Lanman is a biologist \u2014 in fact, a botanist \u2014 at heart, but in visiting her lab, don\u2019t expect to see white coats or even a test tube. In the Collaborative Core for Cancer Bioinformatics, which she manages, her primary tool is a computer.<\/p>\n\n\n\n
\u201cI\u2019m a biologist, but my work leverages computational methods and algorithms to make sense of massive biological datasets,\u201d said Lanman, who helped establish the core in 2015, the same year she earned her PhD from Purdue. \u201cMy goal is to understand disease processes and to leverage that knowledge to improve treatments. That\u2019s what\u2019s driving me.\u201d<\/p>\n\n\n\n
Since the Human Genome Project sequenced the 3.2 billion DNA base pairs in a full human genome, the speed with which researchers are able to obtain genetic data has shifted from years to weeks or even hours. Genomic data, or DNA sequencing, produces a record of the instructions for building and maintaining life as written in a sort of Morse code using four molecules.<\/p>\n\n\n\n
Similar to DNA sequencing, researchers use a range of other techniques to understand what processes give rise to cancer and create an environment where it can thrive. They may track specific genes \u2014 each of which contains the instructions for proteins \u2014 that are activated at a given time, the proteins built from the activated instructions, interactions between those proteins, and thousands of molecules that life produces, such as fats, signaling molecules and vitamins. These techniques have given rise to a tsunami of information \u2014 genomic, transcriptomic, proteomic, metabolomic \u2014 collectively known as multiomic sequencing data.<\/p>\n\n\n\n
It\u2019s enough to give anyone a headache, which is why Lanman\u2019s bioinformatics expertise is so critical. The one connection needed might be hidden among a thousand other interactions.<\/p>\n\n\n\n
When she joins a project, Lanman might contribute to experimental design, run simulations to determine the optimal sample size needed, select among advanced data analysis techniques including the use of neural networks and AI, and perform the calculations. Her background in life sciences, as well as extensive training in computer science and statistics, enables her to apply computational methods to large biological datasets.<\/p>\n\n\n\n
For example, in recent work with Purdue colleague Deborah Knapp, a canine cancer scientist and the Dolores L. McCall Professor of Veterinary Medicine and Distinguished Professor of Comparative Oncology, Lanman is combining many types of available data to create a \u201cdigital twin\u201d model<\/a> of bladder cancer that may be powerful enough to predict patient outcomes, including the probability of metastasis. The project builds on Knapp\u2019s long-running study of dogs with bladder cancer, including Scottish terriers, a breed that develops bladder cancer at a rate 20 times higher than other dog breeds. By applying computer techniques to the available data, Lanman aims to create a virtual representation of bladder cancer and use it to simulate various scenarios. Researchers can then take promising leads back to the lab to test whether the results are borne out in real life.<\/p>\n\n\n\n \u201cWhat we can learn is endless, but it has to be done through a marriage between bench science and computation,\u201d Lanman said. \u201cThe highest impact comes from generating or testing a hypothesis computationally and then going back into the lab to validate the results and to identify mechanisms at play. And now that we can process these massive amounts of data, we have many, many targets to test with bench science.\u201d<\/p>\n\n\n As the name of the bioinformatics core suggests, much of Lanman\u2019s work is collaborative, adding her knowledge of data analysis to teams working on a range of questions related to cancer. Her work empowers a broad swath of cancer research, advancing basic science, like how immune cells are reprogrammed<\/a> in a cancerous environment; improving data science methods<\/a> for cancer research; and treating urologic diseases<\/a>, which is her own primary research interest. But regardless of the data or the computational methods for digging into it, her interest is in helping people.<\/p>\n\n\n\n \u201cIf I develop a novel computational technique, what I really want is to use it to gain insight that will improve patients\u2019 lives,\u201d Lanman said.<\/p>\n\n\n\n The core supports research through the Purdue Institute for Cancer Research. The institute, the core and Lanman\u2019s research are part of Purdue\u2019s\u00a0One Health<\/a>\u00a0initiative, which involves research at the intersection of human, animal and plant health and well-being. Computational biology is also a component of\u00a0Purdue Computes<\/a>, a comprehensive university initiative that emphasizes four key pillars of Purdue\u2019s extensive technological and computational environment \u2014 computing departments, physical AI, quantum science and semiconductor innovation. Click here\u00a0for a complete story that includes links to related research publications<\/a>.\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":" With recent advances, cancer research now generates vast amounts of information. The data could help researchers detect patterns in cancer cells and stop their growth, but the sheer volume is just too much for the human mind to digest. Enter Nadia Lanman, research associate professor in the Department of Comparative Pathobiology, whose expertise in computational biology helps researchers at Purdue University distill solutions from the sea of numbers.<\/p>\n","protected":false},"author":7,"featured_media":31902,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[36,41,29,11,1],"tags":[123,56,598,150,25],"class_list":["post-31901","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cancer","category-faculty-staff","category-our-people","category-research","category-uncategorized","tag-cpb","tag-homepage","tag-our-people","tag-research","tag-top-story"],"acf":[],"_links":{"self":[{"href":"https:\/\/vet.purdue.edu\/news\/wp-json\/wp\/v2\/posts\/31901","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vet.purdue.edu\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vet.purdue.edu\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vet.purdue.edu\/news\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/vet.purdue.edu\/news\/wp-json\/wp\/v2\/comments?post=31901"}],"version-history":[{"count":3,"href":"https:\/\/vet.purdue.edu\/news\/wp-json\/wp\/v2\/posts\/31901\/revisions"}],"predecessor-version":[{"id":31928,"href":"https:\/\/vet.purdue.edu\/news\/wp-json\/wp\/v2\/posts\/31901\/revisions\/31928"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/vet.purdue.edu\/news\/wp-json\/wp\/v2\/media\/31902"}],"wp:attachment":[{"href":"https:\/\/vet.purdue.edu\/news\/wp-json\/wp\/v2\/media?parent=31901"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vet.purdue.edu\/news\/wp-json\/wp\/v2\/categories?post=31901"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vet.purdue.edu\/news\/wp-json\/wp\/v2\/tags?post=31901"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"A](\"https:\/\/vet.purdue.edu\/news\/wp-content\/uploads\/2025\/12\/lanman-datasets.jpg\")