2K09 and thereafter : the coming era of integrative bioinformatics, systems biology and intelligent computing for functional genomics and personalized medicine research

Significant interest exists in establishing synergistic research in bioinformatics, systems biology and intelligent computing. Supported by the United States National Science Foundation (NSF), International Society of Intelligent Biological Medicine (http://www.ISIBM.org), International Journal of Computational Biology and Drug Design (IJCBDD) and International Journal of Functional Informatics and Personalized Medicine, the ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (ISIBM IJCBS 2009) attracted more than 300 papers and 400 researchers and medical doctors world-wide. It was the only inter/multidisciplinary conference aimed to promote synergistic research and education in bioinformatics, systems biology and intelligent computing. The conference committee was very grateful for the valuable advice and suggestions from honorary chairs, steering committee members and scientific leaders including Dr. Michael S. Waterman (USC, Member of United States National Academy of Sciences), Dr. Chih-Ming Ho (UCLA, Member of United States National Academy of Engineering and Academician of Academia Sinica), Dr. Wing H. Wong (Stanford, Member of United States National Academy of Sciences), Dr. Ruzena Bajcsy (UC Berkeley, Member of United States National Academy of Engineering and Member of United States Institute of Medicine of the National Academies), Dr. Mary Qu Yang (United States National Institutes of Health and Oak Ridge, DOE), Dr. Andrzej Niemierko (Harvard), Dr. A. Keith Dunker (Indiana), Dr. Brian D. Athey (Michigan), Dr. Weida Tong (FDA, United States Department of Health and Human Services), Dr. Cathy H. Wu (Georgetown), Dr. Dong Xu (Missouri), Drs. Arif Ghafoor and Okan K Ersoy (Purdue), Dr. Mark Borodovsky (Georgia Tech, President of ISIBM), Dr. Hamid R. Arabnia (UGA, Vice-President of ISIBM), and other scientific leaders. The committee presented the 2009 ISIBM Outstanding Achievement Awards to Dr. Joydeep Ghosh (UT Austin), Dr. Aidong Zhang (Buffalo) and Dr. Zhi-Hua Zhou (Nanjing) for their significant contributions to the field of intelligent biological medicine

A probabilistic framework to predict protein function from interaction data integrated with semantic knowledge

<p>Abstract</p> <p>Background</p> <p>The functional characterization of newly discovered proteins has been a challenge in the post-genomic era. Protein-protein interactions provide insights into the functional analysis because the function of unknown proteins can be postulated on the basis of their interaction evidence with known proteins. The protein-protein interaction data sets have been enriched by high-throughput experimental methods. However, the functional analysis using the interaction data has a limitation in accuracy because of the presence of the false positive data experimentally generated and the interactions that are a lack of functional linkage.</p> <p>Results</p> <p>Protein-protein interaction data can be integrated with the functional knowledge existing in the Gene Ontology (GO) database. We apply similarity measures to assess the functional similarity between interacting proteins. We present a probabilistic framework for predicting functions of unknown proteins based on the functional similarity. We use the leave-one-out cross validation to compare the performance. The experimental results demonstrate that our algorithm performs better than other competing methods in terms of prediction accuracy. In particular, it handles the high false positive rates of current interaction data well.</p> <p>Conclusion</p> <p>The experimentally determined protein-protein interactions are erroneous to uncover the functional associations among proteins. The performance of function prediction for uncharacterized proteins can be enhanced by the integration of multiple data sources available.</p

Study on Location Algorithms of Beamforming based on MVDR

Acoustic emission is an effective method of locating the rubbing fault. In order to solve the problem that satisfactory location accuracy is difficult to obtain because of the waveform distortion caused by signal propagation during the application of time delay estimation method in acoustic emission position estimation, beam-forming technique is applied to acoustic emission source location. Simulation studies have been made on the performance of near-field time-domain and frequency domain beam-forming in the location of rubbing acoustic emission source. The paper adopts the wideband signal minimum variance distortionless response (MVDR) location estimation method based on sub-band decomposition to avoid the problems of poor noise immunity and low resolution of traditional beam-forming. Decompose each group of array signals into a number of sub-band of equal length, conduct Fourier transformation on each sub-band to calculate the covariance matrix of each frequency component, get the two-dimensional joint distribution function of the MVDR output power of each sub-band with respect to the distance and azimuth angle, then synthesize the MVDR power of wideband signal, obtain the azimuth spectrum estimation of all frequency bands, and finally get the location of the acoustic source by the peak point. The experimental results show that this algorithm can accurately identify the rubbing fault location

