Special Issue "Bio- and Chemo-Sensor Networks and Arrays"

A special issue of Biosensors (ISSN 2079-6374).

Deadline for manuscript submissions: closed (31 March 2013)

Special Issue Editors

Guest Editor
Prof. Dr. Claes-Göran S. Granqvist

The Ångström Laboratory, Department of Engineering Sciences, Uppsala University, PO Box 534, SE-751 21 Uppsala, Sweden
Fax: +46 18 50 01 31
Interests: materials science for solar energy and energy savings; this includes thin films and nanomaterials for sensors, photocatalysis, electrochromics and thermochromics
Guest Editor
Dr. Maria D. King

Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA
Interests: high-airvolume bioaerosol and aerosolized nanoparticle sampling and characterization; laser-based particle imaging; phage-based bacterial identification and enumeration; bacterial apoptosis; fungal mycotoxin-DNA adduct formation and identification
Guest Editor
Prof. Dr. Laszlo B. Kish

Department of Electrical and Computer Engineering, College Station, Texas A&M University, TX 77843, USA
Website | E-Mail
Fax: +1 979 845 6259
Interests: physical informatics; sensors; unconditional security;nanomaterials/structures; aging/degradation; percolation; fluctuation-enhanced sensing; noise-based computation; thermal demons/engines

Special Issue Information

Dear Colleagues,

Biological and chemical sensing are rapidly growing, strongly interdisciplinary fields of science and technology. At the corporate side, the medical industry is the main factor however environment, food safety, air-quality, defense, etc are also important elements. The nature of research includes physics, biophysics, chemistry, biochemistry, physical chemistry, electrochemistry, electronics and computer science. A particularly fast growing field is sensor networks. There the information is collected from the single sensors in the network and using proper models for interpretation synergistically enhances the information content and the reliability. The network can be a specific array of sensors with enhanced processing of the collective information. The network can also be a large network of stand-alone sensors that communicate with not only the base but also with each other.

Prof. Dr. Claes-Göran S. Granqvist
Dr. Maria D. King
Prof. Dr. Laszlo B. Kish
Guest Editors


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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biosensors 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 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.


  • medical, environmental, food, air-quality, defense, etc sensor networks and arrays
  • agent collection for sensor arrays, concentrators and pre-selectors
  • how to enhance the information synergistically: data processing and interpretation
  • sensor communication: reliability, redundance, coding, wired, wireless and optical
  • sensor electronics, power requirement, robustness, miniaturization

Published Papers (1 paper)

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Open AccessArticle A Comparison of Methods for RNA-Seq Differential Expression Analysis and a New Empirical Bayes Approach
Biosensors 2013, 3(3), 238-258; doi:10.3390/bios3030238
Received: 12 May 2013 / Revised: 9 June 2013 / Accepted: 12 June 2013 / Published: 28 June 2013
Cited by 3 | PDF Full-text (3948 KB) | HTML Full-text | XML Full-text | Supplementary Files
Transcriptome-based biosensors are expected to have a large impact on the future of biotechnology. However, a central aspect of transcriptomics is differential expression analysis, where, currently, deep RNA sequencing (RNA-seq) has the potential to replace the microarray as the standard assay for RNA
[...] Read more.
Transcriptome-based biosensors are expected to have a large impact on the future of biotechnology. However, a central aspect of transcriptomics is differential expression analysis, where, currently, deep RNA sequencing (RNA-seq) has the potential to replace the microarray as the standard assay for RNA quantification. Our contributions here to RNA-seq differential expression analysis are two-fold. First, given the high cost of an RNA-seq run, biological replicates are rare, and therefore, information sharing across genes to obtain variance estimates is crucial. To handle such information sharing in a rigorous manner, we propose an hierarchical, empirical Bayes approach (R-EBSeq) that combines the Cufflinks model for generating relative transcript abundance measurements, known as FPKM (fragments per kilobase of transcript length per million mapped reads) with the EBArrays framework, which was previously developed for empirical Bayes analysis of microarray data. A desirable feature of R-EBSeq is easy-to-implement analysis of more than pairwise comparisons, as we illustrate with experimental data. Secondly, we develop the standard RNA-seq test data set, on the level of reads, where 79 transcripts are artificially differentially expressed and, therefore, explicitly known. This test data set allows us to compare the performance, in terms of the true discovery rate, of R-EBSeq to three other widely used RNAseq data analysis packages: Cuffdiff, DEseq and BaySeq. Our analysis indicates that DESeq identifies the first half of the differentially expressed transcripts well, but then is outperformed by Cuffdiff and R-EBSeq. Cuffdiff and R-EBSeq are the two top performers. Thus, R-EBSeq offers good performance, while allowing flexible and rigorous comparison of multiple biological conditions. Full article
(This article belongs to the Special Issue Bio- and Chemo-Sensor Networks and Arrays)
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