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IEEE Transactions on Nanobioscience

Siavash Ghavami, Farshad Lahouti
A nano abnormality detection scheme (NADS) in molecular nano-networks is studied. This is motivated by the fact that early detection of diseases such as cancer play a crucial role in their successful treatment. The proposed NADS is in fact a two-tier network of sensor nano-machines (SNMs) in the first tier and a data-gathering node (DGN) at the sink. The SNMs detect the presence of competitor cells (abnormality) by variations in input and/or parameters of a nano-communications channel (NCC). The noise of SNMs as their nature suggest is considered correlated in time and space and herein assumed additive Gaussian...
March 2, 2017: IEEE Transactions on Nanobioscience
Michal Marczyk
2D gel electrophoresis is the most commonly used method in biomedicine to separate even thousands of proteins in a complex sample on a single gel. Even though the technique is quite known, there is still a need to find an efficient and reliable method for detection of protein spots on gel image. In this paper a three step algorithm based on mixture of 2D normal distribution functions is introduced to improve the efficiency of spot detection performed by the existing algorithms, namely Pinnacle software and watershed segmentation method...
March 1, 2017: IEEE Transactions on Nanobioscience
Takuya Moriyama, Yuichi Shiraishi, Kenichi Chiba, Rui Yamaguchi, Seiya Imoto, Satoru Miyano
Detection of somatic mutations from tumor and matched normal sequencing data has become a standard approach in cancer research. Although a number of mutation callers have been developed, it is still difficult to detect mutations with low allele frequency even in exome sequencing. We expect that overlapping paired-end read information is effective for this purpose, but no mutation caller has modeled overlapping information statistically in a proper form in exome sequence data. Here, we develop a Bayesian hierarchical method, OVar- Call (https://github...
March 1, 2017: IEEE Transactions on Nanobioscience
Peter Gawthrop
Engineering systems modelling and analysis based on the bond graph approach has been applied to biomolecular systems. In this context, the notion of a Faraday-equivalent chemical potential is introduced which allows chemical potential to be expressed in an analogous manner to electrical volts thus allowing engineering intuition to be applied to biomolecular systems. Redox reactions, and their representation by half-reactions, are key components of biological systems which involve both electrical and chemical domains...
February 24, 2017: IEEE Transactions on Nanobioscience
Yifan Chen, Yu Zhou, Ross Murch, Panagiotis Kosmas
To maximize the effect of treatment and minimize the adverse effect on patients, we propose to optimize nanorobotsassisted targeted drug delivery (TDD) for locoregional treatment of tumor from the perspective of touchable communication channel estimation and waveform design. The drug particles are the information molecules; the loading/injection and unloading of the drug correspond to the transmitting and receiving processes; the concentration-time profile of the drug particles administered corresponds to the signalling pulse...
February 15, 2017: IEEE Transactions on Nanobioscience
Guang Ling, Zhi-Hong Guan, Bin Hu, Qiang Lai, Yonghong Wu
Many biological systems have the conspicuous property to present more than one stable state and diverse rhythmic behaviors. A closed relationship between these complex dynamic behaviors and cyclic genetic structures has been witnessed by pioneering works. In this paper, a typical structure of inhibitory coupled cyclic genetic networks is introduced to further enlighten this mechanism of stability and biological rhythms of living cells. The coupled networks consist two identical cyclic genetic subnetworks, which inhibit each other directly...
February 14, 2017: IEEE Transactions on Nanobioscience
Ryan Eshleman, Rahul Singh
Identifying the temporal progression of a set of biological samples is crucial for comprehending the dynamics of the underlying molecular interactions. It is often also a basic step in data denoising and synchronization. Finally, identifying the progression order is crucial for problems like cell lineage identification, disease progression, tumor classification, and epidemiology and thus impacts the spectrum of disciplines spanning basic biology, drug discovery, and public health. Current methods that attempt solving this problem face difficulty when it is necessary to factor-in complex relationships within the data such as grouping, partial ordering or bifurcating or multifurcating progressions...
