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Special Issue "Emerging Applications for Next Generation Sequencing"
Deadline for manuscript submissions: closed (30 April 2018).
There has been an extraordinary progress in Next Generation Sequencing (NGS) since the completion of the Human Genome Project, yet the original goal that it would be the ultimate basis to curing human diseases has not been reached. Huge amounts of data have been, and are being, created by NGS and related technologies, yet we are still far from delivering on the original promise. Sequencing technologies will continue to evolve, get better and cheaper. Whatever is regarded the standard today will vanish tomorrow, merely to be replaced by more efficient technologies. The holy grail, however, lies in unraveling the information that is contained in the data.
We are just beginning to realize that living systems are so complex that only methods of Artificial Intelligence (AI), particularly machine learning, can help us make significant progress in this endeavor.
With this Special Issue, we invite researchers to present their recent and novel approaches to exploring all-omics data by developing and applying AI methods to Life Sciences. The suggested topics include, but are not limited to, answering the challenges of simultaneously analyzing heterogeneous types of data sets, such as mutations, gene expressions, DNA-protein interactions, methylation, or metabolomics. Likewise, we welcome submissions of studies exploring experiment planning and design using methods from machine intelligence research. While the costs are dropping, and turn-over time of experiments decrease, and the portability of instruments make them increasingly applicable in clinical settings and in the field (notably the application of MinION in the 2014 Ebola outbreak), understanding of the multivariate nature of non-Mendelian diseases will remain the main challenge for precision medicine. We thus invite articles reporting on novel applications of AI methods that explore NGS in connection to other sources of data, with the aim of finding new drugs and promoting better treatments.
Similarly, an exploration of new areas such as, for instance, synthetic biology, in the context of NGS data and application of AI techniques of, e.g., knowledge-based planning, and innovative approaches that combine unstructured text and health data with NGS are also encouraged.
Prof. Dr. Jan Komorowski
Prof. Dr. Bozena Kaminska
Dr. Manfred Grabherr
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 papers will be 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 1800 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.
- Next Generation Sequencing
- Artificial Intelligence
- Machine learning
- Merging genomic data
- Precision medicine
- Experiment planning
- Synthetic biology
- Knowledge-based design
- Unstructured text
- Medical records