oalib

OALib Journal期刊

ISSN: 2333-9721

费用：99美元

投稿

时间不限

( 2673 )

( 2672 )

( 2208 )

( 2024 )

自定义范围…

匹配条件： “Wu Fang-Xiang” ，找到相关结果约89142条。
列表显示的所有文章，均可免费获取
第1页/共89142条
每页显示 条
Sparse Representation for Classification of Tumors Using Gene Expression Data
Xiyi Hang,Fang-Xiang Wu
Journal of Biomedicine and Biotechnology , 2009, DOI: 10.1155/2009/403689
Abstract: Personalized drug design requires the classification of cancer patients as accurate as possible. With advances in genome sequencing and microarray technology, a large amount of gene expression data has been and will continuously be produced from various cancerous patients. Such cancer-alerted gene expression data allows us to classify tumors at the genomewide level. However, cancer-alerted gene expression datasets typically have much more number of genes (features) than that of samples (patients), which imposes a challenge for classification of tumors. In this paper, a new method is proposed for cancer diagnosis using gene expression data by casting the classification problem as finding sparse representations of test samples with respect to training samples. The sparse representation is computed by the 1-regularized least square method. To investigate its performance, the proposed method is applied to six tumor gene expression datasets and compared with various support vector machine (SVM) methods. The experimental results have shown that the performance of the proposed method is comparable with or better than those of SVMs. In addition, the proposed method is more efficient than SVMs as it has no need of model selection.
Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks
Xiaohua Hu, Fang-Xiang Wu
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-324
Abstract: In this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent hypotheses that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable.Experimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network.We are in the era of holistic biology. Massive amounts of biological data await interpretation. This calls for formal modeling and computational methods. In this paper, we present a method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Understanding the biomolecular network implementing some cellular function goes beyond the old dogma of "one gene: one function": only through comprehensive system understanding can we predict the impact of genetic variation in the population, design effective disease therapeutics, and evaluate the potential side-effects of therapies. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model. However, a major ch
Peptide charge state determination of tandem mass spectra from low-resolution collision induced dissociation
Shi Jinhong,Wu Fang-Xiang
Proteome Science , 2011, DOI: 10.1186/1477-5956-9-s1-s3
Abstract: Background Charge states of tandem mass spectra from low-resolution collision induced dissociation can not be determined by mass spectrometry. As a result, such spectra with multiple charges are usually searched multiple times by assuming each possible charge state. Not only does this strategy increase the overall database search time, but also yields more false positives. Hence, it is advantageous to determine charge states of such spectra before database search. Results We propose a new approach capable of determining the charge states of low-resolution tandem mass spectra. Four novel and discriminant features are introduced to describe tandem mass spectra and used in Gaussian mixture model to distinguish doubly and triply charged peptides. By testing on three independent datasets with known validity, the results have shown that this method can assign charge states to low-resolution tandem mass spectra more accurately than existing methods. Conclusions The proposed method can be used to improve the speed and reliability of peptide identification.
A feedback framework for protein inference with peptides identified from tandem mass spectra
Shi Jinhong,Wu Fang-Xiang
Proteome Science , 2012, DOI: 10.1186/1477-5956-10-68
Abstract: Background Protein inference is an important computational step in proteomics. There exists a natural nest relationship between protein inference and peptide identification, but these two steps are usually performed separately in existing methods. We believe that both peptide identification and protein inference can be improved by exploring such nest relationship. Results In this study, a feedback framework is proposed to process peptide identification reports from search engines, and an iterative method is implemented to exemplify the processing of Sequest peptide identification reports according to the framework. The iterative method is verified on two datasets with known validity of proteins and peptides, and compared with ProteinProphet and PeptideProphet. The results have shown that not only can the iterative method infer more true positive and less false positive proteins than ProteinProphet, but also identify more true positive and less false positive peptides than PeptideProphet. Conclusions The proposed iterative method implemented according to the feedback framework can unify and improve the results of peptide identification and protein inference.
Using a State-Space Model and Location Analysis to Infer Time-Delayed Regulatory Networks
Koh Chushin,Wu Fang-Xiang,Selvaraj Gopalan,Kusalik Anthony
EURASIP Journal on Bioinformatics and Systems Biology , 2009,
