Author

Max Lee

University of California, Berkeley

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Neuroscience· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:cell type annotation· 2 Approach:benchmark study· 1 Research Focus:clustering· 1 Research Focus:deconvolution· 1 Approach:experimental assay· 1 Organism:human· 1 Assay:imaging· 1

Frequent Co-Authors

Abigail SheaiD· 1 shared Alejandra BrunaiD· 1 shared Ali Dariush· 1 shared Alina BollhageniD· 1 shared Alireza MolaeinezhadiD· 1 shared Andrew J. L. RothiD· 1 shared Andrew McPhersoniD· 1 shared Anubhav SinhaiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperBioinformatics

Abigail SheaORCID

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3 papers

Recent·Most cited

Automated compartment-based quantification reveals distinct patterns of cutaneous innervation with aging and microbial colonization

Henry Le Chang, Max Lee, Lauren Schneider, Élora Midavaine, Beatriz Caroline de Moraes, Crystal A. Chu, Sakeen W. Kashem

The paper develops an automated image analysis pipeline to quantify cutaneous innervation in different skin compartments, revealing age and microbial effects on nerve area and length.

bioRxiv · Oct 13, 2026 · PreprintNeuroscience

Serval: A modular framework for decoding imaging based spatial transcriptomics data

Jenkin Tsui, Naila Adam, Woongcheol Choi, Luna Y Liu, C. Cárcamo Flores, Shadi Ansari, Esther Kong, Yukta Thapliyal, Hakwoo Lee, Shahid Haider, Issac Von Riedemann, Ciara H. O’Flanagan, Mohammad Al Sa’d, Bruno Albuquerque, Hamid Raza Ali, , Nicholas A. Walton

Serval is a modular framework for decoding spatial transcriptomics data that introduces Cosine, a method improving transcript recovery and correlation with expression references.

Bioinformatics · Sep 27, 2026Peer-reviewedBioinformatics

Interpreting biochemical text with language models:a machine learning framework for reaction extraction and cheminformatic validation

Daven Lim, Swathi Badrinarayanan, Kira Sterling, Guru Rajesh, Eshaan Mistry, Daphne Yang, Max Lee, Kenneth Bryan Hsu, Mrunali Manjrekar, Cassandra Areff, Phil Xie, Ivan Kristanto, Arjun Chandran, J. Christopher Anderson

This paper presents a framework using LLMs and cheminformatics to extract and validate enzymatic reactions from scientific text, showing accurate inference of incomplete reactions.

bioRxiv · May 15, 2025 · PreprintBioinformatics