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: 2.31

Impact Factor Rankings

2015 Impact Factor Available summer 2016
2014 Impact Factor 2.309
2013 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 2.08
Cited half-life 6.00
Immediacy index 0.41
Eigenfactor 0.00
Article influence 0.68
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
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    • Publisher's version/PDF cannot be used
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  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: In this study, we used structural and evolutionary based features to represent the sequences of gram-positive and gram-negative subcellular localizations. To do this, we proposed a normalization method to construct a normalize Position Specific Scoring Matrix (PSSM) using the information from original PSSM. To investigate the effectiveness of the proposed method we compute feature vectors from normalize PSSM and by applying Support Vector Machine (SVM) and Naïve Bayes classifier, respectively, we compared achieved results with the previously reported results. We also computed features from original PSSM and normalized PSSM and compared their results. The archived results show enhancement in gram-positive and gram-negative subcellular localizations. Evaluating localization for each feature, our results indicate that employing SVM and concatenating features (amino acid composition feature, Dubchak feature (physicochemical-based features), normalized PSSM based auto-covariance feature and normalized PSSM based bigram feature) have higher accuracy while employing Naïve Bayes classifier with normalized PSSM based auto-covariance feature proves to have high sensitivity for both benchmarks. Our reported results in terms of overall locative accuracy is 84.8% and overall absolute accuracy is 85.16% for gram-positive dataset; and, for gram-negative dataset, overall locative accuracy is 85.4% and overall absolute accuracy is 86.3%.
    IEEE Transactions on NanoBioscience 11/2015; DOI:10.1109/TNB.2015.2500186
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    ABSTRACT: This study reports the development of a portable whole cell biosensor system for environmental monitoring applications, such as air quality control, water pollution monitoring and radiation leakage detection. The system consists of a lightweight mechanical housing, a temperature regulating system, and a microfluidic bacterial inoculation channel. The overall system, which is less than 200 g, serves as a portable incubator for cell inoculation and can be mounted on an unmanned aerial vehicle for monitoring remote and unreachable locations. The feedback control system maintains the inoculation temperature within 0.05 degree Celsius. The large surface-to-volume ratio of the polydimethylsiloxane microchannel facilitates effective gas exchange for rapid bacterial growth. Molecular dynamic simulation shows effective diffusion of major gas pollutants in PDMS toward gas sensing applications. By optimizing the design, we demonstrate the operation of the system in ambient temperatures from 5 C to 32 C and rapid bacterial growth in microchannels compared to standard bacterial culture techniques.
    IEEE Transactions on NanoBioscience 11/2015; DOI:10.1109/TNB.2015.2478481
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    ABSTRACT: Nano-scale molecular communication is a viable way of exchanging information between nano-machines. In this letter, a low-complexity and non-coherent signal detection technique is proposed to mitigate the inter-symbol-interference (ISI) and additive noise. In contrast to existing coherent detection methods of high complexity, the proposed non-coherent signal detector is more practical when the channel conditions are hard to acquire accurately or hidden from the receiver. The proposed scheme employs the concentration difference to detect the ISI corrupted signals and we demonstrate that it can suppress the ISI effectively. The concentration difference is a stable characteristic, irrespective of the diffusion channel conditions. In terms of complexity, by excluding matrix operations or likelihood calculations, the new detection scheme is particularly suitable for nano-scale molecular communication systems with a small energy budget or limited computation resource.
    IEEE Transactions on NanoBioscience 11/2015;
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    ABSTRACT: Since the existence of graphene, a material only a single atomic layer thick, was demonstrated about a decade ago, it has caught the attention of researchers worldwide. This paper begins with a historical overview of graphene since its discovery, in 2004, and focuses on a citation-weighted review of graphene-based sensors developed for the detection of biological targets. Based on this statistical analysis, we categorize recent developments in graphene-based biosensors (GBBs) as optimized for detecting (1) proteins, (2) nucleic acids (3) carbohydrates, or (4) compounds generated by metabolic processes. Existing detection methods employed by these sensors include electrical, electrochemical, and photonic approaches with respect to detecting labeled (or enzyme-assisted) and label-free (or enzyme-free) probe structures. Herein, we focus on graphene-based glucose sensors because glucose-monitoring technology is extremely important in the management of diabetes and many practical examples of these carbohydrate sensors have been developed using the aforementioned detection methods.
