IEEE Transactions on NanoBioscience Journal Impact Factor & Information

Publisher: IEEE Engineering in Medicine and Biology Society, Institute of Electrical and Electronics Engineers

Journal description

This transaction reports on original, innovative and interdisciplinary work on all aspects of molecular systems, cellular systems, and tissues - including molecular electronics.

Current impact factor: 1.77

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 1.768
2012 Impact Factor 1.286
2011 Impact Factor 1.28
2010 Impact Factor 1.712
2009 Impact Factor 1.705
2008 Impact Factor 1.341
2007 Impact Factor 1.899
2006 Impact Factor 2.592
2005 Impact Factor 1.392

Impact factor over time

Impact factor

Additional details

5-year impact 1.75
Cited half-life 5.90
Immediacy index 0.21
Eigenfactor 0.00
Article influence 0.52
Website IEEE Transactions on NanoBioscience website
Other titles IEEE transactions on nanobioscience
ISSN 1536-1241
OCLC 47360509
Material type Periodical, Internet resource
Document type Journal / Magazine / Newspaper, Internet Resource

Publisher details

Institute of Electrical and Electronics Engineers

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on Author's personal website, employers website or publicly accessible server
    • Author's post-print on Author's server or Institutional server
    • Author's pre-print must be removed upon publication of final version and replaced with either full citation to IEEE work with a Digital Object Identifier or link to article abstract in IEEE Xplore or replaced with Authors post-print
    • Author's pre-print must be accompanied with set-phrase, once submitted to IEEE for publication ("This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible")
    • Author's pre-print must be accompanied with set-phrase, when accepted by IEEE for publication ("(c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")
    • IEEE must be informed as to the electronic address of the pre-print
    • If funding rules apply authors may post Author's post-print version in funder's designated repository
    • Author's Post-print - Publisher copyright and source must be acknowledged with citation (see above set statement)
    • Author's Post-print - Must link to publisher version with DOI
    • Publisher's version/PDF cannot be used
    • Publisher copyright and source must be acknowledged
  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: The papers in this special section were presented at the IEEE BIBM 2014 conference that was held in Belfast, U.K., from November 18???22, 2014. The scientific program highlights five themes to provide breadth, depth, and synergy for research collaboration: 1) genomics and molecular structure, function, and evolution; 2) computational systems biology; 3) medical informatics and translational bioinformatics; 4) cross-cutting computational methods and bioinformatics infrastructures; and 5) healthcare informatics.
    IEEE Transactions on NanoBioscience 07/2015; 14(5):498-499. DOI:10.1109/TNB.2015.2446091
  • [Show abstract] [Hide abstract]
    ABSTRACT: Molecular communication in nanonetworks is an emerging communication paradigm that uses molecules as information carriers. In Molecule Shift Keying (MoSK), where different types of molecules are used for encoding, transmitter and receiver complexities increase as the modulation order increases. We propose a modulation technique called Depleted MoSK (DMoSK) in which, molecules are released if the information bit is 1 and no molecule is released for 0. The proposed scheme enjoys reduced number of the types of molecules for encoding. Numerical results show that the achievable rate is considerably higher.
    IEEE Transactions on NanoBioscience 06/2015; DOI:10.1109/TNB.2015.2436409
  • [Show abstract] [Hide abstract]
    ABSTRACT: The articles in this special section focus on the technologies and applications supported by micro- and nanomachines. The world of artificial micro- and nanomachines has greatly expanded over the last few years to include a range of disciplines from chemistry, physics, biology, to micro/nanoengineering, robotics, and theoretical physics. The dream of engineering nanomachines involves fabricating devices that mimic the mechanical action of biological motors that operate over multiple length scales: from molecular-scale enzymes and motors such as kinesins to the micro-scale biomachinery responsible for the motility of tiny organisms such as the flagella motors of E. coli. However, the design and fabrication of artificial nano- and micromachines with comparable performance as their biological counterparts is not a straightforward task. It requires a detailed understanding of the basic principles of the operation of biomotors and mechanisms that couple the dissipation of energy to mechanical motion. Moreover, micro engineering and microfabrication knowledge is required in order to design efficient, small and even smart micro- and nanomachines.
    IEEE Transactions on NanoBioscience 04/2015; 14(3):258-259. DOI:10.1109/TNB.2015.2428871
  • [Show abstract] [Hide abstract]
    ABSTRACT: Intel Xeon Phi is a new addition to the family of powerful parallel accelerators. The range of its potential applications in computationally driven research is broad; however, at present, the repository of scientific codes is still relatively limited. In this study, we describe the development and benchmarking of a parallel version of eFindSite, a structural bioinformatics algorithm for the prediction of ligand-binding sites in proteins. Implemented for the Intel Xeon Phi platform, the parallelization of the structure alignment portion of eFindSite using pragma-based OpenMP brings about the desired performance improvements, which scale well with the number of computing cores. Compared to a serial version, the parallel code runs 11.8 and 10.1 times faster on the CPU and the coprocessor, respectively; when both resources are utilized simultaneously, the speedup is 17.6. For example, ligand-binding predictions for 501 benchmarking proteins are completed in 2.1 hours on a single Stampede node equipped with the Intel Xeon Phi card compared to 3.1 hours without the accelerator and 36.8 hours required by a serial version. In addition to the satisfactory parallel performance, porting existing scientific codes to the Intel Xeon Phi architecture is relatively straightforward with a short development time due to the support of common parallel programming models by the coprocessor. The parallel version of eFindSite is freely available to the academic community at
