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Search Results: 1 - 10 of 11966 matches for " ZhiWei Cao "
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Correlation of Exchange Bias and Angle on Applied Perturbation Field and Anti-Ferromagnetic Spins  [PDF]
Xinwen Fu, Guixin Cao, Yuze Gao, Zhiwei Wu, Jincang Zhang
Journal of Modern Physics (JMP) , 2011, DOI: 10.4236/jmp.2011.210148
Abstract: High-resolution anisotropic magneto-resistance measurement (AMR) was used to detailed study the training effect in exchange biased CoO/Co bi-layer. The sample was cooled to 10 K from room temperature in the magnetic cooling field of 4000 Oe. Then we used 1500 Oe declined perturbation field to pin the magnetization orientation of the FM layer. The perturbation field forms certain angle Θ with the cooling field direction in-plane to re-induce the untrained state. The dependence of the untrained state on the angle between the direction of perturbation field and cooling field has been investigated. The AMR results reveal that the re-induced degree of untrained state is strongly correlated to the angle Θ. The exchange bias field HE for different Θ has been determined from the AMR results, which is in apparent agreement with the Meiklejohn-Bean model. The recover degree of untrained state is the largest when the angle is 75°, which is different from the traditional view point that untrained state should be the maximum when it is perpendicular. The training effect is related to the FM spin orientation, which can induce the change of the interfacial AFM spin reorientation with different angles.
Heart Murmur Recognition Based on Hidden Markov Model  [PDF]
Lisha Zhong, Jiangzhong Wan, Zhiwei Huang, Gaofei Cao, Bo Xiao
Journal of Signal and Information Processing (JSIP) , 2013, DOI: 10.4236/jsip.2013.42020
Abstract:

Heart murmur recognition and classification play an important role in the auscultative diagnosis. The method based on hidden markov model (HMM) was presented to recognize the heart murmur. The murmur was isolated on basis of the principle of wavelet analysis considering the time-frequency characteristics of the heart murmur. This method uses Mel frequency cepstral coefficient (MFCC) to extract representative features and develops hidden Markov model (HMM) for signal classification. The result shows that this method is able to recognize the murmur efficiently and superior to BP neural network (94.2% vs 82.8%). And the findings suggest that the method may have the potential to be used to assist doctors for a more objective diagnosis.

Study of Scanning Dose Optimization on Chest and Abdomen Enhanced CT Imaging  [PDF]
Zhiwei Huang, Lisha Zhong, Bo Xiao, Gaofei Cao
Journal of Signal and Information Processing (JSIP) , 2013, DOI: 10.4236/jsip.2013.42021
Abstract:


Objective: To investigate the correlation between radiation dose and radiation risk when patients are scanned by 64-slice spiral CT. Materials and Methods: SPSS 17.0 is used statistically for analyzing the patient’s scanning parameters, radiation dose of monitoring and examining the patients who are scanning of their abdomen, chest and pelvic in our affiliated hospital. Results: SPSS statistical analysis shows that the factor related to radiation dose is scanning layer; the basic characteristics such as height and heart rate don’t affect the patient’s scan dose directly. Conclusion: Increasing the delay time after injection can reduce the scan numbers and monitoring layers of the machine, thus reduce the patient’s radiation dose and tube’s exposure time.


Reconsideration of In-Silico siRNA Design Based on Feature Selection: A Cross-Platform Data Integration Perspective
Qi Liu, Han Zhou, Juan Cui, Zhiwei Cao, Ying Xu
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0037879
Abstract: RNA interference via exogenous short interference RNAs (siRNA) is increasingly more widely employed as a tool in gene function studies, drug target discovery and disease treatment. Currently there is a strong need for rational siRNA design to achieve more reliable and specific gene silencing; and to keep up with the increasing needs for a wider range of applications. While progress has been made in the ability to design siRNAs with specific targets, we are clearly at an infancy stage towards achieving rational design of siRNAs with high efficacy. Among the many obstacles to overcome, lack of general understanding of what sequence features of siRNAs may affect their silencing efficacy and of large-scale homogeneous data needed to carry out such association analyses represents two challenges. To address these issues, we investigated a feature-selection based in-silico siRNA design from a novel cross-platform data integration perspective. An integration analysis of 4,482 siRNAs from ten meta-datasets was conducted for ranking siRNA features, according to their possible importance to the silencing efficacy of siRNAs across heterogeneous data sources. Our ranking analysis revealed for the first time the most relevant features based on cross-platform experiments, which compares favorably with the traditional in-silico siRNA feature screening based on the small samples of individual platform data. We believe that our feature ranking analysis can offer more creditable suggestions to help improving the design of siRNA with specific silencing targets. Data and scripts are available at http://csbl.bmb.uga.edu/publications/mat?erials/qiliu/siRNA.html.
