Browsing by Author "Liu, Jiannan"

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    CGPE: A user-friendly gene and pathway explore webserver for public cancer transcriptional data
    Liu, Jiannan; Dong, Chuanpeng; Liu, Yunlong; Wu, Huanmei
    High throughput technology has been widely used by researchers to understand diseases at the molecular level. Database and servers for downloading and analyzing these publicly data is available as well. But there is still lacking tools for facilitating researchers to study the function of genes in pathways views by integrated public omics data.
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    CGPE: an integrated online server for Cancer Gene and Pathway Exploration
    (Oxford University Press, 2021) Liu, Jiannan; Dong, Chuanpeng; Liu, Yunlong; Wu, Huanmei; BioHealth Informatics, School of Informatics and Computing
    Summary: Cancer Gene and Pathway Explorer (CGPE) is developed to guide biological and clinical researchers, especially those with limited informatics and programming skills, performing preliminary cancer-related biomedical research using transcriptional data and publications. CGPE enables three user-friendly online analytical and visualization modules without requiring any local deployment. The GenePub HotIndex applies natural language processing, statistics and association discovery to provide analytical results on gene-specific PubMed publications, including gene-specific research trends, cancer types correlations, top-related genes and the WordCloud of publication profiles. The OnlineGSEA enables Gene Set Enrichment Analysis (GSEA) and results visualizations through an easy-to-follow interface for public or in-house transcriptional datasets, integrating the GSEA algorithm and preprocessed public TCGA and GEO datasets. The preprocessed datasets ensure gene sets analysis with appropriate pathway alternation and gene signatures. The CellLine Search presents evidence-based guidance for cell line selections with combined information on cell line dependency, gene expressions and pathway activity maps, which are valuable knowledge to have before conducting gene-related experiments. In a nutshell, the CGPE webserver provides a user-friendly, visual, intuitive and informative bioinformatics tool that allows biomedical researchers to perform efficient analyses and preliminary studies on in-house and publicly available bioinformatics data.
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    Develop the Disease Specific Bioinformatics Platforms with Integrated Bioinformatics Data
    (2022-11) Liu, Jiannan; Yan, Jingwen; Zhang, Jie; Huang, Kun; Zhang, Chi; Richardson, Timothy I.; Wu, Huanmei
    With the advance of multiple types of omics technology and corresponding analytical methods, various type of bioinformatic data have become available. Mining and integrating these data for analysis will provide valuable insights for disease mechanism investigation, drug target identification and new drug development. However, most of the omics data are large size, heterogeneous, and complex, it is challenging for biomedical researchers to mine the data for relevant evidence, especially for those with limited computational skills. In this thesis, I aimed to develop disease specific platforms integrated with multimodal bioinformatic data types to provide researchers with strong bioinformatics support. To achieve this goal, I explored advanced transcriptomic data analytical methods and proposed a novel biomarker for the prediction of overall survival of colon cancer patients, then prototyped a user-friendly patient oriented clinical decision support system to provide accurate and intuitive colorectal cancer risk factor assessment. With the experience of the transcriptomic data analytical methods and the web-based application development, I further designed and implemented Cancer Gene and Pathway Explorer which is an integrative bioinformatics webserver that can be used for cancer publication trends investigation, gene set enrichment analysis with integrated data, and optimal cancer cell line identification. Based on the framework of CGPE, I developed another bioinformatics platform focusing on Alzheimer’s disease, called Alzheimer’s Disease Explorer, which is a first-of-its-kind bioinformatics server, providing rich bioinformatic support from literature, omics and chemical data to facilitate researchers in ND drug development field. By accomplishing a series of work in my thesis, I have shown that integrated disease specific bioinformatics platforms can provide great value to the research community by allowing 1.) fast and accurate investigation of currently available literature, 2.) quick hypothesis generation and validation using transcriptomic datasets, 3.) multi-dimension drug target evaluation and 4) fast querying of published bioinformatics outcomes.
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    Highly robust model of transcription regulator activity predicts breast cancer overall survival
    (BMC, 2020) Dong, Chuanpeng; Liu, Jiannan; Chen, Steven X.; Dong, Tianhan; Jiang, Guanglong; Wang, Yue; Wu, Huanmei; Reiter, Jill L.; Liu, Yunlong; Medical and Molecular Genetics, School of Medicine
    Background: While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes. Methods: Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome. Result: We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients. Conclusion: Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression.