    IEEE Transactions on NanoBioscience 03/2015; 14(4). DOI:10.1109/TNB.2015.2403776
  • [Show abstract] [Hide abstract]
    ABSTRACT: Influenza type A viruses are classified into subtypes based on their two surface proteins, hemagglutinin (HA) and neuraminidase (NA). The HA protein facilitates the viral binding and entering a host cell and the NA protein helps the release of viral progeny from the infected cell. The complementary roles of HA and NA entail their collaboration, which has important implications for viral replication and fitness. The HA protein from early strains of pandemic 2009 H1N1 of swine origin preferentially binds to human type receptors with a weak binding to avian type receptors. This virus caused several human deaths in De-cember 2013 in Texas USA, which motivated us to investigate the changes of genetic features that might contribute to the surged virulence of the virus. Our time series analysis on the strains of this virus col-lected from 2009 to 2013 implied that the HA binding preference of this virus in USA, Europe, and Asia has been the characteristic of swine H1N1 virus since 2009. However, its characteristic of seasonal hu-man H1N1 and its binding avidity for avian type receptors both were on steady rise and had a clear in-crease in 2013 with American strains having the sharpest surge. The first change could enhance the viral transmission and replication in humans and the second could increase its ability to cause infection deep in lungs, which might account for the recent human deaths in Texas. In light of HA and NA coadaptation and evolutionary interactions, we also explored the NA activity of this virus to reveal the functional bal-ance between HA and NA during the course of virus evolution. Finally we identified amino acid substitu-tions in HA and NA of the virus that were critical for the observed evolution.
    IEEE Transactions on NanoBioscience 03/2015; 14(2). DOI:10.1109/TNB.2015.2406992
  • [Show abstract] [Hide abstract]
    ABSTRACT: Nanowires are extensively used to fabricate highly sensitive electrical sensors for detection of biological and chemical species. The hole mobility can be promoted by the increasing Ge fraction in SiGe, achieved by the oxidation-induced Ge condensation. However, oxidation increases the number of surface states, which brings the nonnegligible contribution in mobility degradation. In this work, 3-aminopropyltrimethoxysilane (APTMS) was used as a biochemical reagent to modify the surface of SiGe nanowires, then bonding to bio-linker, bis (3-sulfo-N-hydroxysuccinimide ester) sodium salt (BS3). Various methods have been proposed for increasing sensitivity of boron-doped SiGe nanowires, such as capping layer, surface treatment and annealing temperature.
    IEEE Transactions on NanoBioscience 03/2015; 14(4). DOI:10.1109/TNB.2015.2407912
  • [Show abstract] [Hide abstract]
    ABSTRACT: Molecular motors of the cell are protein-based, nanoscale machines, which use a variety of strategies to transduce chemical energy into mechanical work in the presence of a large thermal background. The design and construction of artificial molecular motors is one approach to better understand their basic physical principles. Here, we propose the concept of a protein-based, burnt-bridges ratchet, inspired by biological examples. Our concept, the lawnmower, utilizes protease blades to cleave peptide substrates, and uses the asymmetric substrate-product interface arising from productive cleavage to bias subsequent diffusion on the track (lawn). Following experimental screening to select a protease to act as the motor's blades, we chemically couple trypsin to quantum dots and demonstrate activity of the resulting lawnmower construct in solution. Accompanying Brownian dynamics simulations illustrate the importance for processivity of correct protease density on the quantum dot and spacing of substrates on the track. These results lay the groundwork for future tests of the protein-based lawnmower's motor performance characteristics.
    IEEE Transactions on NanoBioscience 03/2015; 14(3). DOI:10.1109/TNB.2015.2393872
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we fabricate a flexible and location traceable micro-motor, named organo-motor, assisted by microfluidic devices and with high throughput. The organo-motors are composed of organic hydrogel material, poly (ethylene glycol) diacrylate (PEGDA), which can provide the flexibility of their structure. For spatial and temporal traceability of the organo-motors under magnetic resonance imaging (MRI), superparamagnetic iron oxide nanoparticles (SPION; Fe3O4) were incorporated into the PEGDA microhydrogels. Furthermore, a thin layer of platinum (Pt) was deposited onto one side of the SPION-PEGDA microhydrogels providing geometrical asymmetry and catalytic propulsion in aqueous fluids containing hydrogen peroxide solution, H2O2. Furthermore, the motion of the organo-motor was controlled by small external magnet enabled by the presence of SPION in the motor architecture.
    IEEE Transactions on NanoBioscience 03/2015; 14(3). DOI:10.1109/TNB.2015.2402651
  • [Show abstract] [Hide abstract]
    ABSTRACT: The eleven papers in this special issue are extended versions of accepted papers selected from the 10th International Symposium on Bioinformatics Research and Applications (ISBRA 2014).
