Single-Cell and Bulk Biomarker Discovery Approaches for Complex Diseases

Dear Colleagues,

Disease diagnostics is crucially important for the treatment of diseases. Accurate and early disease diagnosis could save people’s lives and avoid wasting time, money, and resources on misdiagnosed patients. Although many diseases can be diagnosed with very high accuracy, complex diseases such as cancer and pulmonary fibrosis remain challenging to diagnose. One significant bottleneck for diagnosing these complex diseases is often the lack of effective biomarkers. In the past, various biomarker discovery methods (i.e., feature selection) were developed to identify biomarkers from bulk measurements such as RNA-seq of the patient samples (blood or specimens). This indeed resulted in many novel biomarkers, which were later developed into diagnostic panels. Still, effective biomarkers for complex diseases such as lung cancer or pulmonary fibrosis (particularly biomarkers that signify early stage illness) are greatly lacking. The ever-developing single-cell technologies provide unprecedented opportunities to deconvolve the cellular heterogeneity in complex diseases, and thus enable the discovery of effective biomarkers at the single-cell level, which could mark the dynamic cell populations associated with phenotypic changes (e.g., from health to disease). However, single-cell data are much more noisy, complex, highly dimensional, and large-scale, presenting quite significant challenges for data analytics and the subsequent identification of biomarkers. In recent years, numerous computational and experimental methods have been developed to identify single-cell resolution biomarkers for complex diseases.

In this Special Issue, we invite the submission of computational and experimental approaches for the discovery of novel biomarkers that can predict disease outcomes. This Special Issue welcomes the submission of manuscripts addressing topics including but not limited to:

• Computational or experimental methods for discovering disease biomarkers from single-cell transcriptomic data;
• Computational or experimental methods for discovering disease biomarkers’ single-cell multi-omics data; 
• Novel computational models or machine-learning approaches for biomarker selection (feature selection); 
• Novel experimental models that could enable the discovery of effective disease biomarkers;
• Other experimental or computational methods that are relevant to disease biomarker discovery (or disease diagnostics in general).

Dr. Jun Ding
Dr. Weihua Pan
Guest Editors

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. Genes 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 2600 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.

• single-cell multi-omics data
• biomarker selection
• disease diagnostics
• machine learning
• complex diseases