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Feifei Cui
Researcher at University of Tokyo
Publications - 9
Citations - 106
Feifei Cui is an academic researcher from University of Tokyo. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 1, co-authored 2 publications receiving 5 citations.
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Journal ArticleDOI
Toward smarter management and recovery of municipal solid waste: A critical review on deep learning approaches
Kun Fung Lin,Youcai Zhao,Jiahong Kuo,Hao Deng,Feifei Cui,Zilong Zhang,Meilan Zhang,Chunlong Zhao,Xiao Feng Gao,Tao Zhou,Tao Wang +10 more
TL;DR: In this article , a critical review for deep learning and its application in municipal solid waste management (MSWM) is provided, where a body of deep learning applications have been reviewed according to their engagement in waste collection, transportation and final disposal.
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Goals and approaches for each processing step for single-cell RNA sequencing data
TL;DR: An overview of the goals and most popular computational analysis tools for the quality control, normalization, imputation, feature selection and dimension reduction of scRNA-seq data is provided.
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Critical downstream analysis steps for single-cell RNA sequencing data.
TL;DR: A review of the most widely used methods for critical downstream analysis steps (i.e., clustering, trajectory inference, cell-type annotation and integrating datasets) is presented in this article.
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Protein–DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data
TL;DR: An overview of the development progress of computational methods for protein–DNA/RNA interactions using machine intelligence techniques is provided and the advantages and shortcomings of these methods are summarized.
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webSCST: an interactive web application for single-cell RNA-sequencing data and spatial transcriptomic data integration
TL;DR: A new web-based scRNA-seq analysis tool that integrates well-organized spatial transcriptome sequencing datasets categorized by species and organs, provides a user-friendly interface for raw single-cell processing with popular integration methods, and allows users to submit their raw sc RNA-seq data once to obtain predicted spatial locations for each cell type is introduced.