black sh-color\">The details about specific diseases and cohorts can be found in the metadata file.<\/div><div class=\"sh-color-black sh-color\">We provide 3 files:<\/div><ul class=\"sh-color-black sh-color\"><li class=\"sh-color-black sh-color\"><div class=\"sh-color-black sh-color\"><strong><span class=\"sh-color-black sh-color\">taxonomy.txt<\/span><\/strong>: species-level contribution to the taxonomic profile, calculated using MetaPhlAn<\/div><\/li><li class=\"sh-color-black sh-color\"><div class=\"sh-color-black sh-color\"><strong><span class=\"sh-color-black sh-color\">pathways.txt<\/span><\/strong>: functional profiles of the samples, calculated using HumanN.\u00a0<\/div><\/li><li class=\"sh-color-black sh-color\"><div class=\"sh-color-black sh-color\"><strong><span class=\"sh-color-black sh-color\">metadata.txt<\/span><\/strong>: contains sample names, cohort and diagnosis assignment for each sample, along with scores predicted by the existing taxonomic health indices. Note that higher scores indicate better health for Shannon entropies and GMHI, while worse disease for hiPCA. <\/div><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-01be0e1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"01be0e1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3a11f58\" data-id=\"3a11f58\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-68bd62e elementor-widget elementor-widget-text-editor\" data-id=\"68bd62e\" data-element_type=\"widget\" id=\"amr\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h2>Anti-Microbial Resistance Prediction<\/h2><div><p><strong>Antimicrobial resistance<\/strong> is one of the biggest challenges facing modern medicine. Because the management of COVID-19 was increasingly becoming dependent on pharmacological interventions, there is greater risk for accelerating the evolution and spread of antimicrobial resistance [<a href=\"https:\/\/doi.org\/10.1016\/S2666-5247(21)00039-2\" target=\"_blank\" rel=\"noopener\">Afshinnekoo et al. 2021<\/a>]. A study in a tertiary hospital environment revealed concerning colonisation patterns of microbes during extended periods [<a href=\"https:\/\/doi.org\/10.1038\/s41591-020-0894-4\">Chng et al. 2020<\/a>]. It also highlighted the diversity of antimicrobial resistance gene reservoirs in hospitals that could facilitate the emergence and transmission of new modes of antibiotic resistance and AMR burden in cities in general [<a href=\"https:\/\/doi.org\/10.1016\/j.cell.2021.05.002\" target=\"_blank\" rel=\"noopener\">Danko et al. 2021<\/a>]. This year we would like <span style=\"color: #5fa660;\"><strong>CAMDA Community<\/strong><\/span> to look into <strong>AMR<\/strong> related challenges.<\/p><p><b>This challenge consists<\/b>\u00a0in developing and testing models for predicting antimicrobial resistance (AMR) in\u00a0<b>9\u00a0different bacterial pathogens and 4 drugs<\/b>\u00a0from the\u00a0<a href=\"https:\/\/www.who.int\/news\/item\/27-02-2017-who-publishes-list-of-bacteria-for-which-new-antibiotics-are-urgently-needed\">WHO\u2019s Priority Pathogen List<\/a>. You will be provided with a\u00a0<b>training dataset taken from public databases,\u00a0<\/b>about <span style=\"text-decoration: underline;\">6,000 isolates<\/span> in total, containing both WGS accession numbers for each isolate, as well as the antibiotic susceptibility phenotype. The developed models will then be\u00a0<b>tested on data from another collection<\/b>\u00a0of the same pathogens, over <span style=\"text-decoration: underline;\">5,000 isolates<\/span> in total, for which you will be provided with the genotypes, but not the phenotypes.<\/p><h3><strong><span style=\"color: #0000ff;\">The data sets are now available!<\/span><br \/>The leaderboard will become available soon!<\/strong><\/h3><p><em>Each team will be able to submit <strong>predictions up to 3 times<\/strong> for accuracy evaluation<\/em> on the withheld phenotypes, and the final submission from each team will qualify as the CAMDA challenge submission; the teams with the best submissions will be invited to present their methods at CAMDA conference at ISMB.