Cao Zhenxiao

• Location: Xi’an, Shaanxi, P.R. China
• Email: realalanc@qq.com / realalanc029@gmail.com / alancao@stu.xjtu.edu.cn

News about me!

• “MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction” has been accepted by ICLR2025!!!!
• Re-submit out paper “Post-translation modification(PTM) prediction via deep learning” to ICLR2025
• Former work ‘Gene expression prediction in long sequence NLP algorithms’ is cancelled.
• Starting a new work ‘Protein dynamics generation model’ with Westlake University
• Starting a new work ‘3D protein image structural embedding’ with UNC
• Starting a new work ‘Rare cell identification via LLM’

Education

Bachelor of Science in Computer Science
Xi’an Jiaotong University, 2025
Grade: 85.54/100

• Major courses: Advanced Mathematics, Linear Algebra and Geometry, University Physics, Foundation of Life Science, Thinking of Big Data and Innovation, Data Structure and Algorithms, Discrete Mathematical Structures, Object-Oriented Programming (OOP), Introduction of Computer Systems (ICS), Probability Theory and Stochastic process, Bioinformatics, Artificial Intelligence, Computer Organization, Principles of Computer Networks, The Principle of Operating System

Publications

Conference Papers

• “MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction” has been accepted by ICLR2025

Papers Under Review

• “How Effective is In-Context Learning with Large Language Models for Rare Cell Identification in Single-Cell Expression Data?” is under review of ECML-PKDD 2025

Research Interests

AI for Biology

• LLMs for biologial moleculars
• Predictions of the feature and structure of biologial moleculars
• Explainable AIs in biology
• AIGC in biology (like protein design, small molecular design)
• Biological image analysis and CV for biology

Machine Learning

• DL network structure design
• Explainable AIs
• Causal inference

Feel free to connect me for any research interests and communications

Research Experience

Internship at SenseTime
Year: 2023
Status: Finished

• Developed a smart sensor to monitor Alzheimer patients via CNN on an embedding device, involving quantization from a Yolov5-based inference network. Tasks included face identification, landmark detection, and signal processing for heart rate.

First-author-level works

• MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction
  Westlake University, with Cheng Tan Google Scholar and Stan Z.Li Google Scholar Year: 2024 Status: Accepted by ICLR2025
  • Developed a deep learning framework for PTM prediction, aiming to achieve state-of-the-art results in multi-class prediction using sequence and structural data.
  • Our model is based on VQ-VAE, which constructs graphs by capturing sequence neighbors and structural neighbors. It is divided into two tasks: pre-training the VQ-VAE and extracting intermediate embeddings for downstream tasks.
• Protein dynamics generation model
  Westlake University, with Cheng Tan Google Scholar and Stan Z.Li Google Scholar Year: 2024 Status: Processing
  • The research objective is to develop a model that, by learning from the molecular structure, chemical properties of protein-ligand complexes, and short-term molecular dynamics trajectories (the first few frames), can predict and output longer-term molecular dynamics trajectories of protein-ligand complexes.
• Data refine in protein prediction databasel
  Westlake University, with Cheng Tan Google Scholar and Stan Z.Li Google Scholar Year: 2024 Status: Processing
  • As can be known, protein prediction data often has biases against the ground truth. Our task is to build an algorithm to pair-to-pair refine and enhance the dataset.
• 3D protein image structural embedding
  University of North Carolina at Chapel Hill, with Wenhao ZhengGoogle Scholar and Huaxiu Yao Google Scholar Year: 2024 Status: Processing
  • Training a embedding vector by VAE-like models, and providing it for downstream tasks like atom prediction.
• How Effective is In-Context Learning with Large Language Models for Rare Cell Identification in Single-Cell Expression Data?
  Year: 2024 Status: Under Review
  • Using LLM as an extractor to identify rare cells from data, which can be used in different biological domains. We found that cross-query based in-context learning provides stable performance, independent of the data size, and achieves SOTA on some datasets.
• Causal Prediction in microsatellite instability (MSI) and gene network
  Xi’an Jiaotong University, with Xiaofei Yang Google Scholar and Kai Ye Google Scholar Year: 2022 Status: Paused
  • Developed a toolbox for causal prediction between gene networks and MSI, involving correlation filtering, causal graph generation, and correctness checking via causal inference algorithms.

Other works

• Breast Cancer Medecal Image Dataset in Rare Classes
  Peiking University, Year: 2024 Status: Processing
  • Developing a medical image dataset, including different types of medical images in different types of breast cancers. Focusing on rare cases, our dataset tends to provide a competative dataset and benchmarks for future downstream tasts.
• Uncertainty Guided Generalised Federated Learning
  Rice University, Year: 2024 Status: Processing, expected to submit soon
  • This study employs Gaussian noise in a federated learning model for domain generalization, enhancing the model’s adaptability and robustness across diverse domains while ensuring data privacy.
• Gene expression prediction in Mamba
  University of North Carolina at Chapel Hill, Year: 2024 Status: Cancelled
  • Trying to predict cis-acting elements by using Mamba that is capable to handle long sequences.
• Monomer inference in fruit fly’s centromere
  Xi’an Jiaotong University, Year: 2023 Status: Processing, expected to submit soon
  • Analyzed the structure of the centromere in Drosophila melanogaster, focusing on monomer analysis from long-read gap-free sequence data.

Awards

• China National Biology Olympics (CNBO) Silver
  Year: 2020
  • Awarded a Silver Medal in CNBO for proficiency in biology.
• International Genetic Machine Olympics (iGEM) Gold
  Year: 2022-2023
  • Awarded Gold as a member of the iGEM team for innovative work on cellular automata modeling.
  • The whole project is focused on building a light-induced autolysis on engineered bacteria to emerge genocides, while the cell lysis cycle controls the amount of genocides.
• China International College Students’ Innovation Campaign Honorable Mention
  Year: 2022-2023
  • Received Honorable Mention for research on causal prediction in microsatellite instability and gene networks.

Other Contributions

• Building py-Cicero as a free open-source developer
  • Developing a Python-version Cicero software for calculating single-cell chromatin co-accessibility.
• PR and bug fix in PDBminer
  • Contributed bug fixes and enhancements to PDBminer, an open-source software for retrieving structural data via UniProt IDs.

Professional Skills

Language

• Chinese (native)
• English (TOEFL 103)

Programming

• Familiar with basic Linux commands
• Proficient in Python; competent in R, C, C++, and Java

Biology

• Proficient in biochemistry, molecular biology, cellular biology, genetics, and other related fields
• Experienced in wet lab techniques such as PCR and electrophoresis.

Other Interesting Thinng About Me

Model UN

• I was a paticipant in Model UN in my middle school and high school times, with getting some prizes.

Language Learning

• I am a fan in language learning, I am learning Russian, and having further plan in learning Japanese.

Leadership Trainning

• I once joined a leadership trainning held by ITCILO, learned some skills and facts about life planning.

AI4Bio Community

• I am interested in and willing to join or develop a AI4Bio community.