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A sequence-based multiple kernel model for identifying dna-binding proteins

BMC BIOINFORMATICS 2021年第SUPPL 3期22卷 291-291页

作者： Qian, Yuqing Jiang, Limin Ding, Yijie Tang, Jijun Guo, Fei Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol 1068 Xueyuan Ave Shenzhen Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

Background: dna-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consuming. In recent years, Machine Learning methods have been used to detect DBP. However, it is difficult to adequately describe the information of proteins in predicting dna-binding proteins. In this study, we extract six features from protein sequence and use Multiple Kernel Learning-based on Centered Kernel Alignment to integrate these features. the integrated feature is fed into Support Vector Machine to build predictive model and detect new DBP. Results: In our work, date sets of PDB1075 and PDB186 are employed to test our method. From the results, our model obtains better results (accuracy) than other existing methods on PDB1075 (84.19%) and PDB186 (83.7%), respectively. Conclusion: Multiple kernel learning could fuse the complementary information between different features. Compared with existing methods, our method achieves comparable and best results on benchmark data sets.

关键词： dna-binding proteins Feature extraction Multiple kernel learning Support vector machine Centered kernel alignment

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Solving generic decision problems by in-message computation in dna-based molecular nanonetworks 15th

Solving generic decision problems by in-message computation ...

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15th international conference on Body Area Networks, BodyNets 2020

作者： Lau, Florian-Lennert Adrian Wendt, Regine Fischer, Stefan Institute of Telematics Ratzeburger Allee 160 Lübeck23562 Germany

ISBN: (纸本)9783030649906

One of the biggest unsolved problems in nanonetwork research is the actual construction of the components required for building such networks. Most existing ideas are limited to partial solutions of construction of nanodevices, computation within them, and communication between them. While many ideas are promising, the problem remains how to combine those various building blocks into operational and efficient nanonetworks. In this paper we use dna as the basic building block for all components of nanonetworks. the inherent properties of this molecule are used to assemble complex nanostructures. dna can be utilized to create both nanodevices and a communication mechanism. Properly designed dna-molecules can even be utilized for computational purposes. In summary, dna forms the base for an exhaustive nanonetwork concept. this work specifically presents an approach how to solve arbitrary mathematical problems that can be modeled as boolean formulas using dna-based nanonetworks by in-message computation. the computation itself is encoded in the assembly process of a message. this avoids often-stated space constraints for computations at the nanoscale, as the medium of transportation is commonly less constrained than the size of nanodevices dictates. this method thereby presents a constructive approach on how to actually create message molecules, rather than only proving the general possibility. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.

关键词： Nanostructures

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Research on the Application of dna Cryptography in Electronic Bidding System 14th

Research on the Application of DNA Cryptography in Electroni...

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14th international conference on Bio-inspired computing: theories and Applications, BIC-TA 2019

作者： Liu, Jianxia Jiao, Yangyang Wang, Yibo Li, Hongxuan Zhang, Xuncai Cui, Guangzhao School of Information Management Nanjing University Nanjing210000 China School of Economics and Management Wuhan University Wuhan430072 China School of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou450002 China

ISBN: (纸本)9789811534140

With the rapid development of bidding market, a new type of bidding method-electronic bidding method has been widely used in the bidding market. this new-type of method is a program that the bidding is completed by graphics and textures. Generally, the electronic bidding provides doubles bidders with the data information of the transaction through the Internet. In this study we propose an information encryption and digital signature method based on dna cryptography which is combined with HASH function, symmetric encryption and asymmetric encryption comprehensively for the purpose of the information resources’ integrality, usability, security and reliability in the electronic bidding system. the results of the study show that our new method provides double guarantee for the bidding system under the role of symmetric encryption and asymmetric encryption used dna cryptography, and dna molecular encryption has the characteristics of electronic immunity, concealment, independent of mathematical problems, massive parallelism, etc. It provides a double guarantee for the bidding system. the security analysis results show that the algorithm can effectively protect the security and integrity of the bidder and bidder’s data information. © 2020, Springer Nature Singapore Pte Ltd.

关键词： dna

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GPU computing for Compute-Intensive Scientific Calculation 8th

GPU Computing for Compute-Intensive Scientific Calculation

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8th international conference on Soft computing for Problem Solving (SocProS)

作者： Dubey, Sandhya Parasnath Kumar, M. Sathish Balaji, S. Manipal Acad Higher Educ Manipal Inst Technol Dept Comp Applicat Manipal 576104 India Manipal Acad Higher Educ Manipal Inst Technol Dept Elect & Commun Manipal 576104 India Manipal Acad Higher Educ Manipal Inst Technol Dept Biotechnol Manipal 576104 India

