I am a dedicated researcher specializing in the development of system software platforms and data analysis algorithms, with a strong focus on proteomics and metabolomics. As biological sciences continue to advance rapidly, the explosive growth of biological big data has created an increasing demand for efficient analysis and interpretation. In this context, my research aims to build effective software platforms and algorithms that enable researchers to extract valuable insights from complex biological data with greater accuracy and efficiency.

My expertise lies in the construction of computational tools for proteomics and metabolomics, ensuring that large-scale biological datasets can be processed, analyzed, and interpreted in meaningful ways. In the field of proteomics, I have contributed to the implementation of MSFragger within Proteome Discoverer, providing an efficient and scalable search engine for protein identification (Chang et al., 2022, Journal of Proteome Research). Additionally, my work on tailored proteomic strategies for rheumatoid arthritis has helped refine therapeutic decision-making by identifying key biomarkers for disease progression and treatment response (Chen et al., 2023, Clinical Proteomics). I have also worked on biomarker discovery using microRNA profiling for detecting encapsulating peritoneal sclerosis, showcasing the integration of computational analysis with clinical applications (Wu et al., 2022, Clinica Chimica Acta).

My research extends beyond proteomics into metabolomics software development, where I have co-authored a practical guide to metabolomics software development, offering best practices and methodological frameworks for designing computational tools in this field (Chang et al., 2021, Analytical Chemistry). Through my involvement in these projects, I have gained extensive experience in developing bioinformatics solutions that facilitate large-scale biological data analysis and improve research efficiency.

In addition to my research activities, I am passionate about fostering interdisciplinary collaborations and mentoring students from diverse backgrounds. By guiding young researchers in the fields of bioinformatics, computational biology, and biomedical data science, I aim to cultivate the next generation of scientists who will continue pushing the boundaries of biomedical research.

Overall, my work is dedicated to advancing bioinformatics through the development of robust analytical platforms and computational tools tailored to the needs of proteomics and metabolomics research. By leveraging cutting-edge data science techniques and integrating biological insights, I strive to contribute to the progress of biomedical research, particularly in biomarker discovery, disease modeling, and precision medicine. As the field of biological data science continues to evolve, I remain committed to addressing the challenges posed by complex datasets and translating computational advancements into meaningful biological and clinical applications.
 

Our research focuses on the development of algorithms and software tools for biomedical data analysis. With the rapid advancement of biotechnology, the explosive growth of biological data has significantly increased the demand for efficient analysis and interpretation. In response, our goal is to develop effective algorithms and software tools to help researchers extract valuable insights from complex biological data accurately and efficiently.

Development of Proteomics Software Tools  

Proteomics is essential for studying protein expression, modifications, and interactions, providing crucial insights into physiological and pathological conditions. However, proteomics data is highly complex and multidimensional, requiring specialized analytical tools. To address this challenge, we have developed AutoMod, a comprehensive proteomics analysis software. AutoMod integrates essential functions such as data quality control, quantitative analysis, protein modification identification, and protein interaction network modeling. This software not only enhances our understanding of protein roles in cellular functions but also aids in the discovery of disease biomarkers and drug targets.

Development of Metabolomics Software Tools

Metabolomics focuses on studying metabolic changes and their biological implications. Given the high-dimensional and heterogeneous nature of metabolomics data, efficient data management and analysis tools are crucial. We have developed a metabolomics analysis platform that integrates fundamental data processing functionalities with advanced machine learning techniques for feature selection and pattern recognition. This platform enables researchers to precisely identify metabolite features associated with specific biological conditions or diseases. Additionally, it supports comparative analysis across multiple samples, providing robust technical support for metabolomics research. Through this platform, researchers can gain deeper insights into metabolic changes and uncover potential disease biomarkers.

Machine Learning-Based Platform for Biomedical Data Analysis

The heterogeneity and non-linearity of biological data pose significant challenges for traditional analytical methods. To overcome these challenges, we have developed a machine learning-based analysis platform tailored for biomedical data. This platform integrates a variety of machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and Deep Learning Neural Networks, to accommodate different data types and analytical needs. Designed with biological data characteristics in mind, the platform is highly optimized for accuracy and computational efficiency. We anticipate its wide application in pattern recognition, predictive modeling, and feature extraction, providing diverse analytical tools for biomedical research.

Biomedical Data Analysis for Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disease with a complex pathological mechanism involving multiple biomolecules. Our research team conducts in-depth biomedical data analysis to identify potential pathogenic factors and therapeutic targets for RA. By applying our machine learning-based analysis platform, we extract key features from proteomics and metabolomics data that correlate with disease progression. Additionally, we construct predictive models to assess disease progression and compare these data with clinical manifestations to identify molecular-level correlations with disease phenotypes. These findings contribute to improved diagnosis and treatment strategies for RA.

Development of Multiomics Software Tools

Biomedical imaging data is a vital resource for biomedical research, providing spatial information at tissue and cellular levels. However, relying solely on imaging data may not fully reveal the molecular mechanisms underlying diseases. Therefore, our research integrates proteomics data with biomedical imaging data for multimodal analysis. This approach enables precise localization of specific protein distributions and their changes in diseased tissues, offering deeper insights into disease mechanisms. For instance, in our RA research, the integration of proteomics and imaging data has revealed distinct protein expression patterns in joint tissues, providing essential evidence for understanding the disease pathology.

In summary, our research emphasizes the development of systematic software platforms and advanced data analysis algorithms for cutting-edge biomedical fields such as proteomics and metabolomics. Through these platforms, we aim to help researchers efficiently and accurately extract meaningful insights from vast and complex biological data to address critical biological and medical questions. Whether in proteomics or metabolomics analysis or biomedical data research for RA, our work is dedicated to advancing biomedical research. Our innovative multimodal analysis strategy, which integrates different data sources, offers a more comprehensive biological perspective, ultimately driving progress in precision medicine.