Zhiwei Fan's research while affiliated with University of Texas Health Science Center at Houston and other places
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Publications (9)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of coronavirus disease 19 (COVID-19), has caused a global health crisis. Despite ongoing efforts to treat patients, there is no universal prevention or cure available. One of the feasible approaches will be identifying the key genes from SARS-CoV-2-infected cells. SAR...
Tumors are often polyclonal due to copy number alteration (CNA) events. Through the CNA profile, we can understand the tumor heterogeneity and consistency. CNA information is usually obtained through DNA sequencing. However, many existing studies have shown a positive correlation between the gene expression and gene copy number identified from DNA...
Adenosine-to-inosine (A-to-I) RNA editing, constituting nearly 90% of all RNA editing events in humans, has been reported to contribute to the tumorigenesis in diverse cancers. However, the comprehensive map for functional A-to-I RNA editing events in cancers is still insufficient. To fill this gap, we systematically and intensively analyzed multip...
In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location and provides novel insights into diverse spatially related biological contexts. Even t...
A-to-I RNA editing, constituting nearly 90% of all RNA editing events in human, has been reported to contribute to the tumorigenesis in diverse cancers. However, there was not enough attention to the functional A-to-I RNA editing events in human cancers. To fill this gap, we systematically and intensively analyzed multiple tumorigenic mechanisms of...
The rapid development of single-cell technologies allows for dissecting cellular heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of cellular heterogeneity will boost our understanding of complex biological systems or processes, including cancer, immune system and chronic diseases, thereby providing valuable...
Single-cell RNA-sequencing (scRNA-seq) enables the characterization of transcriptomic profiles at the single-cell resolution with increasingly high throughput. However, it suffers from many sources of technical noises, including insufficient mRNA molecules that lead to excess false zero values, termed dropouts. Computational approaches have been pr...
The basic Generative Adversarial Networks (GAN) model is composed of the input vector, generator, and discriminator. Among them, the generator and discriminator are implicit function expressions, usually implemented by deep neural networks. GAN can learn the generative model of any data distribution through adversarial methods with excellent perfor...
Single-cell RNA-seq (scRNA-seq) enables the characterization of transcriptomic profiles at the single-cell resolution with increasingly high throughput. However, it suffers from many sources of technical noises, including insufficient mRNA molecules that lead to excess false zero values, often termed dropouts. Computational approaches have been pro...
Citations
... Similarly, transcriptome sequencing analysis of ccRCC against adjacent normal tissues in our research demonstrated that APOL1 was highly expressed in the cancer samples with high mortality rates. Furthermore, adenosine to inosine RNA editing events is notably found in the 3'UTR region of APOL1 across 33 cancer types from TCGA dataset (45). This event leads to the upregulation of APOL1, and is associated with poor overall survival of lung adenocarcinoma (46). ...
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... There have been studies that have identified new subpopulations and transcriptional heterogeneity in what were previously thought to be homogeneous compartments by ST techniques, including the discovery of LeprþCxcl12-enriched reticulocytes as a major source of pro-hematopoietic factors, and there have also been studies that have clarified the molecular characterization and localization of bone marrow-resident cell types (Al-Sabah et al. 2019). In addition, differentiation and development in the heart, brain, endocrine, and other tissues are continuing to deepen with ST technologies (Fan et al. 2023;Luo et al. 2021;Mantri et al. 2021;Matsumoto and Yamamoto 2024;Zhong et al. 2023), improving our knowledge of human development and related diseases. In conclusion, ST is a valuable tool for the study of stem cell biology and can provide important insights into the molecular mechanisms of stem cell development and differentiation. ...
... This indicates a task-agnostic understanding of knowledge in these domains, inspiring us to explore its adoption for single-cell omic research. However, current machine-learning-based methods in single-cell research are rather scattered, with specific models dedicated to distinct analysis tasks [24][25][26] . As a result, the datasets used in each study are often limited in breadth and scale 7 . ...
... Thus, these models represent an alternative for feature extraction from high-dimensional data in addition to classical statistical models using singular vector decomposition [12,13]. Moreover, generative adversarial networks (GANs)-based models were developed for single-cell data imputations [14,15] and data augmentation/generation [16], for instance. ...
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... Deep learning-based methods have shown potential in medical image classification, but they often face due to their reliance on extensive training data [1], [2]. This challenge is especially notable in the classification of DUV images with a limited number of subjects, given its novelty [3]. ...
... The imputation accuracy was shown to be significantly improved compared with traditional imputation techniques which ignore the within sample correlation. Recently, a few other approaches have been proposed for imputation of time series gene expression data such as imputeTS (Moritz and Bartz-Beielstein, 2017), SIMPLEs (Hu et al., 2020), and scIGANs (Xu et al., 2020). ...
