{"id":22516,"date":"2026-07-16T13:30:14","date_gmt":"2026-07-16T11:30:14","guid":{"rendered":"https:\/\/www.neuraldesigner.com\/use-cases\/microarray-analysis\/"},"modified":"2026-08-26T13:48:25","modified_gmt":"2026-08-26T11:48:25","slug":"microarray-analysis","status":"publish","type":"page","link":"https:\/\/www.neuraldesigner.com\/use-cases\/microarray-analysis\/","title":{"rendered":"Microarray data analysis using machine learning"},"content":{"rendered":"<style>.ndb{width:100vw;margin-left:calc(50% - 50vw);background:#eeeeee;padding:22px 24px 14px;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif;color:#1b2635}.ndb *{box-sizing:border-box}.ndb a{text-decoration:none}.ndb-wrap{width:min(100%,960px);margin:0 auto}.ndb-lead{font-size:18px;line-height:1.6;color:#3a4a5a;font-weight:300;margin:0 0 22px}.ndb-lead a{color:#2d799f;font-weight:600}.ndb-highlight{margin:0 0 36px;padding:28px 34px;border-radius:18px;background:linear-gradient(135deg,#56a1c8 0%,#245e80 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.nd-usecase-icon-source img,body.page-child.parent-pageid-22498 main.site-main .ndb-wrap .ndb-card td img[src*=\".svg\"],body.page-child.parent-pageid-22498 main.site-main .ndb-wrap .ndb-card td svg,body.page-child.parent-pageid-22498 main.site-main .ndb-wrap .nd-benefit-icon svg{width:60px!important;height:60px!important;max-width:60px!important;display:inline-block!important;margin:0 auto 6px!important;color:#2d799f!important;box-shadow:none!important;border-radius:0!important}.ndb-card p:has(>img),.ndb-card figure,.ndb-lead p:has(>img),.ndb-lead figure{max-width:560px!important;margin-left:auto!important;margin-right:auto!important;text-align:center!important}.ndb-card p:has(>.nd-usecase-icon-source),.ndb-card .nd-usecase-icon-source{max-width:none!important}.ndb-card table:has(td h3){background:transparent!important;box-shadow:none!important;border:0!important;width:100%!important;table-layout:fixed!important;margin:14px 0!important}.ndb-card table:has(td h3) td{background:transparent!important;border:0!important;text-align:center!important;vertical-align:top!important;padding:16px 14px!important}.ndb-card table:has(td h3) td p{margin:0 0 12px!important;text-align:center!important;max-width:none!important}.ndb-card table:has(td h3) td p img{display:inline-block!important;width:52px!important;height:52px!important;max-width:52px!important;object-fit:contain!important;margin:0 auto!important;box-shadow:none!important;border-radius:0!important}.ndb-card table:has(td h3) td h3{color:#001233!important;font-size:16px!important;font-weight:600!important;margin:0!important;text-align:center!important;letter-spacing:.03em}body.page-child.parent-pageid-22498 main.site-main .ndb-wrap .ndb-card table td p img{width:52px!important;height:52px!important;max-width:52px!important;min-width:0!important;object-fit:contain!important;margin:0 auto!important;display:inline-block!important}main#content .ndb-card .nd-usecase-title-icon,main#content .ndb-card h3.nd-usecase-icon-title img{display:none!important}main#content .ndb-card h3.nd-usecase-icon-title{display:block!important}.ndb-card .ndb-subhead{color:#001233!important;font-size:19px!important;font-weight:600!important;margin:26px 0 8px!important;text-align:center!important}.ndb-card table:has(td h3) td p:not(:has(img)){font-size:14px!important;line-height:1.5!important;color:#51606f!important;margin:8px auto 0!important;text-align:center!important;max-width:none!important}<\/style><div class=\"ndb\"><div class=\"ndb-wrap\"><ul class=\"ndb-toc\"><li><a href=\"#introduction\">Introduction<\/a><\/li><li><a href=\"#objectives\">Objectives<\/a><\/li><li><a href=\"#benefits\">Benefits<\/a><\/li><li><a href=\"#approach\">Approach<\/a><\/li><li><a href=\"#conclusions\">Conclusions<\/a><\/li><li><a href=\"#relevant-documentation\">Relevant documentation<\/a><\/li><li><a href=\"#references\">References<\/a><\/li><\/ul><div class=\"ndb-card\" id=\"introduction\"><h2>Introduction<\/h2><p data-start=\"71\" data-end=\"174\">Machine learning analyzes microarray data to uncover complex biological patterns and interactions.<\/p>\n<p data-start=\"71\" data-end=\"174\">Microarrays, structured as gene-by-sample matrices and measuring DNA, RNA, proteins, tissues, or peptides via fluorescence, support identifying disease-related genes, predicting treatment responses, and studying environmental stress.<\/p>\n<p data-start=\"71\" data-end=\"174\">A notable example is cancer classification by gene expression (Golub et al.).<\/p>\n<p data-start=\"71\" data-end=\"174\">Systematic recording, expanding datasets, and public repositories, following standards from the Microarray Gene Expression Data Society, enable meaningful microarray analysis and research.<\/p>\n<p>Healthcare professionals can test this methodology by downloading Neural Designer.<\/p>\n<p><a href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Download Neural Designer<\/a><\/p>\n<\/div><div class=\"ndb-card\" id=\"objectives\"><h2>Objectives<\/h2><p>The analysis of clinical data enables us to understand the biological mechanisms that underlie diseases and how risk factors influence their development.