Human genomicist, Assoc Prof @UVA. Deconvolving complex cardiovascular diseases using systems genetics and single-cell omics.
Charlottesville, VA
Joined April 2013
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Clint Miller retweeted
The 9p21 risk locus — the first and most impactful CAD genetic risk factor in humans — drives VSMCs to adopt an osteochondrogenic state, promoting calcification @clintomics @valelosardo
ahajrnls.org/3E59c3h
Clint Miller retweeted
Thrilled to share the🥇paper from the lab @atvbahajournals. Here we continue to unravel the mysteries of the 9p21.3 CAD locus showing it drives VSMC transition into an osteochondrogenic state, promoting calcification. @clintomics @UWMadisonCRB ahajournals.org/doi/10.1161/…
Clint Miller retweeted
🚀 Thrilled to announce our latest #preprint on intraplaque haemorrhage (IPH) quantification and molecular 🧬characterisation of carotid plaques using deep learning. A thread 🧵👇🏽
Clint Miller retweeted
Trump's slashing of NIH indirects blocked for now in 22 states but not VA. Will the "peoples protector" Attorney General @JasonMiyaresVA stand up for Virginia researchers and doctors and their work to cure cancer, heart disease, diabetes, and more? – at Virginia, USA
Clint Miller retweeted
The number of cells in single-cell transcriptomics studies grew exponentially over the years. But what about the number of donors? Let me tell you the story of my contribution to the fantastic paper of @SikkemaLisa and @KHrovatin. Read the paper: nature.com/articles/s41592-0… 1/12
Clint Miller retweeted
Virtual cell models have the potential to transform biological research. Today @ChanZuckerberg released an initial set of models, including scGenePT and SubCell, designed to be easy to run and build upon. This is a really exciting time in biology. czi.co/4g1jTSa
Clint Miller retweeted
We use Multiome and HiC to build a comprehensive single cell variant to enhancer to gene map for coronary artery disease. Join effort with @PaulChungrohLee and IttaiEres in Nate Stitziel Lab and in collaboration with @Amgen @WUSTLmed
medrxiv.org/content/10.1101/…
Clint Miller retweeted
Thrilled to share two @biorxivpreprint papers from my lab and @StimulatedRaman, reporting on the SAME tissue section multi-modality spatial omics! 🤩🥳🙂
Clint Miller retweeted
Thank you Sara! Happy to finally present the work we’ve been doing with the labs of Sander van deer Laan (UMC Utrecht) and @clintomics (UVA) on carotid plaque morphology and vulnerability at #ASHG24 😊
At #ASHG and interested in deep learning for disease prediction? Come visit @francescocist9 ‘s poster: imaging + transcriptomics to understand how carotid plaque morphology affects patients symptoms and MACE risk (board 4081T)
#ASHG2024
Clint Miller retweeted
A huge day for spatial omics of cancer, tracing the precise origin and evolution of 20 types of cancer, many in 3D, in ~2,000 individuals, with 12 publications @Nature @NatureCancer @NatureMedicine @naturemethods @CommsBio
Collection nature.com/immersive/d42859-…
Clint Miller retweeted
Thrilled to present Patho-DBiT out @CellCellPress A first-of-its-kind technology for spatial whole transcriptome sequencing of clinically archived FFPE tissues, mapping various large/small RNAs, alternative splicing, and genome-wide SNV, all at once from the same tissue section!
Clint Miller retweeted
Excited to share TxGNN, a model that identifies potential therapies from existing medicines for thousands of diseases. Trained across 17,080 diseases, TxGNN predicts drug candidates for conditions with limited or no treatment options, including rare diseases
@NatureMedicine paper: nature.com/articles/s41591-0…
Globally, there are over 7,000 rare and undiagnosed diseases, yet only 5 to 7 percent have treatments, leaving the majority untreated or undertreated. Even for more common diseases, new drugs could offer alternatives with fewer side effects or replace drugs that are ineffective for certain patients
TxGNN generates new insights on its own in the form of multi-hop interpretable rationales, applies them to diseases it was not trained for, and offers explanations for its predictions
Human evaluation showed that TxGNN's predictions perform well across multiple axes of performance
Many of TxGNN's predictions align with off-label prescriptions used in a large healthcare system
Many thanks to a fantastic research team @KexinHuang5 @payal_chandak @WangQianwenToo @_toolazyto_ @AkhilVaidMD @jure @girish_nadkarni @BenGlicksberg
@HarvardDBMI @harvardmed @KempnerInst @harvard_data @broadinstitute @cziscience
@harvardmed News and Gazette: hms.harvard.edu/news/researc… Thanks @EkaterinaPeshev
Clint Miller retweeted
Excited to present “scooby”, which models multi-omic profiles (scRNA-seq coverage & scATAC-seq insertions) directly from 500 kb DNA sequence at single-cell resolution. This was a fantastic collaboration co-led with @thisisjohahi.
biorxiv.org/content/10.1101/…
Clint Miller retweeted
Excited to share HIPPO (Histopathology Interventions of Patches for Predictive Outcomes)! HIPPO is a perturbation-based post hoc explanation tool interprets weakly supervised models for digital pathology. 1/N
Work led by @JakubKaczmarzyk.
Clint Miller retweeted
Some suggested weekend reading: a new primer out in @TrendsinBiotech from our first postdoc @arthurwchow on key concepts and new developments in single-cell multi-omics! Check it out here! cell.com/trends/biotechnolog….
Clint Miller retweeted
We analyzed almost 400 blood, urine, saliva, DNA and RNA samples using 18 #multiomics platforms incl. @Metabolon @biocrates_life @SomaLogic @OlinkProteomics @illumina @GGlycoscience @NgaleHealth
Blog: metabolomix.com/comics/
Our @NatureComms paper:
nature.com/articles/s41467-0…
Clint Miller retweeted
Transformers in single-cell omics: a review and new perspectives nature.com/articles/s41592-0…
Clint Miller retweeted
We were invited to write a review article on DNA/genomic language models (gLMs). We took this occasion to gather our thoughts on promising applications, and major considerations for developing and evaluating gLMs. Pls share with your colleagues: Preprint: arxiv.org/abs/2407.11435
Clint Miller retweeted
What would you do with 1000+ spatial transcriptomics samples with corresponding H&E-stained whole-slide images? Meet HEST-1k, a collection of 1,108 ST samples assembled from 131 public and internal cohorts encompassing 25 organs, 2 species. HEST-1k includes over 1.5 million expression–morphology pairs.
🔍Explore 3 use-cases for HEST-1k:
- HEST-Benchmark: Evaluate gene expression prediction from histology across 10 organs and 9 cancer types, testing multiple foundation models for pathology including UNI, and GigaPath.
- HEST for discovery: Explore our proof-of-concept for multimodal biomarker characterization using Xenium breast cancer samples.
- HEST for fine-tuning pathology foundation models: See how HEST-1k can enhance foundation models for histology with expression-guided fine-tuning.
📄Preprint: arxiv.org/pdf/2406.16192
👩💻 Code and Data access: github.com/mahmoodlab/HEST
Congratulations to @guillaumejaume, @pauldoucet, and everyone else who contributed to this work. Huge thanks to everyone who helped curate the dataset. #SpatialTranscriptomics #ComputationalPathology #CancerResearch #Bioinformatics
Clint Miller retweeted
This promotes cap thinning. Great work by CVPath Institute, Inc. team including Masayuki Mori , Atsushi Sakamoto and Rika Kawakami together with collaborators including @clintomics and Jose Verdezoto Mosquera. ahajournals.org/doi/epdf/10.…