Haopeng Xiao
@haopengxiao.bsky.social
Assistant Professor at Stanford Biochemistry & Stanford Cancer Institute. We combine mass spectrometry, data science, and mechanistic biology to understand protein function and druggability. xiaolab.space
Excited to share our new findings in @science.org on how the DRT3 bacterial defense system uses a reverse transcriptase that builds DNA repeats without a nucleic acid template. Microbes never cease to amaze! www.science.org/doi/10.1126/...
Science | AAAS
science.org
Interested in the mechanisms of aging and 'rejuvenation'? Our lab at Stanford has a Research Assistant/Lab manager position open!! Apply and join our awesome team! 😎 (please share) careersearch.stanford.edu/jobs/life-sc...
Life Science Research Professional 1 in School of Medicine, Stanford, California, United States
The Brunet lab in the Department of Genetics at Stanford University (https://web.stanford.edu/group/brunet/) is seeking an independent and motivated.....
careersearch.stanford.edu
If you’re at #HUPO2025, be sure to stop by my poster and learn about the latest developments in the BioPlex project!
For those who perform thermal proteome profiling (TPP) experiments, you might find this new data analysis approach (and open source Python software) useful. Developed by postdoc @jfhevler.bsky.social 🙏 arxiv.org/abs/2508.096...
Thermal Tracks: A Gaussian process-based framework for universal melting curve analysis enabling unconstrained hit identification in thermal proteome profiling experiments
Thermal Tracks is a Python-based statistical framework for analyzing protein thermal stability data that overcomes key limitations of existing thermal proteome profiling (TPP) work-flows. Unlike stand...
arxiv.org
Excited to share that I’ve started my lab as an assistant professor at the University of Texas Southwestern Medical Center in Dallas. Thank you to all the mentors, friends, and colleagues who have supported me throughout the years!
The results are in: top codes in Stanford #RNA 3D Folding @kaggle.com competition are competitive with CASP16-leading humans Vfold, beat AlphaFold 3. Top team’s trick was template-based modeling, not #DeepLearning. Congrats: john, odat, Eigen, + all 1706 participants! www.kaggle.com/competitions...
We are seeking a life science research professional/technician (medcareers.stanford.edu/job/stanford...) and a postdoctoral fellow(postdocs.stanford.edu/prospective/...). If you are interested in using mass spec and machine learning to understand protein function and druggability please reach out!