    IEEE Transactions on NanoBioscience 11/2015; DOI:10.1109/TNB.2015.2475338
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    ABSTRACT: This study focuses on the successful recrystallization of bacterial S-layer arrays of the Lactobacillus acidophilus ATCC 4356 at textile surfaces to create a novel method and material. Optimum bacterial growth was obtained at approximately 45 °C, pH 5.0 and 14 h pi. The cells were resuspended in guanidine hydrochloride and the 43 kDa S-protein was dialyzed and purified. The optimum reassembly on the polypropylene fabric surface in terms of scanning electron microscopy (SEM), reflectance, and uniformity (spectrophotometry) was obtained at 30 °C, pH 5.0 for 30 minutes in the presence of 2 gr/l (liquor ratio; 1:40) of the S-protein. Overall, our data showed that the functional aspects and specialty applications of the fabric would be very attractive for the textile and related sciences, and result in advanced technical textiles.
    IEEE Transactions on NanoBioscience 11/2015; DOI:10.1109/TNB.2015.2495143
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    ABSTRACT: Protein-Protein Interaction(PPI) plays crucial roles in the performance of various biological processes. A variety of methods are dedicated to identify whether proteins have interaction residues, but it is often more crucial to recognize each amino acid. In practical applications, the stability of a prediction model is as important as its accuracy. However, random sampling, which is widely used in previous prediction models, often brings large difference between each training model. In this paper, a Predictor of protein-protein interaction sites based on Extremelyrandomized Trees(PETs) is proposed to improve the prediction accuracy while maintaining the prediction stability. In PETs, a cluster-based sampling strategy is proposed to ensure the model stability: first, the training dataset is divided into subsets using specific features; second, the subsets are clustered using K-means; and finally the samples are selected from each cluster. Using the proposed sampling strategy, samples which have different types of significant features could be selected independently from different clusters. The evaluation shows that PETs is able to achieve better accuracy while maintaining a good stability. The source code and toolkit are available at
    IEEE Transactions on NanoBioscience 11/2015; DOI:10.1109/TNB.2015.2491303
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    ABSTRACT: Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) are non invasive neuro-imaging modalities largely used in neurology explorations. MRI is considered as a static modality and could be so important for anatomy by its high spatial resolution. EEG, on the other hand, is an important tool permitting to image temporal dynamic activities of the human brain. Fusion of these two essential modalities would be hence a so emerging research domain targeting to explore brain activities with the MRI static modality. Our present research investigates a sophisticated approach for localization of the cerebral activity that could be involved by the dynamic EEG modality and carefully illustrated within MRI static modality. Such careful cerebral activity localization would be first based on an advanced methodology yielding therefore a singular value decomposition-based lead field weighting to sLORETA method formalism, for solving in fact the inverse problem in the EEG. The conceived method for source localization, carried out on different cases of simulated dipoles experiments, showed satisfactory results. Different cases of simulated dipoles experiments and metrics were used to confirm the reliability of the proposed method. The experimental results confirm that our method presents a flexible and robust tool for EEG source imaging.
    IEEE Transactions on NanoBioscience 10/2015; DOI:10.1109/TNB.2015.2477247
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    ABSTRACT: The accuracy of two calculation algorithms of the Eclipse 8.9 treatment planning system (TPS) - the anisotropic analytic algorithm (AAA) and pencil-beam convolution (PBC) - in modeling the enhanced dynamic wedge (EDW) was investigated. Measurements were carried out for 6 and 18 MV photon beams using a 2D ionization chamber array. Accuracy of the TPS was evaluated using a gamma index analysis with the following acceptance criteria for dose differences (DD) and distance to agreement (DTA): 3%/3mm and 2%/2mm. The TPS models the dose distribution accurately except for 20×20 cm2 field size, 60° and 45° wedge angles using PBC at 6 MV photon energy. For these latter fields, the pass rate and the mean value of gamma were less than 90% and more than 0.5, respectively at the (3%/3mm) acceptance criteria. In addition, an accuracy level of (2%/2mm) was achieved using AAA with better agreement for 18 MV photon energy.
