{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/inference-of-sparse-networks-with-unobserved","title":"Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks","arxiv_id":"1406.0193","date":"2014-06-01","proceeding":null,"authors":["Nikolai Slavov"],"abstract":"Networks are a unifying framework for modeling complex systems and network\ninference problems are frequently encountered in many fields. Here, I develop\nand apply a generative approach to network inference (RCweb) for the case when\nthe network is sparse and the latent (not observed) variables affect the\nobserved ones. From all possible factor analysis (FA) decompositions explaining\nthe variance in the data, RCweb selects the FA decomposition that is consistent\nwith a sparse underlying network. The sparsity constraint is imposed by a novel\nmethod that significantly outperforms (in terms of accuracy, robustness to\nnoise, complexity scaling, and computational efficiency) Bayesian methods and\nMLE methods using l1 norm relaxation such as K-SVD and l1--based sparse\nprinciple component analysis (PCA). Results from simulated models demonstrate\nthat RCweb recovers exactly the model structures for sparsity as low (as\nnon-sparse) as 50% and with ratio of unobserved to observed variables as high\nas 2. RCweb is robust to noise, with gradual decrease in the parameter ranges\nas the noise level increases.","url_abs":"http://arxiv.org/abs/1406.0193v1","url_pdf":"http://arxiv.org/pdf/1406.0193v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"inference-of-sparse-networks-with-unobserved","repo_url":"https://github.com/nslavov/RCweb","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}