Special Issue "Systems Biology Approaches for Understanding Human Health and Disease"
Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 717
Interests: systems biology; multi-omics integration; host-microbe interaction; microbiota; metabolism; cancer; precision nutrition; metabolic modeling; genome evolution
Interests: plant natural products; yeast; metabolic engineering; synthetic biology; environmental microbiota
Special Issues, Collections and Topics in MDPI journals
Systems biology approaches aim to understand human health and diseases by analyzing and integrating information from different levels with a systems and dynamic fashion. The advent of omics technology and computational tools has enabled us to explore biological systems from multiple sources (e.g., genomics, transcriptomics, metabolomics, proteomics) in a cost-efficient manner. Multi-omics studies through integrating data have provided efficient ways to unveil the potential mechanisms linked to human health and disease, including metabolic disease (e.g., diabetes, cardiovascular disease), aging, and host–microbiota interactions. With the continuously increasing accumulation of big data in omics, data-driven approaches which an applied machine learning methodology for integrating omics data have provided mechanistic insights into human diseases. Moreover, knowledge-based approaches have capitalized on the extensive prior knowledge about biological systems.
Recent studies have shown that the microbiota perform important roles in human health and disease. Systems biology approaches have been extensively applied to understand host–microbiome interactions in numerous disease (e.g., diabetes, cardiovascular disease, neurodegenerative diseases). Therefore, cutting-edge studies in therapeutics development and preventive strategies to maintain healthy gut microbiota are of great interest.
In this Special Issue, we welcome studies (both reviews and articles) on but not limited to the following potential topics:
- Machine learning workflows and their applications in omics integration;
- Machine-learning-based diagnostic/prognostic models for human disease;
- Machine-learning-based prediction of treatment outcomes;
- Multi-omics studies of human disease or microbiome;
- Network-based analysis of omics data;
- Mathematical modeling of human disease;
- Mathematical modeling of human microbiome;
- Integrative analysis involving in drug development;
- Host–microbiota interaction.
Dr. Yangbo Ji
Dr. Yongjun Wei
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- biological networks
- cancer therapy
- cardiovascular disease
- metabolic diseases
- diagnostic/prognostic models
- healthy aging
- machine learning
- mathematical modeling
- multi-omics integration
- probiotics and nutrition
- systems biology
- treatment outcome