    IEEE Transactions on NanoBioscience 10/2015; DOI:10.1109/TNB.2015.2466102
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    ABSTRACT: A new poly(vinylchloride) (PVC) membrane electrode based on tannin from the bark of Quercus macrolepis (acorn) as the ionophore was prepared and modified onto the surface of a gold electrode. The electrochemical impedance spectroscopy (EIS) technique was used to study the sensitivity of the electrode that was modified with a thin layer of polymeric biomembrane, in order to detect heavy metals ions in solution. The device shows a good sensitivity for Zn2+, Ni2+ ions and a little less for Cd2+. The electrode indicates a good linear response for the three metals over a wide concentration range from 1.0 × 10-9 to 1.0 × 10-4 M, with a detection limit of 1.0 × 10-9 M.
    IEEE Transactions on NanoBioscience 10/2015; DOI:10.1109/TNB.2015.2461444
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    ABSTRACT: Bridging the gap between mathematical and biological models and clinical applications could be considered as one of the new challenges of medical image analysis over the ten last years. This paper presents an advanced and convivial algorithm for brain glioblastomas tumor growth modelisation. The brain glioblastomas tumor region would be extracted using a fast distribution matching developed algorithm based on global pixel wise information. A new model to simulate the tumor growth based on two major elements: Cellular Automata and Fast Marching Method (CFMM) has been developed and used to estimate the brain tumor evolution during the time. On the basis of this model, experiments were carried out on twenty pathological MRI selected cases that were carfully discussed with the clinical part. The obtained simulated results were validated with ground truth references (real tumor growth measure) using Dice Metric parameter. As carefully discussed with the clinical partner, experimental results showed that our proposed algorithm for brain glioblastomas tumor growth model proved a good agreement. Our main purpose behind this research was of course to make advances and progress during clinical explorations helping therefore radiologists in their diagnosis. Clinical decisions and guidelines would be hence so more focused with such an advanced tool that could help clinicians and ensuring more accuracy and objectivity.
    IEEE Transactions on NanoBioscience 10/2015; DOI:10.1109/TNB.2015.2450365
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    ABSTRACT: Protein-protein interactions exist ubiquitously and play important roles in the life cycles of living cells. The interaction sites (residues) are essential to understanding the underlying mechanisms of protein-protein interactions. Previous research has demonstrated that the accurate identification of protein-protein interaction sites (PPIs) is helpful for developing new therapeutic drugs because many drugs will interact directly with those residues. Because of its significant potential in biological research and drug development, the prediction of PPIs has become an important topic in computational biology. However, a severe data imbalance exists in the PPIs prediction problem, where the number of the majority class samples (non-interacting residues) is far larger than that of the minority class samples (interacting residues). Thus, we developed a novel cascade random forests algorithm (CRF) to address the serious data imbalance that exists in the PPIs prediction problem. The proposed CRF resolves the negative effect of data imbalance by connecting multiple random forests in a cascade-like manner, each of which is trained with a balanced training subset that includes all minority samples and a subset of majority samples using an effective ensemble protocol. Based on the proposed CRF, we implemented a new sequence-based PPIs predictor, called CRF-PPI, which takes the combined features of position-specific scoring matrices, averaged cumulative hydropathy, and predicted relative solvent accessibility as model inputs. Benchmark experiments on both the cross-validation and independent validation datasets demonstrated that the proposed CRF-PPI outperformed the state-of-the-art sequence-based PPIs predictors. The source code for CRF-PPI and the benchmark datasets are available online at for free academic use.
    IEEE Transactions on NanoBioscience 10/2015; DOI:10.1109/TNB.2015.2475359
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    ABSTRACT: Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In most applications of SN P systems, synchronization plays a key role which means the execution of a rule is completed in exactly one time unit (one step). However, such synchronization does not coincide with the biological fact: in biological nervous systems, the execution times of spiking rules cannot be known exactly. Therefore, a “realistic” system called time-free SN P systems were proposed, where the precise execution time of rules is removed. In this paper, we consider building arithmetical operation systems based on timefree SN P systems. Specifically, adder, subtracter, multiplier and divider are constructed by using time-free SN P systems. The obtained systems always produce the same computation result independently from the execution time of the rules.
    IEEE Transactions on NanoBioscience 09/2015; 14(6):1-1. DOI:10.1109/TNB.2015.2438257
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    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
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    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; 14(6). DOI:10.1109/TNB.2015.2436409