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/activity_diagram_microarray_analysis-1024x277.webp\" sizes=\"(max-width: 800px) 100vw, 800px\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/activity_diagram_microarray_analysis-1024x277.webp 1024w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/activity_diagram_microarray_analysis-300x81.webp 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/activity_diagram_microarray_analysis-768x208.webp 768w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/activity_diagram_microarray_analysis-1536x415.webp 1536w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/activity_diagram_microarray_analysis-2048x554.webp 2048w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/activity_diagram_microarray_analysis-600x162.webp 600w\" alt=\"\" width=\"800\" height=\"216\" \/><\/p>\n<p>Thankfully, a large amount of data is currently available to clinicians. These data range from clinical symptoms to various biochemical assays and outputs of imaging devices.<\/p>\n<p>These are some examples of types of data that could be useful to make an accurate medical diagnosis using machine learning:<\/p>\n<ul>\n<li><b>Disease data:<\/b>\u00a0Physiological measurements and information about known diseases or symptoms that an individual has experienced.<\/li>\n<li><b>Environmental data:<\/b>\u00a0Information about an individual\u2019s environmental exposures, such as smoking, sunbathing, weather conditions, etc.<\/li>\n<li><b>Genetic data:<\/b>\u00a0All or critical parts of the DNA sequence of an individual.<\/li>\n<\/ul>\n<\/div><div class=\"ndb-card\" id=\"benefits\"><h2>Benefits<\/h2><div class=\"nd-loose-benefit-cards nd-static-benefit-cards\">\n<article class=\"nd-loose-benefit-card\">\n<p class=\"nd-loose-benefit-card__icon\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/search.svg\" width=\"37\" height=\"37\" alt=\"\" \/><\/p>\n<div>\n<h3>Analyze genes<\/h3>\n<p>Analyze gene changes to identify patterns and determine when genes move from a normal state to a diseased state.<\/p>\n<\/div>\n<\/article>\n<article class=\"nd-loose-benefit-card\">\n<p class=\"nd-loose-benefit-card__icon\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/trending_up.svg\" width=\"42\" height=\"42\" alt=\"\" \/><\/p>\n<div>\n<h3>Predict future stages<\/h3>\n<p>Develop a model that can detect gene changes and predict whether they correspond to a normal or diseased state.<\/p>\n<\/div>\n<\/article>\n<article class=\"nd-loose-benefit-card\">\n<p class=\"nd-loose-benefit-card__icon\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/thumb_up.svg\" width=\"42\" height=\"42\" alt=\"\" \/><\/p>\n<div>\n<h3>Prevent diseases<\/h3>\n<p>Use the predictive model to support preventive medicine and early diagnosis by revealing relationships between genes and diseases.<\/p>\n<\/div>\n<\/article>\n<\/div>\n<\/div><div class=\"ndb-card\" id=\"approach\"><h2>Approach<\/h2><p>Neural Designer is a machine learning software using neural networks to analyze large datasets, uncover gene\u2013environment interactions, perform model and variable selection, and validate predictive models.<\/p>\n<p>In this example, it identifies correlations between leukemia subtypes and gene expression, highlighting the most influential genes.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/leukemia_correlations_chart-1.webp\" sizes=\"(max-width: 600px) 100vw, 600px\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/leukemia_correlations_chart-1.webp 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/leukemia_correlations_chart-1-222x300.webp 222w\" alt=\"\" width=\"600\" height=\"810\" \/><\/p>\n<\/div><div class=\"ndb-card ndb-card--accent\" id=\"conclusions\"><h2>Conclusions<\/h2><ul>\n<li>The study of microarrays and gene expression can bring useful knowledge to very complex subjects, and it is a tool healthcare researchers are using to learn about diseases and how to treat them.<\/li>\n<\/ul>\n<\/div><div class=\"ndb-card\" id=\"relevant-documentation\"><h2>Relevant documentation<\/h2><ul>\n<li>If you want to learn how to use Neural Designer, you can do that by reading the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/user-guide\/user-guide\/\">user\u00b4s guide<\/a> or practicing with the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/examples\/\">examples<\/a>.<\/li>\n<li>You can also learn more about neural networks by reading <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/\">this guide<\/a>.<\/li>\n<\/ul>\n<\/div><div class=\"ndb-card\" id=\"references\"><h2>References<\/h2><ul>\n<li><a href=\"https:\/\/science.sciencemag.org\/content\/286\/5439\/531\" rel=\"nofollow\">Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., &#8230; &amp; Bloomfield, C. D. (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. science, 286(5439), 531-537<\/a>.<\/li>\n<li><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1532046404000693\" rel=\"nofollow\">Kuo, W. P., Kim, E. Y., Trimarchi, J., Jenssen, T. K., Vinterbo, S. A., &amp; Ohno-Machado, L. (2004). A primer on gene expression and microarrays for machine learning researchers. Journal of Biomedical Informatics, 37(4), 293-303<\/a>.<\/li>\n<li><a href=\"https:\/\/dl.acm.org\/citation.cfm?id=820213\">Cho, S. B., &amp; Won, H. H. (2003, January). Machine learning in DNA microarray analysis for cancer classification. In Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003-Volume 19 (pp. 189-198). Australian Computer Society, Inc.