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    A patient-oriented clinical decision support system for CRC risk assessment and preventative care
    (BioMed Central, 2018-12-07) Liu, Jiannan; Li, Chenyang; Xu, Jing; Wu, Huanmei; Biohealth Informatics, School of Informatics and Computing
    Colorectal Cancer (CRC) is the third leading cause of cancer death among men and women in the United States. Research has shown that the risk of CRC associates with genetic and lifestyle factors. It is possible to prevent or minimize certain CRC risks by adopting a healthy lifestyle. Existing Clinical Decision Support Systems (CDSS) mainly targeted physicians as the CDSS users. As a result, the availability of patient-oriented CDSS is limited. Our project is to develop patient-oriented CDSS for active CRC management.
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    Text mining and portal development for gene-specific publications on Alzheimer's disease and other neurodegenerative diseases
    (Springer Nature, 2024-04-17) Liu, Jiannan; Wu, Huanmei; Robertson, Daniel H.; Zhang, Jie; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Background: Tremendous research efforts have been made in the Alzheimer's disease (AD) field to understand the disease etiology, progression and discover treatments for AD. Many mechanistic hypotheses, therapeutic targets and treatment strategies have been proposed in the last few decades. Reviewing previous work and staying current on this ever-growing body of AD publications is an essential yet difficult task for AD researchers. Methods: In this study, we designed and implemented a natural language processing (NLP) pipeline to extract gene-specific neurodegenerative disease (ND) -focused information from the PubMed database. The collected publication information was filtered and cleaned to construct AD-related gene-specific publication profiles. Six categories of AD-related information are extracted from the processed publication data: publication trend by year, dementia type occurrence, brain region occurrence, mouse model information, keywords occurrence, and co-occurring genes. A user-friendly web portal is then developed using Django framework to provide gene query functions and data visualizations for the generalized and summarized publication information. Results: By implementing the NLP pipeline, we extracted gene-specific ND-related publication information from the abstracts of the publications in the PubMed database. The results are summarized and visualized through an interactive web query portal. Multiple visualization windows display the ND publication trends, mouse models used, dementia types, involved brain regions, keywords to major AD-related biological processes, and co-occurring genes. Direct links to PubMed sites are provided for all recorded publications on the query result page of the web portal. Conclusion: The resulting portal is a valuable tool and data source for quick querying and displaying AD publications tailored to users' interested research areas and gene targets, which is especially convenient for users without informatic mining skills. Our study will not only keep AD field researchers updated with the progress of AD research, assist them in conducting preliminary examinations efficiently, but also offers additional support for hypothesis generation and validation which will contribute significantly to the communication, dissemination, and progress of AD research.
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    Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice
    (BMC, 2020-09-21) Liu, Jiannan; Dong, Chuanpeng; Jiang, Guanglong; Lu, Xiaoyu; Liu, Yunlong; Wu, Huanmei; BioHealth Informatics, School of Informatics and Computing
    Background Colon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from pathological indicators. Transcription factors function as critical regulators in all aspects of cell life, but transcription factors-based biomarkers for colon cancer prognosis were still rare and necessary. Methods We implemented an innovative process to select the transcription factors variables and evaluate the prognostic prediction power by combining the Cox PH model with the random forest algorithm. We picked five top-ranked transcription factors and built a prediction model by using Cox PH regression. Using Kaplan-Meier analysis, we validated our predictive model on four independent publicly available datasets (GSE39582, GSE17536, GSE37892, and GSE17537) from the GEO database, consisting of 925 colon cancer patients. Results A five-transcription-factors based predictive model for colon cancer prognosis has been developed by using TCGA colon cancer patient data. Five transcription factors identified for the predictive model is HOXC9, ZNF556, HEYL, HOXC4 and HOXC6. The prediction power of the model is validated with four GEO datasets consisting of 1584 patient samples. Kaplan-Meier curve and log-rank tests were conducted on both training and validation datasets, the difference of overall survival time between predicted low and high-risk groups can be clearly observed. Gene set enrichment analysis was performed to further investigate the difference between low and high-risk groups in the gene pathway level. The biological meaning was interpreted. Overall, our results prove our prediction model has a strong prediction power on colon cancer prognosis. Conclusions Transcription factors can be used to construct colon cancer prognostic signatures with strong prediction power. The variable selection process used in this study has the potential to be implemented in the prognostic signature discovery of other cancer types. Our five TF-based predictive model would help with understanding the hidden relationship between colon cancer patient survival and transcription factor activities. It will also provide more insights into the precision treatment of colon cancer patients from a genomic information perspective.