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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Wenjuan Zhang Alex Lee Mary Qu Yang Information Science Department MidSouth Bioinformatics Center and Bioinformatics Graduate Program University of Arkansas Little Rock and Univ. of Arkansas for Medical Sciences University of Arkansas Little Rock Little Rock USA Biology Department University of Arkansas Little Rock Little Rock USA

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

The immune system plays a central role in recognizing and eliminating cancerous cells. The activity and functionality of immune cells within the tumor microenvironment can significantly impact the prognosis of cancer patients. Leveraging single-cell RNA sequencing technology, we conducted an in-depth investigation into single-cell-based gene signatures for predicting cancer patient survival. The study utilized single-cell RNA sequencing data from breast cancer patients, encompassing over 14,875 immune cells. Gene signatures from major innate immune cell types, including Natural Killer (NK) cells, Macrophages, and Neutrophils, as well as adaptive immune cells, including Cytotoxic T Lymphocytes (T cells) and B lymphocytes (B cells), were identified. To elucidate their relationship in breast cancer patients, we constructed Cox proportional hazard regression models for predicting patient survival, utilizing the concordance index as a measure of prediction model performance. The evaluation incorporated three independent breast cancer patient cohorts: TCGA (The Cancer Genome Atlas program), METABRIC (Molecular Taxonomy of Breast Cancer International Consortium), and UK breast cancer data. Our results showed that the T cell-based signature model outperformed the NK cell-based and Neutrophil-based prediction models. Moreover, the $T$ cell-based signature model exhibited robust performance across independent patient datasets. These findings suggest that gene signatures derived from cells of the adaptive immune system have a better survival prediction value than those derived from the innate immune system, underscoring the potential clinical relevance of harnessing the adaptive immune response for refining prognostic assessments in breast cancer. These insights contribute to advancing our understanding of the complex interplay between immune cells and cancer prognosis, paving the way for more tailored and effective therapeutic interventions.

关键词： Adaptation models Sequential analysis Adaptive systems RNA Predictive models Biomarkers Breast cancer

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Missing Value Recovery for Single Cell RNA Sequencing Data

Missing Value Recovery for Single Cell RNA Sequencing Data

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Wenjuan Zhang William Yang John Talburt Sherman Weissman Mary Qu Yang MidSouth Bioinformatics Center and Bioinformatics Graduate Program University of Arkansas Little Rock and Univ. of Arkansas for Medical Sciences Little Rock USA Computer Science Department Princeton University Princeton USA Information Science Department University of Arkansas Little Rock Little Rock USA Department of Genetics Yale University New Haven USA

ISBN: (纸本)9781665458429

The emergence of single-cell sequencing technologies has enabled the production of high-resolution data at the individual cell level, providing unprecedented opportunities to capture cell population diversity and dissect the cellular heterogeneity of complex diseases. At the same time, relatively high biological and technical noise poses new challenges for single-cell data analysis. The single-cell RNA sequencing (scRNA-seq) data often contains substantial missing values due to gene dropout events. Here, we developed a convolutional neural network based model to recover missing values for scRNA-seq data. We first calculated the probability of dropout employing gamma-normal expectation maximum algorithm. Unlike most existing approaches, our model only recovered the expression values that have a dropout probability larger than a threshold. The mean square error and Pearson correlation coefficient were used to assess the accuracy of predicted expression values. The purity and entropy were computed to measure the homogeneity of cell clusters using imputed gene expression profiles. Across various scRNAseq datasets, our model demonstrated robust performance and achieved comparable or better results compared to the other imputation methods.

关键词： Sequential analysis Scientific computing Computational modeling RNA Sociology Production Mean square error methods

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Characterizing Mutually Exclusive Driver Mutations in Pan-Cancer

Characterizing Mutually Exclusive Driver Mutations in Pan-Ca...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Yifan Zhang William Yang Jun S. Liu Mary Qu Yang MidSouth Bioinformatics Center and Bioinformatics Program University of Arkansas at Little Rock Univ. of Arkansas Medical Sciences Little Rock USA Carnegie Mellon University School of Computer Science Pittsburgh USA Harvard University Cambridge USA

ISBN: (纸本)9781728118680

Efforts on cancer genomic study have revealed that mutations in cancer-driving oncogenes tend to mutually exclusive. To understand this tumorigenic mechanism and common cancer features, large-scale genomic study and molecular analyses across many cancer types are essential. We compiled somatic mutation and transcriptome profiles for a pan-cancer dataset that contains tumor tissue samples from 32 cancer types for over 10,000 patients. The genomic and molecular analysis was applied to four pan-cancer subgroups including pan-gastrointestinal, pan-gynecological, pan-kidney and pan-squamous, as well as the entire patient group. We identified seven genes, TP53, PIK3CA, PTEN, PIK3R1, LRP1B, CDH1, and TRRAP exhibited mutually exclusive mutation patterns across all cancer types (adjusted ). The mutations in these genes occurred in 55.9% of all cancer patients and over 70% of patients with pan-gastrointestinal, pan-gynecological and pan-squamous. Excepting LRP1B, the other six genes are included in the known cancer pathways. Notably, TP53, PIK3CA, and PIK3R1 are involved in over 20 cancer-related pathways. The co-expression network analysis suggested that LRP1B was associated with multiple genes that showed significant enrichment in Wnt signal pathway (adjusted p=0.0023) and ERBB signal pathway (adjusted p=0.0097). We further constructed a regulatory network to infer novel regulations of LRP1B in cancer. In addition, based on the mutations in the seven genes, we divided the patients into different groups for survival analysis. We found that the survival rate of the patient group with TP53 mutations was significantly shorter than in other groups (log rank p = 5.88e-10) in pan-gynecological. Our analysis systematically revealed the mutually exclusive mutation patterns of seven cancer genes across 32 cancer types. It helps us to better understanding the relations among different cancer types and pan-cancer subgroups, providing new insights into common mechanisms underlying diver