<\/a><\/li>\n<\/ul>\n<\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>IntroductionObjectivesBenefitsApproachConclusionsRelevant documentationReferencesIntroductionMachine learning analyzes microarray data to uncover complex biological patterns and interactions. Microarrays, structured as gene-by-sample matrices and measuring DNA, RNA, proteins, tissues, or peptides via fluorescence, support identifying disease-related genes, predicting treatment responses, and studying environmental stress. A notable example is cancer classification by gene expression (Golub et al.). Systematic recording, expanding datasets, [&hellip;]<\/p>\n","protected":false},"author":152,"featured_media":1925,"parent":22498,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-22516","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Microarray data analysis using machine learning - Neural Designer<\/title>\n<meta name=\"description\" content=\"Explore this Neural Designer use case for Microarray data analysis using machine learning, including problem framing, data, evaluation and limitations.\" \/>\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.neuraldesigner.com\/use-cases\/microarray-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Microarray data analysis using machine learning - Neural Designer\" \/>\n<meta property=\"og:description\" content=\"IntroductionObjectivesBenefitsApproachConclusionsRelevant documentationReferencesIntroductionMachine learning analyzes microarray data to uncover complex biological patterns and interactions. 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Microarrays, structured as gene-by-sample matrices and measuring DNA, RNA, proteins, tissues, or peptides via fluorescence, support identifying disease-related genes, predicting treatment responses, and studying environmental stress. A notable example is cancer classification by gene expression (Golub et al.). Systematic recording, expanding datasets, [&hellip;]","og_url":"https:\/\/www.neuraldesigner.com\/use-cases\/microarray-analysis\/","og_site_name":"Neural Designer","article_modified_time":"2026-08-26T11:48:25+00:00","og_image":[{"width":1200,"height":628,"url":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/microarray-solution.webp","type":"image\/webp"}],"twitter_card":"summary_large_image","twitter_site":"@NeuralDesigner","twitter_misc":{"Est. reading time":"2 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.neuraldesigner.com\/use-cases\/microarray-analysis\/","url":"https:\/\/www.neuraldesigner.com\/use-cases\/microarray-analysis\/","name":"Microarray data analysis using machine learning - Neural Designer","isPartOf":{"@id":"https:\/\/www.neuraldesigner.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.neuraldesigner.com\/use-cases\/microarray-analysis\/#primaryimage"},"image":{"@id":"https:\/\/www.neuraldesigner.com\/use-cases\/microarray-analysis\/#primaryimage"},"thumbnailUrl":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/microarray-solution.webp","datePublished":"2026-07-16T11:30:14+00:00","dateModified":"2026-08-26T11:48:25+00:00","breadcrumb":{"@id":"https:\/\/www.neuraldesigner.com\/use-cases\/microarray-analysis\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.neuraldesigner.com\/use-cases\/microarray-analysis\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.neuraldesigner.com\/use-cases\/microarray-analysis\/#primaryimage","url":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/microarray-solution.webp","contentUrl":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/microarray-solution.webp","width":1200,"height":628},{"@type":"BreadcrumbList","@id":"https:\/\/www.neuraldesigner.com\/use-cases\/microarray-analysis\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.neuraldesigner.com\/"},{"@type":"ListItem","position":2,"name":"Neural Designer Use Cases","item":"https:\/\/www.neuraldesigner.com\/use-cases\/"},{"@type":"ListItem","position":3,"name":"Microarray data analysis using machine learning"}]},{"@type":"WebSite","@id":"https:\/\/www.neuraldesigner.com\/#website","url":"https:\/\/www.neuraldesigner.com\/","name":"Neural Designer","description":"Explainable AI Platform","publisher":{"@id":"https:\/\/www.neuraldesigner.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.neuraldesigner.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.neuraldesigner.com\/#organization","name":"Neural Designer","url":"https:\/\/www.neuraldesigner.com\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.neuraldesigner.com\/#\/schema\/logo\/image\/","url":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/05\/logo-neural-1.png","contentUrl":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/05\/logo-neural-1.png","width":1024,"height":223,"caption":"Neural Designer"},"image":{"@id":"https:\/\/www.neuraldesigner.com\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/x.com\/NeuralDesigner","https:\/\/es.linkedin.com\/showcase\/neuraldesigner\/"]}]}},"_links":{"self":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/pages\/22516","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/users\/152"}],"replies":[{"embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/comments?post=22516"}],"version-history":[{"count":5,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/pages\/22516\/revisions"}],"predecessor-version":[{"id":23760,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/pages\/22516\/revisions\/23760"}],"up":[{"embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/pages\/22498"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/media\/1925"}],"wp:attachment":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/media?parent=22516"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}