February 9, 2017: IEEE Transactions on Nanobioscience
Letu Qingge, Xiaowen Liu, Farong Zhong, Binhai Zhu
In mass spectrometry-based de novo protein sequencing, it is hard to complete the sequence of the whole protein. Motivated by this we study the (one-sided) problem of filling a protein scaffold S with some missing amino acids, given a sequence of contigs none of which is allowed to be altered, with respect to a complete reference protein P of length n, such that the BLOSUM62 score between P and the filled sequence S' is maximized. We show that this problem is polynomial-time solvable in O(n26) time. We also consider the case when the contigs are not of high quality and they are concatenated into an (incomplete) sequence I, where the missing amino acids can be inserted anywhere in I to obtain I', such that the BLOSUM62 score between P and I' is maximized...
February 9, 2017: IEEE Transactions on Nanobioscience
Leyi Wei, Pengwei Xing, Jijun Tang, Quan Zou
Many recent efforts have been made for the development of machine learning based methods for fast and accurate phosphorylation site prediction. Currently, a majority of well-performing methods are based on hybrid information to build prediction models, such as evolutionary information, and disorder information, etc. Unfortunately, this type of methods suffers two major limitations: one is that it would be not much of help for protein phosphorylation site prediction in case of no obvious homology detected; the other is that computing such the complicated information is time-consuming, which probably limits the usage of predictors in practical applications...
January 31, 2017: IEEE Transactions on Nanobioscience
Hesham G Moussa, Ghaleb A Husseini, Nabil Abdel-Jabbar, Salma E Ahmed
The use of echogenic liposomes to deliver chemotherapeutic agents for cancer treatment has gained wide recognition in the last twenty years. Cancerous cells can develop multiple drug resistance (MDR), in part, due to the drop of concentration of chemotherapeutic agents below the therapeutic levels inside the tumor. This suggests that MDR can be reduced by controlling the level of drug release in the diseased area. In this work, a Model Predictive Controller based on Neural Networks is proposed to maintain a constant chemotherapeutic release at the cancer site...
January 30, 2017: IEEE Transactions on Nanobioscience
Emily Flynn, Ileana Streinu
We describe efficient methods for consistently coloring and visualizing collections of rigid cluster decompositions obtained from variations of a protein structure, and lay the foundation for more complex setups that may involve different computational and experimental methods. The focus here is on three biological applications: the conceptually simpler problems of visualizing results of dilution and mutation analyses, and the more complex task of matching decompositions of multiple NMR models of the same protein...
January 27, 2017: IEEE Transactions on Nanobioscience
Min Ye, Xiuwei Zhang, Gabriela C Racz, Qijia Jiang, Bernard M E Moret
Modelling the evolution of biological networks is a major challenge. Biological networks are usually represented as graphs; evolutionary events include addition and removal of vertices and edges, but also duplication of vertices and their associated edges. Since duplication is viewed as a primary driver of genomic evolution, recent work has focused on duplication-based models. Missing from these models is any embodiment of modularity, a widely accepted attribute of biological networks. Some models spontaneously generate modular structures, but none is known to maintain and evolve them...
January 19, 2017: IEEE Transactions on Nanobioscience
Malay Bhattacharyya, Soumi Maity, Sanghamitra Bandyopadhyay
Disease dietomics is an emerging area of systems biology that attempts to explore the connections between the dietary habits and diseases. Some of the topical studies highlight that foods might have different impacts over an organism either in progressing a disease (negative association) or in fighting against it (positive association). The association of foods with different diseases can be put together to build a network that might provide a global view of the entire system. Again, such disease-food networks might emerge in a more complex form while considering the disease subtypes individually...
January 16, 2017: IEEE Transactions on Nanobioscience
Akiko Tanaka, Tomoyuki Furubayashi, Hitomi Yamasaki, Katsuko Takano, Mayuko Kawakami, Shunsuke Kimura, Daisuke Inoue, Hidemasa Katsumi, Toshiyasu Sakane, Akira Yamamoto
For nasal drug absorption, powder formulations can be expected to provide many advantages. The first aim of this study was to examine drug absorption following nasal administration of powder formulations in rats. Pharmaceutical excipients are typically added to most powder formulations. The second aim was to investigate the change in nasal drug absorption of powder formulations in the presence of sodium carboxymethyl cellulose (CMC-Na). Model drugs used were norfloxacin (NFX), warfarin (WF), and piroxicam (PXC)...
