Topical Collection "Human Single Nucleotide Polymorphisms and Disease Diagnostics"
Human DNA is not identical among individuals and this causes natural differences among races and ethnic populations, and also among healthy individuals and individuals susceptible to diseases. While natural differences between humans are harmless and are essential to maintain the diversity of human population, the DNA defects leading to high risks of disease are unwanted and need to be discovered, in the best case scenario, even before the patient gets sick. This approach, known as personalized diagnostics is currently being extensively developed by many researchers in both academia and corporate industry. A successful development of tools, both computational and experimental, for predicting disease-causing DNA differences would have several important outcomes: (a) it would allow patients at high risk to develop this disease to be diagnosed ahead of the time and to be prescribed preventive treatment; (b) it would allow already sick patients to receive personalized diagnostics and a treatment more suitable for their genetic disorder; and (c) it would improve parental screening for those who are planning to have children. In the long run, such a development will be at the core of personalized medicine allowing therapy to be individually tailored to the patient’s own genome. This Topical Collection will contain articles describing new advances in the science of understanding human polymorphism and disease diagnostics.
Professor Dr. Emil Alexov
The meeting "Human Single Nucleotide Polymorphisms & Disease", a partner of International Journal of Molecular Science, will be hold on 3-8 August 2014, Easton, MA, USA. Selected papers from this meeting will be published in this collection. Detailed information of the meeting can be found at http://www.grc.org/programs.aspx?year=2014&program=human.
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 collection 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. International Journal of Molecular Sciences 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.
- single nucleotide polymorphism (SNP)
- missense mutations
- personalized diagnostics
- personalized medicine
- human DNA variations
- disease-causing mutations
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: A Comparative Analysis of Chaotic Particle Swarm Optimizations for Detecting SNP Barcodes using High-Dimensional Datasets
Authors: Cheng-Hong Yang , Sin-Hua Moi , Yu-Da Lin and Li-Yeh Chuang
1 Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, 415 Chien-Kung Road, Kaohsiung 80778, Taiwan; E-Mail: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org
2 Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, No.1, Sec. 1, Syuecheng Rd., Dashu District, Kaohsiung 84001,Taiwan; E-Mails: email@example.com; firstname.lastname@example.org
Abstract: An epistasis model testing the interaction between single nucleotide polymorphisms (SNPs) may suppress the effect of disease susceptibility. Efficacious evolutionary computing methods could overcome the computational limitations of large statistical evaluation for high-order epistasis models. In previous studies, several chaotic particle swarm optimization (CPSO) methods have been proposed to detect the SNP barcodes for disease analysis, for example, for breast cancer and chronic diseases. The objective of this investigation is to evaluate more chaotic maps combined with CPSO for detecting SNP barcodes using high-dimensional genomic data. We used nine chaotic maps to improve PSO and compared the search ability amongst all versions of CPSO. XOR and ZZ disease models were used to compare all chaotic maps combined with PSOs. The efficacy evaluation of both computational methods was based on the statistical values from the chi-square test (χ2). The results showed that the searching ability of PSOs that have been entrapped into a local optimum could be improved by a chaotic map. According to the minor allele frequency (MAF), the numbers of SNP, numbers of sample, and the highest χ2 value in all datasets was found in the Sinai chaotic map combined with PSO amongst all versions of CPSO. Moreover, a gbest value of PSO was found which continued to effectively enhance the fitness values (χ2). Our study indicates that the Sinai chaotic map combined with PSO is more effective at detecting the potential SNP barcodes in both XOR and ZZ disease models. Future research should investigate the detection of SNP barcodes using more epistasis models and CPSO.
Keywords: Single Nucleotide Polymorphisms (SNP); Particle swarm optimization (PSO); susceptibility genes