Author

Weilai Chi

Peking UniversityORCID 0000-0002-0637-747XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Assay:ChIP-seq· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Fang‐Xiang WuiD· 1 shared Ganqi DengiD· 1 shared Minghua DengiD· 1 shared Peiqiang YuiD· 1 shared Xiao LuoiD· 1 shared

Activity by Year

2019-2026 · peak 1/yr

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5 top collaborators · 4 mutual co-author links

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1 shared paperBioinformatics

Fang‐Xiang WuORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintSep 18, 2026

One-dimensional CNNs for Near-Infrared Prediction of Protein and Moisture in Cereal Grains: The Effects of Architecture and Input Preparation

Ganqi Deng, Weilai Chi, Peiqiang Yu, Fang‐Xiang Wu

This study compares CNNs, PLSR, SVR, and XGBoost for NIR prediction of protein and moisture in cereal grains, finding that model accuracy depends more on input preparation and sample size than network architecture.

DOI 10.64898/2026.09.12.751160

machine learning (classical)deep learning

𝕏 PostBluesky

BioinformaticsFrontiers in GeneticsPeer-reviewedNov 19, 2019

Deepprune: Learning efficient and interpretable convolutional networks through weight pruning for predicting DNA-protein binding

Xiao Luo, Weilai Chi, Minghua Deng

Deepprune prunes and fine-tunes CNNs to improve motif inference with fewer kernels, achieving better performance on simulated and ChIP-seq data.

20 citations

ChIP-seqdeep learning

𝕏 PostBluesky