Learning structure in gene expression data using deep architectures, with an application to gene clustering

Aman Gupta; Haohan Wang; Madhavi Ganapathiraju

doi:10.1101/031906

CARVIEW

MOTORHOMES

Select Language

HTTP/2 301 date: Sun, 18 Jan 2026 10:02:08 GMT content-length: 0 location: https://doi.org/10.1101/031906 server: cloudflare vary: Origin expires: Mon, 19 Jan 2026 10:02:07 GMT permissions-policy: interest-cohort=(),browsing-topics=() cf-cache-status: DYNAMIC nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} strict-transport-security: max-age=31536000; includeSubDomains; preload report-to: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=19gP%2BjccthP2r48Oh2SxQ3PBFFb9inNKA85FgpvzrEonk63QDVIdjK3jjIYEj%2F3syiTrexHy6cJRxpFH7nhRd81GFzSHNg%3D%3D"}]} cf-ray: 9bfd41474b88cf00-BLR alt-svc: h3=":443"; ma=86400 HTTP/2 302 date: Sun, 18 Jan 2026 10:02:08 GMT content-type: text/html;charset=utf-8 location: https://biorxiv.org/lookup/doi/10.1101/031906 server: cloudflare vary: Origin vary: Accept expires: Sun, 18 Jan 2026 10:10:59 GMT permissions-policy: interest-cohort=(),browsing-topics=() cf-cache-status: DYNAMIC nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} strict-transport-security: max-age=31536000; includeSubDomains; preload report-to: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=XX9OgzP4cnT7cyxMsA%2BCZvguCA7Jo9nFUHQQTOev1CVqosQ1ve3JYUlJiDr2yLQqmvRzl44sx2k6AK5iXVbWEm4VSGySIg%3D%3D"}]} cf-ray: 9bfd4148bd8bcf00-BLR alt-svc: h3=":443"; ma=86400 HTTP/1.1 302 Found Date: Sun, 18 Jan 2026 10:02:08 GMT Content-Type: text/html; charset=iso-8859-1 Transfer-Encoding: chunked Connection: keep-alive server: cloudflare location: https://www.biorxiv.org/lookup/doi/10.1101/031906 cf-cache-status: DYNAMIC Nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800} Report-To: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=49zmDf1v0kxTcfUvLWD3%2B%2BlhB6pAuDGPgzTSqsAuiHC29EXxHbwwWKtGwBKDj4gaKdRtCyFHXJLEFCljIUIYA6uAWohp4zR0ihKg"}]} CF-RAY: 9bfd41494fe946a8-BOM alt-svc: h3=":443"; ma=86400 HTTP/2 301 date: Sun, 18 Jan 2026 10:02:10 GMT content-type: text/html; charset=UTF-8 location: https://www.biorxiv.org/content/10.1101/031906v1 cf-ray: 9bfd414c6d842ffb-BLR x-content-type-options: nosniff x-content-type-options: nosniff x-drupal-cache: MISS expires: Sun, 18 Jan 2026 10:32:10 GMT cache-control: public, max-age=1800 pragma: no-cache vary: Accept-Encoding x-highwire-sitecode: biorxiv x-highwire-smart-code: biorxiv_production x-varnish: 694624315 via: 1.1 varnish x-varnish-ttl: x-varnish-cache: cf-cache-status: MISS set-cookie: __cf_bm=y2jtpkWKUD8lWgVJIG96FDelhYFleV_MZ5lQkpqgjRg-1768730530-1.0.1.1-UMz3hHPxq9kphOMPnPWVuHDdXlm3As0flm0WDH4Ox6rzJ5.dUykyaHKe3nR1oD.LduucCIVkKxFm4e5fwqJuQiqLP2SRMgymzInpm1uqP5M; path=/; expires=Sun, 18-Jan-26 10:32:10 GMT; domain=.www.biorxiv.org; HttpOnly; Secure; SameSite=None server: cloudflare HTTP/2 200 date: Sun, 18 Jan 2026 10:02:11 GMT content-type: text/html; charset=utf-8 content-encoding: gzip x-content-type-options: nosniff x-content-type-options: nosniff x-drupal-cache: MISS expires: Sun, 19 Nov 1978 05:00:00 GMT cache-control: no-cache, must-revalidate set-cookie: SSESS1dd6867f1a1b90340f573dcdef3076bc=nLZ21n6hpUloiCOZPqq1VvSJoq3H02wChVNSDOdNJSQ; expires=Tue, 10-Feb-2026 13:35:30 GMT; path=/; domain=.biorxiv.org; secure; HttpOnly content-language: en x-frame-options: SAMEORIGIN x-generator: Drupal 7 (https://drupal.org) link: ; rel="canonical",; rel="shortlink" vary: Accept-Encoding x-highwire-sitecode: biorxiv x-highwire-smart-code: biorxiv_production x-varnish: 694624369 age: 0 via: 1.1 varnish x-varnish-ttl: x-varnish-cache: cf-cache-status: DYNAMIC server: cloudflare cf-ray: 9bfd4154dea22ffb-BLR Learning structure in gene expression data using deep architectures, with an application to gene clustering | bioRxiv

Abstract

Genes play a central role in all biological processes. DNA microarray technology has made it possible to study the expression behavior of thousands of genes in one go. Often, gene expression data is used to generate features for supervised and unsupervised learning tasks. At the same time, advances in the field of deep learning have made available a plethora of architectures. In this paper, we use deep architectures pre-trained in an unsupervised manner using denoising autoencoders as a preprocessing step for a popular unsupervised learning task. Denoising autoencoders (DA) can be used to learn a compact representation of input, and have been used to generate features for further supervised learning tasks. We propose that our deep architectures can be treated as empirical versions of Deep Belief Networks (DBNs). We use our deep architectures to regenerate gene expression time series data for two different data sets. We test our hypothesis on two popular datasets for the unsupervised learning task of clustering and find promising improvements in performance.