关键词： cancer cellular biophysics genetics genomics gynaecology kidney molecular biophysics tumours LRP1B gene cellular biophysics molecular biology Gynecology Genomics Mutually exclusive Tumours PIK3R1 gene Genetics Mutation Kidney Oncogenes

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An Integrating Analysis Reveals Key Regulatory Networks in Breast Cancer

An Integrating Analysis Reveals Key Regulatory Networks in B...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Wenjuan Zhang Yifan Zhang William Yang Dan Li Jun S. Liu Mary Qu Yang MidSouth Bioinformatics Center and Bioinformatics Program University of Arkansas at Little Rock and Univ. of Arkansas Medical Sciences Little Rock USA Department of Computer Science Carnegie Mellon University School of Computer Science Pittsburgh USA Department of Statistics Harvard University Cambridge USA

ISBN: (数字)9781728155845

ISBN: (纸本)9781728155852

Breast cancer is the second most common cancer diagnosed with women in the United States. Treatment of the disease has been substantially improved in recent years. However, the 5-year survival rate of patients with distant metastatic breast cancer is still below 30%. The transcription factors (TFs) regulate the expression of many genes and are commonly mutated in cancer. It has been shown that targeting transcript factor activity is effective in cancer treatment. In this study, we performed a large-scale integrative analysis to reveal the TFs that play essential regulatory roles in disease development. We first constructed expression networks based on gene expression correlation patterns in normal and tumor tissues. Incorporating the Bayesian analysis, TF and target interactions and methylation analysis with the expression networks, we identified a total of 19 regulatory networks consisting of differentially expressed genes. Gene set enrichment analysis of TF targets further revealed four core regulatory networks that were mainly controlled by 10 transcription factors. The dysregulated gene networks and their major TF regulators inferred by our study provide new insights into the regulatory mechanisms of breast cancer and offer new therapeutic targets.

关键词： Breast cancer bioinformatics Bayes methods Rocks Tumors

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Correction to: Identification and characterization of conserved lncRNAs in human and rat brain

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BMC bioinformatics 2018年第1期19卷 181页

作者： Dan Li Mary Qu Yang MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program University of Arkansas at Little Rock and University of Arkansas for Medical Sciences 2801 S. University Avenue Little Rock AR 72204 USA. MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program University of Arkansas at Little Rock and University of Arkansas for Medical Sciences 2801 S. University Avenue Little Rock AR 72204 USA. mqyang@ualr.edu.

After publication of the original article [1], it was noticed that the Acknowledgement statement was incorrect. The original statement reads.

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An integrative approach revealing the landscape of long noncoding RNAs in human brain

An integrative approach revealing the landscape of long nonc...

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2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016

作者： Li, Dan Yang, William Arthur, Carolyn Yoshigoe, Kenji Liu, Jun Tong, Weida Yang, Mary MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program University of Arkansas at Little Rock University of Arkansas for Medical Sciences 2801 S. University Avenue Little RockAR72204 United States Harvard T. H. Chan School of Public Health Harvard University One Oxford Street CambridgeMA02138 United States United States Food and Drug Adminstration National Center for Toxicological Research 3900 NCTR Road JeffersonAR72079 United States Department of Informatics Science MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program University of Arkansas at Little Rock University of Arkansas for Medical Sciences 2801 S. Univ. Avenue Little RockAR72204 United States

ISBN: (纸本)9781509055104

Long noncoding RNAs (lncRNAs) function as regulators and play critical roles in diverse biological processes, however, the majority of lncRNAs are not characterized, and their roles in regulation remain to be elucidated. Present RNA-seq assembly approaches are insufficient to identify complete full-length transcripts and often reveal excessive amount of single-exon lncRNAs, many of them tend to be the fragments of transcripts. Here we developed an integrated approach that combined the results from reference-guided and de novo assembly to systematically identify lncRNAs. Experiments on simulated data and real RNA-seq data showed that our method effectively yielded a more comprehensive set of lncRNAs. We applied the method on 299 human brain RNA-seq samples with a total of 4.5 billion raw reads. The results yield a comprehensive lncRNAs mapping in human brain, allowing discovery of novel lncRNAs and further function annotation. © 2016 IEEE.

关键词： RNA

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Author Correction: Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways

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Scientific reports 2018年第1期8卷 8995页

作者： Renchu Guan Xu Wang Mary Qu Yang Yu Zhang Fengfeng Zhou Chen Yang Yanchun Liang Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun 130012 China. MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program of University of Arkansas at Little Rock and Univ. of Arkansas Medical Sciences Little Rock AR 72204 USA. Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing 100093 China. College of Earth Sciences Jilin University Changchun 130061 China. yangc616@***. Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun 130012 China. ycliang@***. Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Zhuhai 519041 China. ycliang@***.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

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