ISBN: (纸本)9789811501845;9789811501838

GPU has emerged as a platform that off-loads computation intensive work from CPU and performs numerical computations in less time. One such mathematical operation is matrix multiplication. Matrix is one of the fundamental mathematical objects used in the scientific calculation, with applicability in various fields such as computer graphics, analysis of electrical circuits, computer networks, dna sequence comparison, protein structure prediction, etc. this work presents a comparative analysis of scalar matrix multiplication in three modes, namely: (i) sequential programming in C language (ii) parallel implementations using OpenCL, and (iii) MPI. the testbed comprises of input matrices ranging from small size of 100 x 100 to a higher size of 12,800 x 12,800. We observe that parallel execution in OpenCL outperforms MPI and sequential C for higher dimensional matrices. In contrast, sequential C outperforms both MPI and OpenCL for small dimension matrices. Besides, we analyze that OpenCL program has attained a speedup of 9x. therefore, we conclude that parallel execution of code is more efficient for data of computationally large sizes and hence provides a potentially useful solution to address NP-complete problems.

关键词： HPC GPU Matrix multiplication OpenCL MPI

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15th international conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2018

15th International Conference on Computational Intelligence ...

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15th international conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2018

ISBN: (纸本)9783030345846

the proceedings contain 30 papers. the special focus in this conference is on Computational Intelligence Methods for Bioinformatics and Biostatistics. the topics include: Recurrent Deep Neural Networks for Nucleosome Classification;searching for the Source of Difference: A Graphical Model Approach;a New Partially Segment-Wise Coupled Piece-Wise Linear Regression Model for Statistical Network Structure Inference;Inhibition of Primed Ebola Virus Glycoprotein by Peptide Compound Conjugated to HIV-1 Tat Peptide through a Virtual Screening Approach;pharmacophore Modelling, Virtual Screening, and molecular Docking Simulations of Natural Product Compounds as Potential Inhibitors of Ebola Virus Nucleoprotein;global Sensitivity Analysis of Constraint-Based Metabolic Models;efficient and Settings-Free Calibration of Detailed Kinetic Metabolic Models with Enzyme Isoforms Characterization;automatic Discrimination of Auditory Stimuli Perceived by the Human Brain;neural Models for Brain Networks Connectivity Analysis;Variational Inference in Probabilistic Single-cell RNA-seq Models;exposing and Characterizing Subpopulations of Distinctly Regulated Genes by K-Plane Regression;network Propagation-Based Semi-supervised Identification of Genes Associated with Autism Spectrum Disorder;designing and Evaluating Deep Learning Models for Cancer Detection on Gene Expression Data;analysis of Extremely Obese Individuals Using Deep Learning Stacked Autoencoders and Genome-Wide Genetic Data;predicting the Oncogenic Potential of Gene Fusions Using Convolutional Neural Networks;unravelling Breast and Prostate Common Gene Signatures by Bayesian Network Learning;Effect of Epigallocatechin-3-gallate on DMPC Oxidation Revealed by Infrared Spectroscopy;Effect of EGCG on the dna in Presence of UV Radiation;non-thermal Atmospheric Pressure Plasmas: Generation, Sources and Applications;Adsorption of Triclosan on Sensors Based on PAH/PAZO thin-Films: the Effect of pH;improved Predictor-Corrector Algorith

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Fabrication of a Carbon Chain Based Nanosensor for Maximizing Spatial Resolution in dna Sequencing

Fabrication of a Carbon Chain Based Nanosensor for Maximizin...

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IEEE international conference on Nano/Micro Engineered and molecular Systems (NEMS)

作者： Bo Ma Steve Tung University of Arkansas USA

ISBN: (数字)9781728172309

ISBN: (纸本)9781728172316

Using a carbon chain as the sensing element can significantly improve the spatial resolution in nanodevice based single molecule dna sequencing. In this report, we demonstrate a top-down approach to reduce a single monolayer graphene attached to a silicon microelectrode chip to a sub-nanometer wide carbon chain suitable for dna sequencing. this approach consists three major processing steps to first transfer the graphene to the chip, dice the attached graphene into graphene nanoribbons using Focused Ion Beam (FIB), and finally reduce the ribbon width by knocking off carbon atoms on the ribbon edge using Transmission Electron Microscopy (TEM). A final carbon chain of 0.8 nm wide and 5 nm long is accomplished. the measured electrical properties of the reduced graphene agree well with previous studies.

关键词： conferences

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Identification of dna-Binding Proteins via Fuzzy Multiple Kernel Model and Sequence Information 15th

Identification of DNA-Binding Proteins via Fuzzy Multiple Ke...

