{"id":2763,"date":"2019-02-28T16:58:00","date_gmt":"2019-02-28T16:58:00","guid":{"rendered":"https:\/\/clinomic.ai\/?p=2763"},"modified":"2023-10-05T14:38:26","modified_gmt":"2023-10-05T14:38:26","slug":"an-adaptive-learning-approach-to-parameter-estimation-for-hybrid-petri-nets-in-systems-biology","status":"publish","type":"post","link":"https:\/\/www.clinomic.ai\/de\/2019\/02\/28\/an-adaptive-learning-approach-to-parameter-estimation-for-hybrid-petri-nets-in-systems-biology\/","title":{"rendered":"An Adaptive Learning Approach To Parameter Estimation For Hybrid Petri Nets In Systems Biology"},"content":{"rendered":"\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/clinomic.ai\/wp-content\/uploads\/2019\/02\/nerve-cell-2213009_1920-1-1024x576.jpg\" alt=\"\" class=\"wp-image-2782\"\/><\/figure><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Petri nets (HPNs) that can model biological systems. In particular, based on a state space formulation we develop a decisionaided adaptive gradient descent (DAAGD) algorithm capable of cost-effectively estimating the parameters used in an HPN model. Contrary to standard gradient descent techniques, the DAAGD algorithm does not require prior knowledge, i.e., information about the discrete transitions\u2019 firing instants. Simulations of a gene regulatory network assess the performance of the proposed DAAGD algorithm against standard gradient descent algorithms with full, imperfect and no prior knowledge.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Request our conference paper via our contact form!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Petri nets (HPNs) that can model biological systems. In particular, based on a state space formulation we develop a decisionaided adaptive gradient descent (DAAGD) algorithm capable of cost-effectively estimating the parameters used in an HPN model. Contrary to standard gradient descent techniques, the DAAGD algorithm does not require prior knowledge, i.e., information about the discrete [&#8230;]<\/p>\n<p><a class=\"btn btn-secondary understrap-read-more-link\" href=\"https:\/\/www.clinomic.ai\/de\/2019\/02\/28\/an-adaptive-learning-approach-to-parameter-estimation-for-hybrid-petri-nets-in-systems-biology\/\">Weiterlesen&#8230;<\/a><\/p>\n","protected":false},"author":4,"featured_media":3108,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"iawp_total_views":22,"footnotes":"","_et_pb_custom_css":""},"categories":[10],"tags":[256],"class_list":["post-2763","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications","tag-ailab"],"acf":[],"_et_pb_custom_css":"","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>An Adaptive Learning Approach To Parameter Estimation For Hybrid Petri Nets In Systems Biology - Clinomic<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.clinomic.ai\/2019\/02\/28\/an-adaptive-learning-approach-to-parameter-estimation-for-hybrid-petri-nets-in-systems-biology\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"An Adaptive Learning Approach To Parameter Estimation For Hybrid Petri Nets In Systems Biology - Clinomic\" \/>\n<meta property=\"og:description\" content=\"Petri nets (HPNs) that can model biological systems. In particular, based on a state space formulation we develop a decisionaided adaptive gradient descent (DAAGD) algorithm capable of cost-effectively estimating the parameters used in an HPN model. Contrary to standard gradient descent techniques, the DAAGD algorithm does not require prior knowledge, i.e., information about the discrete [...]Weiterlesen...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.clinomic.ai\/2019\/02\/28\/an-adaptive-learning-approach-to-parameter-estimation-for-hybrid-petri-nets-in-systems-biology\/\" \/>\n<meta property=\"og:site_name\" content=\"Clinomic\" \/>\n<meta property=\"article:published_time\" content=\"2019-02-28T16:58:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-10-05T14:38:26+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.clinomic.ai\/wp-content\/uploads\/2019\/02\/Adaptive-Learning-Approach-To-Parameter-Estimation-For-Hybrid-Petri-Nets-In-Systems-Biology.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"576\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Arne Peine\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Verfasst von\" \/>\n\t<meta name=\"twitter:data1\" content=\"Arne Peine\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.clinomic.ai\\\/2019\\\/02\\\/28\\\/an-adaptive-learning-approach-to-parameter-estimation-for-hybrid-petri-nets-in-systems-biology\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.clinomic.ai\\\/2019\\\/02\\\/28\\\/an-adaptive-learning-approach-to-parameter-estimation-for-hybrid-petri-nets-in-systems-biology\\\/\"},\"author\":{\"name\":\"Arne Peine\",\"@id\":\"https:\\\/\\\/www.clinomic.ai\\\/#\\\/schema\\\/person\\\/96e364f58fe927a66f5cfbfe4064b257\"},\"headline\":\"An Adaptive Learning Approach To Parameter Estimation For Hybrid Petri Nets In Systems Biology\",\"datePublished\":\"2019-02-28T16:58:00+00:00\",\"dateModified\":\"2023-10-05T14:38:26+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.clinomic.ai\\\/2019\\\/02\\\/28\\\/an-adaptive-learning-approach-to-parameter-estimation-for-hybrid-petri-nets-in-systems-biology\\\/\"},\"wordCount\":107,\"image\":{\"@id\":\"https:\\\/\\\/www.clinomic.ai\\\/2019\\\/02\\\/28\\\/an-adaptive-learning-approach-to-parameter-estimation-for-hybrid-petri-nets-in-systems-biology\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.clinomic.ai\\\/wp-content\\\/uploads\\\/2019\\\/02\\\/Adaptive-Learning-Approach-To-Parameter-Estimation-For-Hybrid-Petri-Nets-In-Systems-Biology.jpg\",\"keywords\":[\"ailab\"],\"articleSection\":[\"Publications with partner universities\"],\"inLanguage\":\"de\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.clinomic.ai\\\/2019\\\/02\\\/28\\\/an-adaptive-learning-approach-to-parameter-estimation-for-hybrid-petri-nets-in-systems-biology\\\/\",\"url\":\"https:\\\/\\\/www.clinomic.ai\\\/2019\\\/02\\\/28\\\/an-adaptive-learning-approach-to-parameter-estimation-for-hybrid-petri-nets-in-systems-biology\\\/\",\"name\":\"An Adaptive Learning Approach To Parameter Estimation For Hybrid Petri Nets In Systems Biology - 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