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Informatics
Special Issues
Biomedical Informatics
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Biomedical Informatics

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: closed (31 March 2018) | Viewed by 7673

Special Issue Editor

Prof. Dr. Feng Lin

E-Mail Website
Guest Editor
School of Computer Engineering, Nanyang Technological University, N4-2A-05, Nanyang Avenue, Singapore 639798, Singapore
Interests: biomedical informatics; biomedical imaging and visualization; computer graphics; high-performance computing

Special Issue Information

Dear Colleagues,

We look forward to original research contributions or review papers in the domain of biomedical informatics. First, works on computational biology and bioinformatics for genomics and proteomics in life sciences are welcome, especially those in the niche areas of single cell data analysis and the next-generation sequencing technology. Secondly, research in in vivo and in vitro cellular imaging, feature detection and classification for clinical decision making are encouraged to be submitted. Thirdly, multi-modal tissue and organ-level imaging, mutual information and image registration methods are looked forward. Finally, novel techniques in all bio-signal processing, such as ECG, EMG and EEG, addressing deep learning algorithms for clinical data analytics are considered in this Special Issue.

Prof. Dr. Feng Lin
Guest Editor

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. Informatics is an international peer-reviewed open access quarterly 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.

Keywords

  • bioinformatics
  • single cell data analysis
  • cellular imaging
  • multi-modal imaging
  • mutual information
  • bio-signal processing
  • feature detection
  • pattern recognition
  • machine learning

Published Papers (1 paper)

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Research

19 pages, 1124 KiB  
Article
A Novel Three-Stage Filter-Wrapper Framework for miRNA Subset Selection in Cancer Classification
by Mohammad Bagher Dowlatshahi, Vali Derhami and Hossein Nezamabadi-pour
Informatics 2018, 5(1), 13; https://doi.org/10.3390/informatics5010013 - 01 Mar 2018
Cited by 27 | Viewed by 7234
Abstract
Micro-Ribonucleic Acids (miRNAs) are small non-coding Ribonucleic Acid (RNA) molecules that play an important role in the cancer growth. There are a lot of miRNAs in the human body and not all of them are responsible for cancer growth. Therefore, there is a [...] Read more.
Micro-Ribonucleic Acids (miRNAs) are small non-coding Ribonucleic Acid (RNA) molecules that play an important role in the cancer growth. There are a lot of miRNAs in the human body and not all of them are responsible for cancer growth. Therefore, there is a need to propose the novel miRNA subset selection algorithms to remove irrelevant and redundant miRNAs and find miRNAs responsible for cancer development. This paper tries to propose a novel three-stage miRNAs subset selection framework for increasing the cancer classification accuracy. In the first stage, multiple filter algorithms are used for ranking the miRNAs according to their relevance with the class label, and then generating a miRNA pool obtained based on the top-ranked miRNAs of each filter algorithm. In the second stage, we first rank the miRNAs of the miRNA pool by multiple filter algorithms and then this ranking is used to weight the probability of selecting each miRNA. In the third stage, Competitive Swarm Optimization (CSO) tries to find an optimal subset from the weighed miRNAs of the miRNA pool, which give us the most information about the cancer patients. It should be noted that the balance between exploration and exploitation in the proposed algorithm is accomplished by a zero-order Fuzzy Inference System (FIS). Experiments on several miRNA cancer datasets indicate that the proposed three-stage framework has a great performance in terms of both the low error rate of the cancer classification and minimizing the number of miRNAs. Full article
(This article belongs to the Special Issue Biomedical Informatics)
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