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15th international conference on Intelligent computing, ICIC 2019

作者： Ding, Yijie Tang, Jijun Guo, Fei School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou China School of Computer Science and Technology College of Intelligence and Computing Tianjin University No. 135 Yaguan Road Tianjin Haihe Education Park Tianjin China Department of Computer Science and Engineering University of South Carolina Columbia United States

ISBN: (纸本)9783030269685

dna-binding proteins is the molecular basis for understanding the basic processes of life activities. Many diseases are associated with dna binding proteins. the methods of detecting dna-binding proteins are mainly realized by biochemical experiment, which is time consuming and extremely expensive. A lot of computational methods based on Machine Learning (ML) algorithm have been developed to detect dna-binding proteins. In this study, we propose a novel dna-binding proteins model via a Fuzzy Multiple Kernel Support Vector Machine. the multiple features of sequence and evolutionary are extracted and constructed as multiple kernels, respectively. Next, these corresponding kernels are integrated by Multiple Kernel Learning (MKL) algorithm. At last, Fuzzy Support Vector Machine (FSVM) is employed to build an effective dna-binding protein predictor. Comparing with other outstanding methods, our proposed approach achieves good results. the accuracy of our model are 82.98% and 81.70% on PDB1075 (benchmark data set of dna-binding proteins) and PDB186 (independent test set), respectively. Our approach is comparable to previous methods. © 2019, Springer Nature Switzerland AG.

关键词： Learning algorithms

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A Low-drift Extended-Gate Field Effect Transistor (EGFET) with Differential Amplifier for Cordyceps Sinensis dna Detection Optimized by gm/ID theory

A Low-drift Extended-Gate Field Effect Transistor (EGFET) wi...

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IEEE international conference on Nano/Micro Engineered and molecular Systems (NEMS)

作者： Yifan Xu Hadi Tavakkoli Jingting Xu Yi-Kuen Lee Hong Kong University of Science and Technology HKSAR China HKUST HKSAR China

ISBN: (数字)9781728172309

ISBN: (纸本)9781728172316

An extended-gate field effect transistor (EGFET) integrated with a differential MOSFET amplifier and an open-source Arduino Yun MCU system was realized for detection of Cordyceps Sinensis dna molecules. A gold microelectrode chip coated with a single-stranded dna probe, as the extended gate was fabricated by MEMS fabrication processes. the differential MOSFET amplifier and additional coating of alkyl-thiol reduced the drifting by one order of magnitude. Moreover, generalized g m /I D theory was used to study the optimized working regime of the EGFET sensor. the highest electric-electrochemical sensitivity could be achieved in the Moderate Inversion (MI) regime. the sensitivity and limit of detection (LOD) of the EGFET sensor were obtained to be 13.85mV/dec and 10nM, respectively. this low-cost low-drift EGFET sensor system is promising for Internet of Living things in the near future.

关键词： dna Probes Sensitivity Gold MOSFET Logic gates Fabrication

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A Prediction Method of dna-Binding Proteins Based on Evolutionary Information 15th

A Prediction Method of DNA-Binding Proteins Based on Evoluti...

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15th international conference on Intelligent computing, ICIC 2019

作者： Lu, Weizhong Song, Zhengwei Ding, Yijie Wu, Hongjie Huang, Hongmei School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Suzhou Key Laboratory of Virtual Reality Intelligent Interaction and Application Technology Suzhou University of Science and Technology Suzhou215009 China

ISBN: (纸本)9783030269685

dna is the carrier of genetic information in organisms, and dna-binding protein is one type of unwinding enzymes, which plays a key role in various biological molecular functions. that has greatly promoted the research of various methods for identifying dna-binding proteins. In recent years, researchers have developed a Machine Learning-based method to predict dna-binding proteins quickly and accurately. Although the prediction accuracy of current methods is considerable, the performance of their prediction can be further improved. In this paper, a dna-binding proteins prediction model based on PSSM (Position Specific Scoring Matrix) features and Random Forest classifier is proposed. the results of experiments show that the proposed method can achieve great prediction performance on PDB1075 and PDB186 datasets, whose accuracy is 82.14% and 79.0%, respectively. Experiments show that the method can be compared with other methods, and even surpass the previous methods on some datasets. © 2019, Springer Nature Switzerland AG.

关键词： dna

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Fabrication of Small-Scale Solid-State Nanopores by Dielectric Breakdown

Fabrication of Small-Scale Solid-State Nanopores by Dielectr...

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IEEE international conference on Nano/Micro Engineered and molecular Systems (NEMS)

作者： Zengdao Gu Dexian Ma Zhicheng Zhang Yin Zhang Jingjie Sha School of Mechanical Engineering Southeast University Nanjing P. R. China

ISBN: (数字)9781728172309

ISBN: (纸本)9781728172316

As a tool for next-generation dna sequencing, solid-state nanopores have significant advantages in terms of stability and process integration. However, traditional manufacturing methods such as FIB and TEM are pretty expensive and require higher-skilled operators. In this paper, we explored the effect of the initial leakage current on the dielectric breakdown time and then demonstrated a low-cost and rapid method of fabricating SiNx nanopores. We prepared nanopores of appropriate size by changing the voltage and cut-off current, and finally realized the preparation of nanopores as small as a few nanometers and as large as tens of nanometers. We also found that the thinned area of silicon nitride film affects the value of the initial leakage current, thereby impacting the time of dielectric breakdown. the foundation of these problems helps us to perfect the fabrication of nanopores by dielectric breakdown.

关键词： Nanobioscience Leakage currents Dielectric breakdown Fabrication Current measurement Breakdown voltage dna

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