    IEEE Transactions on NanoBioscience 03/2015; 14(2):154-156. DOI:10.1109/TNB.2015.2406991
  • [Show abstract] [Hide abstract]
    ABSTRACT: Mild cognitive impairment (MCI) has been considered as a transition phase to Alzheimer's disease (AD), and the diagnosis of MCI may help patients to carry out appropriate treatments to delay or even prevent AD. Recent advanced network analysis techniques utilizing resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been widely used to get more comprehensive understanding of neurological disorders at a whole-brain connectivity level. However, how to explore effective brain functional connectivity from fMRI data is still a challenge especially when the ultimate goal is to train classifiers for discriminating patients effectively. In our research, we studied the functional connectivity of the whole brain by calculating Pearson's correlation coefficients based on rs-fMRI data, and proposed a set of novel features by applying Two Sample T-Test on the correlation coefficients matrix to identify the most discriminative correlation coefficients. We trained a L2-regularized Logistic Regression classifier based on the five novel features for the first time and evaluated the classification performance via leave-one-out cross validation. We also iterated 10-fold cross validation ten times in order to evaluate the statistical significance of our method. The experiment result demonstrates that classification accuracy and the area under receiver operating characteristic (ROC) curve in our method are 87.5% and 0.929 respectively, and the statistical results prove that our method is statistically significant better than other three algorithms, which means our method could be meaningful to assist physicians efficiently in “real-world” diagnostic situations.
    IEEE Transactions on NanoBioscience 02/2015; 14(2). DOI:10.1109/TNB.2015.2403274
  • [Show abstract] [Hide abstract]
    ABSTRACT: The interaction of two RNA molecules involves a complex interplay between folding and binding that warranted recent developments in RNA-RNA interaction algorithms. However, biological mechanisms in which more than two RNAs take part in an interaction also exist. It is reasonable to believe that interactions involving multiple RNAs are generally more complex to be treated pairwise. In addition, given a pool of RNAs, it is not trivial to predict which RNAs interact without sufficient biological knowledge. Therefore, structures resulting from multiple RNA interactions often cannot be predicted by the existing algorithms that handle RNAs pairwise and may simply favor the best interacting pair. We propose a system for multiple RNA interaction that overcomes the difficulties mentioned above by formulating a combinatorial optimization problem called Pegs and Rubber Bands. A solution to this problem encodes a structure of interacting RNAs. The problem, not surprisingly, is NP-hard. However, our experiments with approximation algorithms and heuristics for the problem suggest that this formulation is adequate to predict known interaction patterns of multiple RNAs. In general, however, the optimal solution obtained does not necessarily correspond to the actual structure observed in biological experiments. Moreover, a structure produced by interacting RNAs may not be unique. We extend our approach to generate multiple sub-optimal solutions. By clustering these solutions, we are able to reveal representatives that correspond to realistic structures. Specifically, our results on the U2-U6 complex with introns in the spliceosome of human/yeast and the CopA-CopT complex in E. Coli are consistent with published biological structures.
    IEEE Transactions on NanoBioscience 02/2015; 14(2). DOI:10.1109/TNB.2015.2402591
  • [Show abstract] [Hide abstract]
    ABSTRACT: Transcriptomes are routinely compared in term of a list of differentially expressed genes followed by functional enrichment analysis. Due to the technology limitations of microarray, the molecular mechanisms of differential expression is poorly understood. Using RNA-seq data, we propose a generalized dSpliceType framework to systematically investigate the synergistic and antagonistic effects of differential splicing and differential expression. We applied the method to two public RNA-seq data sets and compared the transcriptomes between treatment and control conditions. The generalized dSpliceType detects and prioritizes a list of genes that are differentially expressed and/or spliced. In particular, the multivariate dSpliceType is among the fist to utilize sequential dependency of normalized base-wise read coverage signals and capture biological variability among replicates using a multivariate statistical model. We compared dSpliceType with two other methods in terms of five most common types of differential splicing events between two conditions using RNA-Seq. dSpliceType is free available from
    IEEE Transactions on NanoBioscience 02/2015; 14(2). DOI:10.1109/TNB.2015.2388593
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, the mechanism of cilia-induced ‡ow is discussed through a mathematical model. In this study two dimensional ‡ow of a viscous ‡uid in the presence of nanoparticles are observed in a curved channel with ciliated walls. Cilia have a distinctive pattern of motion by which they can set ‡uid into motion at low Reynolds number. The ‡ow is modeled in both …xed and wave frame of reference. Exact solution is calculated for the velocity as well as for temperature pro…le and the ‡ow properties for the Cu-blood nano‡uid is determined as a function of the cilia and metachronal wave velocity. Results for temperature pro…le, velocity, pressure rise, pressure gradient and stream function are constructed and evaluated graphically.
    IEEE Transactions on NanoBioscience 02/2015; 14(4). DOI:10.1109/TNB.2015.2401972