January 4, 2017: IEEE Transactions on Nanobioscience
Feng Bao, Yue Deng, Yanyu Zhao, Jinli Suo, Qionghai Dai
In genome-wide association studies (GWAS), the acquired sequential data may exhibit imbalance structure: abundant control vs. limited case samples. Such sample imbalance issue is particularly serious when investigating rare diseases or common diseases on rare populations. Conventional GWAS methods may suffer from severe statistic biases to the major group, leading to power losses in uncovering true suspicious loci. We introduce a boosting correction method termed as Bosco to deal with such imbalanced problem...
January 2017: IEEE Transactions on Nanobioscience
Zeng Yu, Tianrui Li, Shi-Jinn Horng, Yi Pan, Hongjun Wang, Yunge Jing
Microarray data often contain missing values which significantly affect subsequent analysis. Existing LLSimpute-based imputation methods for dealing with missing data have been shown to be generally efficient. However, all of the LLSimpute-based methods do not consider the different importance of different neighbors of the target gene in the missing value estimation process and treat all the neighbors equally. In this paper, a locally auto-weighted least squares imputation (LAW-LSimpute) method is proposed for missing value estimation, which can automatically weight the neighboring genes based on the importance of the genes...
January 2017: IEEE Transactions on Nanobioscience
Saurav Mallik, Tapas Bhadra, Ujjwal Maulik
Epigenetic Biomarker discovery is an important task in bioinformatics. In this article, we develop a new framework of identifying statistically significant epigenetic biomarkers using maximal-relevance and minimal-redundancy criterion based feature (gene) selection for multi-omics dataset. Firstly, we determine the genes that have both expression as well as methylation values, and follow normal distribution. Similarly, we identify the genes which consist of both expression and methylation values, but do not follow normal distribution...
January 2017: IEEE Transactions on Nanobioscience
Jiawei Luo, Gen Xiang, Chu Pan
It is well known that regulators known as microRNA (miRNA) and transcription factor (TF) have been found to play an important role in gene regulation. However, there are few researches of collaborative regulatory (co-regulatory) mechanism between miRNA and TF on system level (function level). Meanwhile, recent advances in high-throughput genomic technologies have enabled researchers to collect diverse large-scale genomic data, which can be used to study the co-regulatory mechanism between miRNA and TF. In this paper, we propose a novel method called Sparse Network regularized non-negative matrix factorization for co-regulatory modules identification which adopts multiple non-negative matrix factorization framework to identify co-regulatory modules including miRNAs, TFs and genes...
January 2017: IEEE Transactions on Nanobioscience
Martin Damrath, Sebastian Korte, Peter Adam Hoeher
This paper introduces the equivalent discrete-time channel model (EDTCM) to the area of diffusion-based molecular communication (DBMC). Emphasis is on an absorbing receiver, which is based on the so-called first passage time concept. In the wireless communications community the EDTCM is well known. Therefore, it is anticipated that the EDTCM improves the accessibility of DBMC and supports the adaptation of classical wireless communication algorithms to the area of DBMC. Furthermore, the EDTCM has the capability to provide a remarkable reduction of computational complexity compared to random walk based DBMC simulators...
January 2017: IEEE Transactions on Nanobioscience
Ka-Chun Wong, Chengbin Peng, Shankai Yan, Cheng Liang
Understanding genome-wide protein-DNA interaction signals forms the basis for further focused studies in gene regulation. In particular, the chromatin immunoprecipitation with massively parallel DNA sequencing technology (ChIP-Seq) can enable us to measure the in vivo genome-wide occupancy of the DNA-binding protein of interest in a single run. Multiple ChIP-Seq runs thus inherent the potential for us to decipher the combinatorial occupancies of multiple DNA-binding proteins. To handle the genome-wide signal profiles from those multiple runs, we propose to integrate regularized regression functions (i...
January 2017: IEEE Transactions on Nanobioscience
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