ZnFe2O4-TiO2 Nanoparticles within Mesoporous MCM-41

A novel nanocomposite ZnFe2O4-TiO2/MCM-41 (ZTM) was synthesized by a sol-gel method and characterized through X-ray diffraction (XRD), high-resolution transmission electron microscopy (HRTEM), N2 adsorption-desorption, Raman spectroscopy, and ultraviolet visible (UV-vis) spectrophotometry. The results confirmed the incorporation of ZnFe2O4-TiO2 nanoparticles inside the pores of the mesoporous MCM-41 host without destroying its integrity. ZnFe2O4 nanoparticles can inhibit the transformation of anatase into rutile phase of TiO2. Incorporation of ZnFe2O4-TiO2 within MCM-41 avoided the agglomeration of nanoparticles and reduced the band gap energy of TiO2 to enhance its visible light photocatalytic activity. UV-vis absorption edges of ZTM nanocomposites redshifted with the increase of Zn/Ti molar ratio. The nanocomposite approach could be a potential choice for enhancing the photoactivity of TiO2, indicating an interesting application in the photodegradation and photoelectric fields

An improved multi-scale branching convolutional neural network for rolling bearing fault diagnosis.

The vibration signals measured in practical engineering are usually complex and noisy, which brings challenges to fault diagnosis. In addition, industrial scenarios also put forward higher requirements for the accuracy and computational efficiency of diagnostic models. Aiming at these problems, an improved multiscale branching convolutional neural network is proposed for rolling bearing fault diagnosis. The proposed method first applies the multiscale feature learning strategy to extract rich and compelling fault information from diverse and complex vibration signals. Further, the lightweight dynamic separable convolution is elaborated and coupled into the feature extractor to "slim down" the model, reduce the computational loss on the one hand, and further improve the model's adaptive learning ability for different inputs hand. Extensive experiments indicate that the proposed method is significantly improved compared with existing multi-scale neural networks

Localization of Acoustic Emission Source Based on Chaotic Neural Networks

Because of containing several model waveforms and transmission speed of each model are various, the source signal of rub-impact acoustic emission (AE) will lead to waveform distortion in propagation process, and it is difficult to achieve exact source location by traditional time difference of arrival algorithm. A chaotic neural network technique was introduced to calculate the location of AE source. Numerous researches show that rotor rub-impact fault has sufficient non-linear features, so obtain the characteristics of the non-linear dynamics which reveal the AE source form the rub-impact data by using the chaos theory and use it as the input of the neural network to get the localization. We propose a modified Gaussian Mixed Model (GMM) with an embedded Time Delay Neural Network (TDNN). It integrates the merits of GMM and TDNN. Simulation results prove, theoretically and practically, that it can locate AE source efficiently and provide the basis for the rotor rub-impact fault diagnosis, so it has good application prospect and is worth to research further more

Resonance-based sparse adaptive variational mode decomposition and its application to the feature extraction of planetary gearboxes.

Due to the assumption that the VMD technique is essentially a set of adaptive Wiener filter banks and its performance depends to a large extent on the preset parameter K (the number of decomposition). A new method named resonance-based sparse adaptive variational mode decomposition (RSAVMD) is proposed for the decomposition of planetary gearbox vibration signals. Tunable Q-Factor Wavelet Transform (TQWT) and morphological component analysis (MCA) are introduced to decompose the original signal into high and low resonance components. High resonance components containing planetary gearbox signals are screened for analysis. At the same time, Quality factor is used to select the number of Variational mode decomposition (VMD) adaptively. This method was applied in fault diagnosis of planetary gearbox. Compared with VMD, RASVMD could extract fault characteristic frequency of planetary gearbox accurately, but VMD lost part of fault information, showing the superiority of RSAVMD. Simultaneously, the selection method of VMD decomposition number in literature was cited, and it was found that the decomposition number selected by the method in this paper was more accurate

Recognition of Acoustic Emission Signal based on the Algorithms of TDNN and GMM

Abstract: Friction fault diagnosis of rotating machinery based on acoustic emission (AE) technique is a research hotspot in recent years. The rotating machinery will produce multi-source noise during the operation process, so how to correctly identify the friction acoustic emission signals has become a key factor for accurate diagnosis of the fault. In this paper, it proposes a Gaussian mixed model (GMM) based on an embedded time delay neural network (TDNN) to identify friction acoustic emission signals. It comprehensively utilizes the advantages of the learning ability of time delay neural network about data structure and data distribution presentation capability of Gaussian mixture model. Time delay neural network fully exploits the time-ordered of eigenvector set, makes the maximum likelihood probability more reasonable which needs to assume that the variables are independent of each other through the transformation of the time delay network and uses them for the training as a whole with the criteria of maximum likelihood (ML) probability. During the training process, the parameters of Gaussian mixture model and neural network update alternately. The average amplitude, maximum amplitude, amplitude dynamic range, the Hurst exponent and approximate entropy (ApEn) of friction acoustic emission signals are selected as the characteristic parameters of fault recognition and these five parameters constitutes the input parameters vector of the identification model. Through the verification the AE signals of different friction states collected on the rotor test bed, the experimental results show that the identification method of rotor friction acoustic emission signals of Gaussian mixe