Investigations on Inhibitors of Hedgehog Signal Pathway: A Quantitative Structure-Activity Relationship Study
Ruixin Zhu,Qi Liu,Jian Tang,Huiliang Li,Zhiwei Cao
International Journal of Molecular Sciences , 2011, DOI: 10.3390/ijms12053018
Abstract: The hedgehog signal pathway is an essential agent in developmental patterning, wherein the local concentration of the Hedgehog morphogens directs cellular differentiation and expansion. Furthermore, the Hedgehog pathway has been implicated in tumor/stromal interaction and cancer stem cell. Nowadays searching novel inhibitors for Hedgehog Signal Pathway is drawing much more attention by biological, chemical and pharmological scientists. In our study, a solid computational model is proposed which incorporates various statistical analysis methods to perform a Quantitative S tructure- A ctivity R elationship (QSAR) study on the inhibitors of Hedgehog signaling. The whole QSAR data contain 93 cyclopamine derivatives as well as their activities against four different cell lines (NCI-H446, BxPC-3, SW1990 and NCI-H157). Our extensive testing indicated that the binary classification model is a better choice for building the QSAR model of inhibitors of Hedgehog signaling compared with other statistical methods and the corresponding in silico analysis provides three possible ways to improve the activity of inhibitors by demethylation, methylation and hydroxylation at specific positions of the compound scaffold respectively. From these, demethylation is the best choice for inhibitor structure modifications. Our investigation also revealed that NCI-H466 served as the best cell line for testing the activities of inhibitors of Hedgehog signal pathway among others.
Comparison of Different Ranking Methods in Protein-Ligand Binding Site Prediction
Jun Gao,Qi Liu,Hong Kang,Zhiwei Cao,Ruixin Zhu
International Journal of Molecular Sciences , 2012, DOI: 10.3390/ijms13078752
Abstract: In recent years, although many ligand-binding site prediction methods have been developed, there has still been a great demand to improve the prediction accuracy and compare different prediction algorithms to evaluate their performances. In this work, in order to improve the performance of the protein-ligand binding site prediction method presented in our former study, a comparison of different binding site ranking lists was studied. Four kinds of properties, i.e., pocket size, distance from the protein centroid, sequence conservation and the number of hydrophobic residues, have been chosen as the corresponding ranking criterion respectively. Our studies show that the sequence conservation information helps to rank the real pockets with the most successful accuracy compared to others. At the same time, the pocket size and the distance of binding site from the protein centroid are also found to be helpful. In addition, a multi-view ranking aggregation method, which combines the information among those four properties, was further applied in our study. The results show that a better performance can be achieved by the aggregation of the complementary properties in the prediction of ligand-binding sites.
