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243 results on '"Xi, Jianing"'

1. Integrating multi-type aberrations from DNA and RNA through dynamic mapping gene space for subtype-specific breast cancer driver discovery

Author

Xi, Jianing, Deng, Zhen, Liu, Yang, Wang, Qian, and Shi, Wen

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Driver event discovery is a crucial demand for breast cancer diagnosis and therapy. Especially, discovering subtype-specificity of drivers can prompt the personalized biomarker discovery and precision treatment of cancer patients. still, most of the existing computational driver discovery studies mainly exploit the information from DNA aberrations and gene interactions. Notably, cancer driver events would occur due to not only DNA aberrations but also RNA alternations, but integrating multi-type aberrations from both DNA and RNA is still a challenging task for breast cancer drivers. On the one hand, the data formats of different aberration types also differ from each other, known as data format incompatibility. One the other hand, different types of aberrations demonstrate distinct patterns across samples, known as aberration type heterogeneity. To promote the integrated analysis of subtype-specific breast cancer drivers, we design a "splicing-and-fusing" framework to address the issues of data format incompatibility and aberration type heterogeneity respectively. To overcome the data format incompatibility, the "splicing-step" employs a knowledge graph structure to connect multi-type aberrations from the DNA and RNA data into a unified formation. To tackle the aberration type heterogeneity, the "fusing-step" adopts a dynamic mapping gene space integration approach to represent the multi-type information by vectorized profiles. The experiments also demonstrate the advantages of our approach in both the integration of multi-type aberrations from DNA and RNA and the discovery of subtype-specific breast cancer drivers. In summary, our "splicing-and-fusing" framework with knowledge graph connection and dynamic mapping gene space fusion of multi-type aberrations data from DNA and RNA can successfully discover potential breast cancer drivers with subtype-specificity indication., Comment: 14 pages, 5 figures, 1 table

Published
2022
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15. The Crucial Role of Organic Ligands on 2D/3D Perovskite Solar Cells: A Comprehensive Review.

Author

Zhang, Yi, Xi, Jianing, Deng, Yanyu, Liu, Wenwen, Li, Zhuowei, Liu, Chunyu, and Guo, Wenbin

Subjects
*ENERGY levels (Quantum mechanics), *SOLAR cells, *ORGANIC bases, *PEROVSKITE, *SURFACE defects
Abstract

The exceptional performance and potential of 2D/3D perovskites for enhancing the efficiency and stability of perovskite solar cells (PSCs) are extensively studied. However, selecting appropriate organic ligands is critical to this process. The incorporation of suitable ligands into the 2D layer can passivate surface defects, regulate energy level alignment, and significantly improve device stability. Conversely, using unsuitable ligands not only fails to achieve the desired functions, but also negatively impacts device efficiency by hindering charge carrier transport and causing energy level mismatch issues. Therefore, exploring the selection of organic ligands based on different requirements is essential. In this review, recent studies are classified based on the chemical structure of organic ligands, analyze their application requirements, and provide suggestions for choosing optimal organic ligands to construct 2D/3D perovskites. Additionally, the feasibility and necessity of utilizing more efficient organic ligands while providing guidance for future research on 2D/3D PSCs are emphasized. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Evolutionary Mechanism Based Conserved Gene Expression Biclustering Module Analysis for Breast Cancer Genomics.

Author

Yuan, Wei, Li, Yaming, Han, Zhengpan, Chen, Yu, Xie, Jinnan, Chen, Jianguo, Bi, Zhisheng, and Xi, Jianing

Subjects
GENE expression, GENE regulatory networks, FISHER exact test, COMPUTATIONAL statistics, RNA sequencing
Abstract

The identification of significant gene biclusters with particular expression patterns and the elucidation of functionally related genes within gene expression data has become a critical concern due to the vast amount of gene expression data generated by RNA sequencing technology. In this paper, a Conserved Gene Expression Module based on Genetic Algorithm (CGEMGA) is proposed. Breast cancer data from the TCGA database is used as the subject of this study. The p-values from Fisher's exact test are used as evaluation metrics to demonstrate the significance of different algorithms, including the Cheng and Church algorithm, CGEM algorithm, etc. In addition, the F-test is used to investigate the difference between our method and the CGEM algorithm. The computational cost of the different algorithms is further investigated by calculating the running time of each algorithm. Finally, the established driver genes and cancer-related pathways are used to validate the process. The results of 10 independent runs demonstrate that CGEMGA has a superior average p-value of 1.54 × 10−4 ± 3.06 × 10−5 compared to all other algorithms. Furthermore, our approach exhibits consistent performance across all methods. The F-test yields a p-value of 0.039, indicating a significant difference between our approach and the CGEM. Computational cost statistics also demonstrate that our approach has a significantly shorter average runtime of 5.22 × 100 ± 1.65 × 10−1 s compared to the other algorithms. Enrichment analysis indicates that the genes in our approach are significantly enriched for driver genes. Our algorithm is fast and robust, efficiently extracting co-expressed genes and associated co-expression condition biclusters from RNA-seq data. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Dual-Site Crystallization Regulation for Highly Efficient and Stable Perovskite Solar Cells

Author

Xi, Jianing, Zhang, Yi, Gao, Hanxiao, Li, Zhuowei, Liu, Chunyu, and Guo, Wenbin

Abstract

Two-step sequential deposition method has been widely used to fabricate high-performance FAPbI3-based perovskite solar cells (PSCs). However, fast reaction between FAI and PbI2 always leads to an incomplete reaction and poor crystal quality. Here, 1-ethyl-3-methylimidazolium trifluoroacetate (EMIMTFA) is added to the FAI precursor solution, so as to regulate the crystallization behavior and obtain high-quality FAPbI3 perovskite films. The EMIMTFA can chemically interact simultaneously with FAI and PbI2, which will slow down the crystallization process of the perovskite film, achieving enlarged grain size, enhanced orientation and minimized defects, as well as less PbI2 residue. As a result, the PSCs with EMIMTFA achieve a champion efficiency of 24.31%, maintaining 90% of their initial efficiency after 1000 h under dark conditions in a N2-filled glove box. This study provides an efficient dual-site crystallization regulation strategy for the growth of high-quality FAPbI3 films.

Published
2024
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25. Scientometric and Visualized Analysis of Intravoxel Incoherent Motion Magnetic Resonance Imaging: 1988-2021

Author

Li, Youwei, primary, Zhang, Yuanfang, additional, Shang, Honglin, additional, Gao, Qiang, additional, Wen, Qiping, additional, Chen, Na, additional, Zhai, Mingchun, additional, Liu, Jingxin, additional, Zhou, Zhichao, additional, Yang, Guanying, additional, Wang, Wei, additional, Zhang, Zhongming, additional, and Xi, Jianing, additional

Published
2023
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