Fan Zhang, PhD

GitHub - fanzhanglab/STEAM: Evaluating consistency and reliability of clustering in spatial omics using STEAM Evaluating consistency and reliability of clustering in spatial omics using STEAM - fanzhanglab/STEAM

Software code and tutorial notebooks: github.com/fanzhanglab/...

STEAM: Spatial Transcriptomics Evaluation Algorithm and Metric for clustering performance Abstract. Spatial transcriptomic technologies allow researchers to explore the diversity and specificity of gene expression within an original tissue struc

🚀 Excited to share our lab new paper published today in Briefings in Bioinformatics: "STEAM: Spatial Transcriptomics Evaluation Algorithm and Metric for clustering performance” academic.oup.com/bib/article/...
@cumedicalschool.bsky.social

📖 Presentation Type: Plenary
⌛ Date: Sunday, October 26
⏰ Presentation Time: 10:15 CT

This work highlights a fantastic collaboration with Joel Guthridge, Ph.D. and Kevin Deane!

Excited 🎉 to share our work at #ACR25 at Chicago tomorrow 10:15! @juninamo.bsky.social from my lab will present a #Plenary talk on integrating #longitudinal #SingleCell and #MachineLearning to uncover #Predictive immune signatures preceding #RheumatoidArthritis onset! @curhuematology.bsky.social

Thank you for highlighting our recent work - we’re honored and excited to continue contributing to the field!

It was a great honor to be invited to give a talk at @kiroxford.bsky.social University of Oxford today. Many thanks to Dr. @y-luo.bsky.social for the kind invitation. I truly enjoyed the inspiring discussions with the brilliant scientists here!

GitHub - fanzhanglab/pyCellPhenoX: An eXplainable Cell-specific machine learning method to predict clinical Phenotypes using single-cell multi-omics An eXplainable Cell-specific machine learning method to predict clinical Phenotypes using single-cell multi-omics - fanzhanglab/pyCellPhenoX

⭐️ Successfully benchmarking on both simulation and real single-cell datasets: Reveals activated monocytes in COVID-19 severity, fibroblast transitions in chronic inflammation, and therapy (anti-PD1 treatment) induced T cell changes in TNBC.
⭐️ Software available: github.com/fanzhanglab/...

CellPhenoX: An Explainable Machine Learning Method for Identifying Cell Phenotypes To Predict Clinical Outcomes from Single‐Cell Multi‐Omics CellPhenoX is an explainable machine learning framework that identifies cell-specific phenotypes and interaction effects from single-cell omics data. By leveraging interpretable models, it enables ro...

Excited 🎉 to share our lab's new paper CellPhenoX, an #explainable #MachineLearning tool 🔨 for #SingleCell data, published advanced.onlinelibrary.wiley.com/doi/10.1002/...
⭐️ CellPhenoX detects both linear, nonlinear, and even subtle interaction effects underlying clinical single-cell datasets!

More on the gut-joint connection.

We are #hiring #Postdoc/#Bioinformatician:
- Innovate computational methods for single-cell spatial transcriptomics.
- Develop prediction models for longitudinal single-cell.
⭐️Send your CV to me at fanzhanglab@gmail.com
⭐️ More at our lab website: fanzhanglab.org

Thrilled 🎉 to shared I received my R01 NoA today @NIH_NIAMS! We will continue pushing the frontiers of autoimmune research by developing novel #AI #MachineLearning for #SingleCell #SpatialTranscriptomics 🧬💻💊! @cuanschutz.bsky.social @cudeptofmedicine.bsky.social @cubiomedinfo.bsky.social

Next week! Join us on Thursday, August 14 at 6PM ET in Cambridge, MA (or online) for a Broad Discovery Series talk with @soumya-boston.bsky.social, “Arthritis, autoimmunity, and how the genome works.” Register: broadinstitute.swoogo.com/DiscoverySeries25

Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines - Nature Methods The analysis presented in this Brief Communication shows that, despite their complexity, current deep learning models do not outperform linear baselines in predicting gene perturbation effects, thus e...

An analysis shows that current deep learning models do not beat linear baselines in predicting gene perturbation effects, thus emphasizing the importance of further method development and evaluation. @const-ae.bsky.social @wkhuber.bsky.social @s-anders.bsky.social

www.nature.com/articles/s41...

Zhang Lab

Contact me if you are interested in joining us fanzhanglab.org
@cuanschutz.bsky.social

Excited 🎉 to receive the grant from the "Leadership Scholars Program in Women’s Health, Autoimmune Diseases, and Immune-Mediated Diseases" supported by NIH Office of Research on Women's Health and AMP-AIM network: orwh.od.nih.gov/in-the-spotl....
We have openings for bioinformaticians and postdocs!

Distinguishing individuals at risk for rheumatoid arthritis from healthy controls!

@fanzhangphd.bsky.social, Deepak A. Rao & team define specific lymphocyte phenotypes during the preclinical stage of rheumatoid arthritis: buff.ly/WdjB8VD

Thanks for sharing!

Really appreciate the huge collaborations with #AcceleratingMedicinesPartnership, Deepak Rao, and @cudeptofmedicine.bsky.social

⭐️ Paper: www.jci.org/articles/vie...
⭐️ Data availability: www.synapse.org/Synapse:syn6... & arkportal.synapse.org/Explore/Data...

JCI - Deep immunophenotyping reveals circulating activated lymphocytes in individuals at risk for rheumatoid arthritis Advertisement

Excited 🎉to share our work led by@juninamo.bsky.social out in JCI @jclinical-invest.bsky.social! Using cross-cohort #singlecell (CyTOF & CITE-seq), we found key T cell expansions in at-risk RA: CCR2+T with Th17/Th22 phenotype and Tph, for #RheumatoidArthritis prevention! www.jci.org/articles/vie...