Discrimination of approved drugs from experimental drugs by learning methods
Kailin Tang, Ruixin Zhu, Yixue Li, Zhiwei Cao
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-157
Abstract: Four methodologies were compared by their performance to classify approved drugs from experimental ones. The best results were obtained by SVM, in which the accuracy is 0.7911, the sensitivity is 0.5929, and the specificity is 0.8743. Based on the results, consensus model was developed to effectively discriminate drugs, which further pushed the correct classification rate up to 0.8517, sensitivity up to 0.7242, specificity up to 0.9352. The applications on the Traditional Chinese Medicine Ingredients Database (TCM-ID) tested the methods. Therefore this model has been proven to be a potent tool for identifying drug molecules.The studies would have potential applications in the research of combinatorial library design and virtual high throughput screening for drug discovery.In the early 1990s, the advent of high-throughput screening (HTS) and combinational chemistry methodologies was widely seen as having great potential to revolutionize modern drug discovery. However, the quality of the output from these technologies was limited than expected. Despite advances in technology and understanding of biological systems, drug discovery is still a "lengthy, expensive, difficult, and inefficient process" with low rate of new therapeutic discovery [1]. Drugs as well as drug-like compounds are distributed extremely meagerly through chemical space, which is estimated to contain ~1040 to ~10100 molecules. Among the whole chemical space, the majority is nondrug molecules, the minority is druglike molecules. To assess whether a compound is druglike or not as early as possible in drug discovery process will be extremely meritorious. Druglike compounds generally indicates molecules that contain functional groups and/or have physical properties consistent with the majority of known drugs, and hence can be inferred as compounds which might be biologically active or show therapeutic potential [2]. For a drug, properties like synthetic ease, stability, oral availability, good pharmacokin
Quantitatively integrating molecular structure and bioactivity profile evidence into drug-target relationship analysis
Tianlei Xu, Ruixin Zhu, Qi Liu, Zhiwei Cao
BMC Bioinformatics , 2012, DOI: 10.1186/1471-2105-13-75
Abstract: In this study a multi-view based clustering algorithm was introduced to quantitatively integrate compound similarity from both bioactivity profiles and structural fingerprints. Firstly, a hierarchy clustering was performed with the fused similarity on 37 compounds curated from PubChem. Compared to clustering in a single view, the overall common target number within fused classes has been improved by using the integrated similarity, which indicated that the present multi-view based clustering is more efficient by successfully identifying clusters with its members sharing more number of common targets. Analysis in certain classes reveals that mutual complement of the two views for compound description helps to discover missing similar compound when only single view was applied. Then, a large-scale drug virtual screen was performed on 1267 compounds curated from Connectivity Map (CMap) dataset based on the fused similarity, which obtained a better ranking result compared to that of single-view. These comprehensive tests indicated that by combining different data representations; an improved assessment of target-specific compound similarity can be achieved.Our study presented an efficient, extendable and quantitative computational model for integration of different compound representations, and expected to provide new clues to improve the virtual drug screening from various pharmacological properties. Scripts, supplementary materials and data used in this study are publicly available at http://lifecenter.sgst.cn/fusion/ webcite.
A pathway profile-based method for drug repositioning
Hao Ye,LinLin Yang,ZhiWei Cao,KaiLin Tang,YiXue Li
Chinese Science Bulletin , 2012, DOI: 10.1007/s11434-012-4982-9
Abstract: Finding new applications for existing pharmaceuticals, known as drug repositioning, is a validated strategy for resolving the problem of high expenditure but low productivity in drug discovery. Currently, the prevalent computational methods for drug repositioning are focused mainly on the similarity or relevance between known drugs based on their “features”, including chemical structure, side effects, gene expression profile, and/or chemical-protein interactome. However, such drug-oriented methods may constrain the newly predicted functions to the pharmacological functional space of the existing drugs. Clinically, many drugs have been found to bind “off-target” (i.e. to receptors other than their primary targets), which can lead to undesirable effects. In this study, which integrates known drug target information, we propose a disease-oriented strategy for evaluating the relationship between drugs and disease based on their pathway profile. The basic hypothesis of this method is that drugs exerting a therapeutic effect may not only directly target the disease-related proteins but also modulate the pathways involved in the pathological process. Upon testing eight of the global best-selling drugs in 2010 (each with more than three targets), the FDA (Food and Drug Administration, USA)-approved therapeutic function of each was included in the top 10 predicted indications. On average, 60% of predicted results made using our method are proved by literature. This approach could be used to complement existing methods and may provide a new perspective in drug repositioning and side effect evaluation.
A Way For Accelerating The DNA Sequence Reconstruction Problem By CUDA
Yukun Zhong,ZhiWei He,XianHong Wang,XiongBin Cao
Computer Science , 2014,
Abstract: Traditionally, we usually utilize the method of shotgun to cut a DNA sequence into pieces and we have to reconstruct the original DNA sequence from the pieces, those are widely used method for DNA assembly. Emerging DNA sequence technologies open up more opportunities for molecular biology. This paper introduce a new method to improve the efficiency of reconstructing DNA sequence using suffix array based on CUDA programming model. The experimental result show the construction of suffix array using GPU is an more efficient approach on Intel(R) Core(TM) i3-3110K quad-core and NVIDIA GeForce 610M GPU, and study show the performance of our method is more than 20 times than that of CPU serial implementation. We believe our method give a cost-efficient solution to the bioinformatics community.
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