<\/p><\/div>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-01a5a18 elementor-widget elementor-widget-ekit_wb_4478\" data-id=\"01a5a18\" data-element_type=\"widget\" data-widget_type=\"ekit_wb_4478.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div style=\"justify-content: center; display:flex\">\n<div class=\"box-blue\" style=\"width: 50%;\">\n<div class=\"container\" style=\"display: flex; flex-direction: column; align-items: center;\">\n\n<span style=\"font-size:64px\">\n  <i class=\"fas fa-cloud-download-alt\"><\/i><\/span>\n\n<div class=\"ml-5\" style=\"display: flex; flex-direction: column; flex-grow: 1; justify-content: center;\">\n<div>\n\n <div class=\"wrap_download wrap_round plugin_wrap\"><p>Please read and accept the data download agreement for access to the <strong>Download Site<\/strong>.<\/p><\/div> \n\n<\/div>\n<button type=\"button\" class=\"btn btn-primary\" style=\"display: noneblock\" onclick=\"location.href='..\/datasets'\">Go to download<\/button>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1b59c4b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1b59c4b\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-caa73dc\" data-id=\"caa73dc\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-205a928 elementor-widget elementor-widget-shortcode\" data-id=\"205a928\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><div class='w3eden'><!-- WPDM Link Template: Default Template -->\n\n<div class=\"link-template-default card mb-2\">\n    <div class=\"card-body\">\n        <div class=\"media\">\n            <div class=\"mr-3 img-48\"><img decoding=\"async\" class=\"wpdm_icon\" alt=\"Icon\" src=\"https:\/\/bipress.boku.ac.at\/camda2025\/wp-content\/plugins\/download-manager\/assets\/file-type-icons\/zip.svg\" \/><\/div>\n            <div class=\"media-body\">\n                <h3 class=\"package-title\"><a href='https:\/\/bipress.boku.ac.at\/camda2025\/download\/synthetic-electronic-health-record-trajectories-gen-3\/'>Synthetic Electronic Health Record Trajectories Gen.3<\/a><\/h3>\n                <div class=\"text-muted text-small\"><i class=\"fas fa-copy\"><\/i> 1 file(s) <i class=\"fas fa-hdd ml-3\"><\/i> 29.95 MB<\/div>\n            <\/div>\n            <div class=\"ml-3\">\n                <a class='wpdm-download-link download-on-click btn btn-primary ' rel='nofollow' href='#' data-downloadurl=\"https:\/\/bipress.boku.ac.at\/camda2025\/download\/synthetic-electronic-health-record-trajectories-gen-3\/?wpdmdl=5730&refresh=69e1b427f17ee1776399399\">Download<\/a>\n            <\/div>\n        <\/div>\n    <\/div>\n<\/div>\n\n<\/div><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>The CAMDA Contest Challenges For CAMDA 2025, we present: The\u00a0Health Privacy\u00a0Challenge presents an interactive platform for achieving trust and robustness in the generation of privacy-preserving synthetic gene expression datasets. Join us as either as a Blue Team defending or a Red Team attacking! The Synthetic Clinical Health Records\u00a0Challenge provides a rich set of highly realistic Electronic Health Records (EHRs) tracing [&hellip;]<\/p>\n","protected":false},"author":55,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0},"_links":{"self":[{"href":"https:\/\/bipress.boku.ac.at\/camda2025\/wp-json\/wp\/v2\/pages\/4453"}],"collection":[{"href":"https:\/\/bipress.boku.ac.at\/camda2025\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/bipress.boku.ac.at\/camda2025\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/bipress.boku.ac.at\/camda2025\/wp-json\/wp\/v2\/users\/55"}],"replies":[{"embeddable":true,"href":"https:\/\/bipress.boku.ac.at\/camda2025\/wp-json\/wp\/v2\/comments?post=4453"}],"version-history":[{"count":272,"href":"https:\/\/bipress.boku.ac.at\/camda2025\/wp-json\/wp\/v2\/pages\/4453\/revisions"}],"predecessor-version":[{"id":6296,"href":"https:\/\/bipress.boku.ac.at\/camda2025\/wp-json\/wp\/v2\/pages\/4453\/revisions\/6296"}],"wp:attachment":[{"href":"https:\/\/bipress.boku.ac.at\/camda2025\/wp-json\/wp\/v2\/media?parent=4453"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}