AI for Precision Medicine, Causal Modeling and Reasoning, Network Biology and Evolution. Professor @UTHSC

Joined April 2014
Bridging ancestry gaps in genomic risk prediction with tabular foundation models academic.oup.com/bioinformat…
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Digital pathways connecting social and biological factors to health outcomes and equity | @npjDigitalMed @Nature 3/20/2025 #DigitalHealth #AI #ML #SDOH #HealthEquity #PopHealth #TechHartford Link: nature.com/articles/s41746-0…
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President Jimmy Carter wrote this message to accompany Voyager 1, which is now 15.4 billion miles away from Earth.
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In new research on Genome Medicine, Gao and Cui demonstrate how an optimized deep transfer learning approach can improve the performance of clinico-genomic prediction models for data-disadvantaged and underrepresented ancestry groups. @YanCui2 Paper 👇 genomemedicine.biomedcentral…
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Check out our latest study in Genome Medicine! Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement. rdcu.be/dJV0w #AI #HealthEquity #MachineLearning #Genomics #Polygenic #Multiancestral
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Nature rejected her paper for not being original, University of Pennsylvania (her employer) demoted her, and yesterday Katalin Karikó won the Noble Prize in physiology. In mid-2000s, Karikó and her Drew Weismann submitted their paper on mRNA (messenger Ribonucleic Acid) to Nature. Nature desk rejected their paper for being "an incremental contribution" only. The paper was later published in another journal, Immunity. Earlier in her career at the University of Pennsylvania, Karikó was demoted because her applications for grants kept getting rejected. But Karikó persevered and kept on going. In 2013, she joined BioNTech, a German company founded by two scientists, Uğur Şahin and Özlem Türeci. In 2018, BioNTech partnered with Pfizer to develop mRNA vaccines against the influenza virus. When the COVID-19 hit the world, Karikó's research helped Pfizer to produce the first vaccine against the disease. I don't know how the Nature editors who desk rejected Karikó's paper and the Penn administration who demoted her feel about Karikó Nobel Prize. Takeaway: Many academics and scientists worry about getting published in "prestigious" journals. Instead of worrying about prestige, we should try to put our work out as quickly as possible like Karikó did. Once you put your work out without caring about prestige, two good things happen: 1. Your work will lead to newer opportunities. 2. You will start getting feedback from the scholarly community, which you can use to iterate and improve. Here's another interesting Nobel Prize story. Peter Higgs, a British physicist, joined the University of Edinburgh in 1956. By 1964, Higgs has published his groundbreaking work about subatomic particles. After 1964, Higgs published less than 10 papers. When his department would ask him how many papers, he published in a given year, he would reply "None." It happened so often that he stared feeling like an "embarrassment to the department." The University of Edinburgh, however, never fired Higgs because in 1980 he had been nominated for the Nobel Prize. Higgs retired in 1996 and stayed on as an emeritus professor at Edinburg. In 2012, experiments conducted at the CERN laboratory confirmed Higgs work and the existence of Higgs Particle. And in 2013, Higgs was awarded the Noble Prize in physics and the University of Edinburgh got rewarded for being patient.
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Congratulations to Cancer Center Director, Neil Hayes and colleagues for publication of a phase II clinical trial suggesting benefit for head and neck cancer patients with a novel targeted therapy. aacrjournals.org/clincancerr…
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We all agree we need more diversity in #genomics , and yet less #GWAS are being performed in 2022 compared to 2016… 🤷‍♂️👎😳😩THIS IS ONLY GOING TO INCREASE INEQUALITY IN HEALTH! #ESHG2022 ⁦@SFatumo⁩ #PredictiveGenomics #PRS
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Gao and Cui develop a #MachineLearning method to incorporate info from related clinical outcomes to improve accuracy of time-to-event prediction for important clinical events like stroke, heart failure, or cancer progression and improve disease outcome dx.plos.org/10.1371/journal.…
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This fully-specified model fits all the observations and thus constitutes a plausible explanation of what's going on.
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New postdoc position in my group to develop innovative machine learning strategies for equitable genomic medicine (careers.iscb.org/jobs/view/8…). Please RT, Thanks!
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ICYMI: In this global review the @MITCriticalData group investigates sources of bias in artificial intelligence that perpetuate healthcare disparities #DigitalHealth #healthequity dx.plos.org/10.1371/journal.…
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Replying to @davidneilhayes
Join us on April 20 for the Spector Lecture with Neil Hayes MD, assistant dean Cancer Research, Van Fleet Endowed Professor @UTHSCMedicine as he presents "Celebrating 40 years of cisplatin and HPV in head and neck cancer: a new look at an old couple." oto.wustl.edu/connect/specia…
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Excellent collaboration with @SapovalNicolae @amiraliagz @michaelnute @AdvaitBalaji @LeoElworth @edrisi_amin @optimalab1 @vyao @NakhlehRice @rbaraniuk (and others!) now out in @NatureComms highlighting the impact of Deep Learning across the Biosciences: nature.com/articles/s41467-0…
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Severe Side Effects of Cancer Treatment Are More Common in Women than Men cancer.gov/news-events/cance… @theNCI #NCICancerCurrentsblog
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The @humancellatlas lung atlas that was published today is impressive, but with 2% Asian samples when 60% of the world is Asian, it seems that the initial goal of keeping "ethnic diversity in mind"... may have escaped the mind.
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Yan Cui retweeted
🎓 Mathematics for Machine Learning When I started to learn ML, there weren't too many great resources teaching maths-related bits. Luckily for new students, this recent book provides a good review of the mathematics for ML. mml-book.github.io/
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Yan Cui retweeted
Brain changes after COVID revealed by imaging go.nature.com/3HZQ3f4
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