{"id":14,"date":"2016-04-05T21:59:11","date_gmt":"2016-04-05T21:59:11","guid":{"rendered":"http:\/\/projects.nib.si\/multisight\/?page_id=14"},"modified":"2026-03-24T17:35:27","modified_gmt":"2026-03-24T16:35:27","slug":"summary","status":"publish","type":"page","link":"https:\/\/projects.nib.si\/multisight\/summary\/","title":{"rendered":"Work Programme"},"content":{"rendered":"
    \n
  1. Benchmarking environment
    \n<\/strong>To improve the explainability of the data integration workflow, create benchmarking datasets in two ways: i) experimental (\u201creal life\u201d), and ii) synthetic data. In WP3, the datasets are used to create efficient multi-omic specific analysis pipelines. Hence, synthetic data generated in this work package serve as a gold standard.<\/span>\u00a0<\/span><\/li>\n
  2. Muti-model <\/span>f<\/span>ramework<\/span>\u00a0d<\/span>evelopment\u00a0<\/span>for\u00a0<\/span>reproducible and explainable\u00a0<\/span>multi-omics data\u00a0<\/span>integration
    \n<\/span><\/span><\/strong>Design and implement a framework for reproducible and explainable multi-omics data integration. Using this framework, <\/span>investigate<\/span>\u00a0how preprocessing, sparsity, and various data transformation techniques influences\u00a0<\/span>particular combinations<\/span>\u00a0of data-driven and\/or knowledge-based approaches<\/span>,\u00a0<\/span>among others. Investigate how the principle of <\/span><\/span>divide and conquer<\/span>\u00a0<\/span><\/span>(e.g.<\/span>\u00a0when feature extraction precedes integration<\/span>)<\/span>, influences<\/span>\u00a0steps such as<\/span> classification or clustering. Test robustness of methods to unbalanced data, especially in cases where the number of conditions is higher than number of biological replicates.<\/span><\/span>\u00a0<\/span>\u00a0<\/span><\/li>\n
  3. Biological evaluation of the <\/span>array<\/span>–<\/span>of<\/span>–<\/span>models within developed framework
    \n<\/span><\/span><\/strong>Biological evaluation of<\/span>\u00a0<\/span>the\u00a0<\/span>outputs\u00a0<\/span>of<\/span>\u00a0the arrays of models (developed in WP2)<\/span>\u00a0is crucial for ensuring that the computational outcomes align with known biological principles.<\/span> U<\/span>se existing biological data<\/span>\u00a0(<\/span>from\u00a0<\/span>T<\/span>1.1<\/span>)<\/span>\u00a0and generate hypotheses for wet-lab validation\u00a0<\/span>in order to<\/span>\u00a0<\/span>identify<\/span>\u00a0m<\/span>ost suitable\u00a0<\/span>mode<\/span>l<\/span>\u00a0combination for<\/span>\u00a0experimental scenarios.<\/span>\u00a0<\/span><\/span><\/span><\/span>\u00a0<\/span><\/span><\/span><\/li>\n
  4. Project <\/span>management, data management, communication, dissemination<\/span><\/span>
    \n<\/span><\/strong>Data generated within the project will are organized according to FAIR principles for data management. <\/span><\/span><\/span>All experiments, both wet lab and dry lab, are recorded in real-time in our data management system <\/span><\/span><\/span>pISA-tree<\/span><\/a>.<\/span><\/span>
    \n<\/span><\/span><\/span><\/span><\/span><\/span>
    \n<\/span><\/span><\/strong>\"\"<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Benchmarking environment To improve the explainability of the data integration workflow, create benchmarking datasets in two ways: i) experimental (\u201creal life\u201d), and ii) synthetic data. In WP3, the datasets are used to create efficient multi-omic specific analysis pipelines. Hence, synthetic data generated in this work package serve as a gold standard.\u00a0 Muti-model framework\u00a0development\u00a0for\u00a0reproducible and explainable\u00a0multi-omics […]<\/p>\n","protected":false},"author":31,"featured_media":0,"parent":0,"menu_order":2,"comment_status":"closed","ping_status":"closed","template":"page-fullwidth.php","meta":{"footnotes":""},"class_list":["post-14","page","type-page","status-publish","hentry"],"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/PhacEQ-e","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/projects.nib.si\/multisight\/wp-json\/wp\/v2\/pages\/14","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/projects.nib.si\/multisight\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/projects.nib.si\/multisight\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/projects.nib.si\/multisight\/wp-json\/wp\/v2\/users\/31"}],"replies":[{"embeddable":true,"href":"https:\/\/projects.nib.si\/multisight\/wp-json\/wp\/v2\/comments?post=14"}],"version-history":[{"count":16,"href":"https:\/\/projects.nib.si\/multisight\/wp-json\/wp\/v2\/pages\/14\/revisions"}],"predecessor-version":[{"id":402,"href":"https:\/\/projects.nib.si\/multisight\/wp-json\/wp\/v2\/pages\/14\/revisions\/402"}],"wp:attachment":[{"href":"https:\/\/projects.nib.si\/multisight\/wp-json\/wp\/v2\/media?parent=14"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}