About this Research Topic
This Research Topic will host a selection of papers from The 11th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2022) taking place in Shenzen, China from November 15th - 18th 2022.
Recent advances in integrative analysis offer researchers the opportunity to study disease at the systems level. Through multidimensional methods and analyses, we can gain a more systematic and comprehensive understanding of diseases, especially complex diseases.
In the era of precision medicine, we aspire to have useful and robust biomarkers to predict treatment effects and disease prognosis, leading to better and more timely management of patients. However, selecting biomarker candidates from the large number of molecules involved in various layers of biological systems is challenging. Most diseases have many etiologies, genetics, disease comorbidities, and environments.
Today, we are expanding our understanding of molecular classifications of diseases, e.g., non-small cell lung cancer, acute lymphocytic leukemia, and more. Individual biomarkers may not be effective in overcoming the high heterogeneity of complex diseases. Thus, systems biology emerged and many tools and pipelines were available, e.g., network-based analysis, systems-based approaches, and machine learning models. It is a good time to discuss study design and biomarker discovery pipelines for complex diseases. We are eager to gain a deeper understanding of disease and discover new biomarkers.
This Research Topic (RT) proposes an integrative analytical approach to reveal complex disease expression patterns and identify potential clinical impacts for biomarkers of interest from the public databases. Interestingly, machine learning algorithms use historical data as input to predict new output values. It may provide new insights into the pathogenesis of complicated diseases. This RT will establish a platform to curate influential research and reviews to inspire the application of integrative analysis to biomarker discovery in complex diseases, from in silico analyses, and preclinical studies to clinical trials.
This Research Topic focuses on publishing Original Research and Review articles focusing on, but not limited to the following:
1. Underlying mechanisms explored with machine learning tools as well as validation from preclinical experiments and gene manipulation.
2. Development of tools and algorithms to identify biomarkers of complex diseases/Database
3. Development of AI algorithms that can be explained, easily understood and bio-verified
4. Integrative analyses from various Omics platforms for the disease biomarkers discovery (e.g., genetics, epigenetics, proteomics, metabolomics, immunomics, etc) with solid experimental validation.
Please be aware that those works describing essentially bioinformatic analyses leading to the identification of “potential” biomarkers, prognostic factors or pathogenic mechanisms without experimental proof would be out of scope.
Recent advances in integrative analysis offer researchers the opportunity to study disease at the systems level. Through multidimensional methods and analyses, we can gain a more systematic and comprehensive understanding of diseases, especially complex diseases.
In the era of precision medicine, we aspire to have useful and robust biomarkers to predict treatment effects and disease prognosis, leading to better and more timely management of patients. However, selecting biomarker candidates from the large number of molecules involved in various layers of biological systems is challenging. Most diseases have many etiologies, genetics, disease comorbidities, and environments.
Today, we are expanding our understanding of molecular classifications of diseases, e.g., non-small cell lung cancer, acute lymphocytic leukemia, and more. Individual biomarkers may not be effective in overcoming the high heterogeneity of complex diseases. Thus, systems biology emerged and many tools and pipelines were available, e.g., network-based analysis, systems-based approaches, and machine learning models. It is a good time to discuss study design and biomarker discovery pipelines for complex diseases. We are eager to gain a deeper understanding of disease and discover new biomarkers.
This Research Topic (RT) proposes an integrative analytical approach to reveal complex disease expression patterns and identify potential clinical impacts for biomarkers of interest from the public databases. Interestingly, machine learning algorithms use historical data as input to predict new output values. It may provide new insights into the pathogenesis of complicated diseases. This RT will establish a platform to curate influential research and reviews to inspire the application of integrative analysis to biomarker discovery in complex diseases, from in silico analyses, and preclinical studies to clinical trials.
This Research Topic focuses on publishing Original Research and Review articles focusing on, but not limited to the following:
1. Underlying mechanisms explored with machine learning tools as well as validation from preclinical experiments and gene manipulation.
2. Development of tools and algorithms to identify biomarkers of complex diseases/Database
3. Development of AI algorithms that can be explained, easily understood and bio-verified
4. Integrative analyses from various Omics platforms for the disease biomarkers discovery (e.g., genetics, epigenetics, proteomics, metabolomics, immunomics, etc) with solid experimental validation.
Please be aware that those works describing essentially bioinformatic analyses leading to the identification of “potential” biomarkers, prognostic factors or pathogenic mechanisms without experimental proof would be out of scope.
Keywords: Complex disease, Biomarker discovery, Data mining, Integrative analysis, Machine learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
