I am a Postdoc Fellow at Lab of Artificial Intelligence for Cell Engieering led by Dr. Mo Lotfollahi, Wellcome Sanger Institute, Cambridge as of September 2025. I was a Postdoc Associate at Yale University from June 2024 to August 2025 supervised by Dr. Hyojung Seo. I received my PhD supervised by Dr. Kun Qian in Shanghai Jiao Tong University in June, 2023. My research focuses on computational biology. My long-term research goal is to advance the understanding of biological problems with computational algorithms.

If you are interested in collaboration with me about computational biology, please contact mengji.zhang0809@gmail.com.

1. 2025/09 I joined the Artificial Intelligence for Cell Engineering Lab led by Dr. Mo Lotfollahi!
2. 2025/07 My first-authored paper was published in Nature Biomedical Engineering !

• In computational metabolomics, I am interested in increasing the profile reproducibility and decoding the mapping space between metabolites and measured profiles.
• In computational transcriptomics, I am interested in removing the unwanted variations brought by batch, platform, purity, and other unknown effects.
• In molecular modeling, I am interested in the quantatitive structure-property relationship.

• I am interested in neural dynamics between neural activities and behaviors.

DeepAdapter is available as a PyPi package. This tool can remove unwanted variations caused by batch effects, platform differences, purity, and other unknown factors in transcriptomic studies. You can find the source code on GitHub. Try it by running: pip install deepadapter.

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  Integrating the large-scale and heterogenous transcriptome datasets. Learn more

  Accurate identification of true biological signals from diverse undesirable variations in large-scale transcriptomes is essential for downstream discoveries. Herein, we develop a universal deep neural network, called DeepAdapter, to eliminate various undesirable variations from transcriptomic data.

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  Decoding the relationship between olfactory perception and molecular structure. Learn more

  While visual and auditory information have been decoded, predicting olfactory information remains challenging. Herein, we develop a deep learning model called Mol-PECO to predict olfactory perception from molecular structures.

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  Stabilizing the metabolic profilings. Learn more

  Although mass spectrometry (MS) of metabolites has the potential for real-time monitoring of patient status, its application is limited by data quality. Here, we report the generation of a deep stabilizer for ultra-fast, label-free MS detection.

Google Scholar Profile

1. Mengji Zhang et al. A self-adaptive and versatile tool for eliminating multiple undesirable variations from transcriptome. Nature Biomedical Engineering 2025, 1-14 (First author, IF 26.8). Read more

1. Mengji Zhang* et al. A deep position-encoding model for predicting olfactory perception from molecular structures and electrostatics. npj Systems Biology and Applications 2024, 10, 76 (First author, IF 4.3). Read more
2. Shuyi Jin#, Mengji Zhang# et al. et al. A deep graph model for the signed interaction prediction in biological network. preprint (Co-first author). Read more
3. Haiyang Su … Mengji Zhang et al. Fast Label‐Free Metabolic Profile Recognition Identifies Phenylketonuria and Subtypes. Advanced Science 2024, 2305701. Read more
4. Shunxiang Li … Mengji Zhang et al. Serum metabolic fingerprints characterize systemic lupus erythematosus. Advanced Science 2024, 2304610. Read more

1. Weikang Shu#, Mengji Zhang# et al. An Alloy Platform of Dual-Fingerprints for High-Performance Stroke Screening. Advanced Functional Materials 2023, 2210267. (Co-first author, IF 18.81). Read more
2. Jing Cao#, Yan Xiao#, Mengji Zhang# et al. Deep learning of dual plasma fingerprints for high-performance infection classification. Small 2023, 202206349. (Co-first author, IF 15.15). Read more

1. Lin Wang#, Mengji Zhang#, Xufeng Pan#, Mingna Zhao# et al. Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification. Advanced Science 2022, 2203786. (Co-first author, IF 16.80). Read more
2. Xia Yin#, Jing Yang#, Mengji Zhang#, Xinyao Wang# et al. Serum Metabolic Fingerprints on Bowl-Shaped Submicroreactor Chip for Chemotherapy Monitoring. ACS Nano 2022. (Co-first author, IF 15.88). Read more
3. Mengji Zhang# et al. Plasma metabolic signature of cardiovascular and cerebrovascular diseases from a large cohort study. Journal of Shanghai Jiao Tong University (Medical Science) 2022, 2, 259. Read more
4. Yida Huang … Mengji Zhang et al. Diagnosis and prognosis of breast cancer by high-performance serum metabolic fingerprints. Proceedings of the National Academy of Sciences 2022, 119, 12, e2122245119. Read more

1. Mengji Zhang et al. Ultra-Fast Label-Free Serum Metabolic Diagnosis of Coronary Heart Disease via a Deep Stabilizer. Advanced Science 2021, 8, 2101333. (First author, IF 16.80). Read more
2. Haiyang Su … Mengji Zhang et al. Plasmonic alloys reveal distinct metabolic phenotype of early gastric cancer. Advanced Materials 2021, 33, 2007978. Read more

1. Jing Yang … Mengji Zhang et al. Urine Metabolic Fingerprints Encode Subtypes of Kidney Diseases. Angewandte Chemie-International Edition 2020, 59, 1703. Read more
2. Jing Cao … Mengji Zhang et al. Metabolic fingerprinting on synthetic alloys for medulloblastoma diagnosis and radio therapy evaluation. Advanced Materials 2020, 32, 2000906. Read more

1. Mengji Zhang et al. Predicting Stock Trends Based on News Events. Data Analysis and Knowledge Discovery 2019, 3, 11. (First author). Read more
2. Xinyu Wang … Mengji Zhang et al. An Effective Optimization Algorithm for Application Mapping in Network-on-Chip Designs. IEEE Transactions on Industrial Electronics 2019, 67, 5798. (IF 8.14). Read more