Abstract: Computational gene regulation models provide a means for scientists to draw biological inferences from time-course gene expression data. Based on the state-space approach, we developed a new modeling tool for inferring gene regulatory networks, called time-delayed Gene Regulatory Networks (tdGRNs). tdGRN takes time-delayed regulatory relationships into consideration when developing the model. In addition, a priori biological knowledge from genome-wide location analysis is incorporated into the structure of the gene regulatory network. tdGRN is evaluated on both an artificial dataset and a published gene expression data set. It not only determines regulatory relationships that are known to exist but also uncovers potential new ones. The results indicate that the proposed tool is effective in inferring gene regulatory relationships with time delay. tdGRN is complementary to existing methods for inferring gene regulatory networks. The novel part of the proposed tool is that it is able to infer time-delayed regulatory relationships.
Prediction of CircRNA-Disease Associations Using KATZ Model Based on Heterogeneous Networks
Chunyan Fan,Fang-Xiang Wu,Xiujuan Lei
- , 2018, DOI: 10.7150/ijbs.28260
Abstract: Circular RNAs (circRNAs) are a large group of endogenous non-coding RNAs which are key members of gene regulatory processes. Those circRNAs in human paly significant roles in health and diseases. Owing to the characteristics of their universality, specificity and stability, circRNAs are becoming an ideal class of biomarkers for disease diagnosis, treatment and prognosis. Identification of the relationships between circRNAs and diseases can help understand the complex disease mechanism. However, traditional experiments are costly and time-consuming, and little computational models have been developed to predict novel circRNA-disease associations. In this study, a heterogeneous network was constructed by employing the circRNA expression profiles, disease phenotype similarity and Gaussian interaction profile kernel similarity. Then, we developed a computational model of KATZ measures for human circRNA-disease association prediction (KATZHCDA). The leave-one-out cross validation (LOOCV) and 5-fold cross validation were implemented to investigate the effects of these four types of similarity measures. As a result, KATZHCDA model yields the AUCs of 0.8469 and 0.7936+/-0.0065 in LOOCV and 5-fold cross validation, respectively. Furthermore, we analyze the candidate association between hsa_circ_0006054 and colorectal cancer, and results showed that hsa_circ_0006054 may function as miRNA sponge in the carcinogenesis of colorectal cancer. Overall, it is anticipated that our proposed model could become an effective resource for clinical experimental guidance.
Features-Based Deisotoping Method for Tandem Mass Spectra
Zheng Yuan,Jinhong Shi,Wenjun Lin,Bolin Chen,Fang-Xiang Wu
Advances in Bioinformatics , 2011, DOI: 10.1155/2011/210805
Abstract: For high-resolution tandem mass spectra, the determination of monoisotopic masses of fragment ions plays a key role in the subsequent peptide and protein identification. In this paper, we present a new algorithm for deisotoping the bottom-up spectra. Isotopic-cluster graphs are constructed to describe the relationship between all possible isotopic clusters. Based on the relationship in isotopic-cluster graphs, each possible isotopic cluster is assessed with a score function, which is built by combining nonintensity and intensity features of fragment ions. The non-intensity features are used to prevent fragment ions with low intensity from being removed. Dynamic programming is adopted to find the highest score path with the most reliable isotopic clusters. The experimental results have shown that the average Mascot scores and -scores of identified peptides from spectra processed by our deisotoping method are greater than those by YADA and MS-Deconv software. 1. Introduction With the development of tandem mass spectrometry, it has obtained an important status in protein and peptide analysis, such as the acquisition of structure information and identification and qualitative analysis [1]. Since the fundamental data used for peptide identification in tandem mass spectra (MS/MS) is the values, charge states of fragment ions, their detection can directly influence the subsequent analysis of mass spectra including the peptide identification and quantification [2]. However, there are two difficulties during the process of detecting fragment ions: first, in some cases many real fragment ions have very low intensity that they can be removed as noise peaks by accident [3]. Numerous noisy peaks in tandem mass spectra can cause either false negative or false positive fragment ions. Second, due to the existence of heavy isotopes in nature, more than one isotopic peak for each fragment ion is resolved in high-resolution tandem mass spectra. Though isotopic peaks can provide us useful information, such as compound composition and charge states, it will cost an expensive computation if peptide identification is done without removing them. And, also, isotopic peaks can overlap that could result in wrong interpretation of masses of fragment ions. Thus, to increase the accuracy of the peptide identification and reduce the complexity of MS/MS analysis, many existing deisotoping algorithms [4–19] have already been explored to detect the isotopic clusters of fragment ions. Some of these deisotoping methods [4–10, 19] are based on the theoretical isotopic distribution matching
Using a State-Space Model and Location Analysis to Infer Time-Delayed Regulatory Networks
Chushin Koh, Fang-Xiang Wu, Gopalan Selvaraj, Anthony J Kusalik
EURASIP Journal on Bioinformatics and Systems Biology , 2009, DOI: 10.1155/2009/484601
Abstract: Microarray technology allows researchers to study expression profiles of thousands of genes simultaneously. One of the ultimate goals for measuring expression data is to reverse engineer the internal structure and function of a transcriptional regulation network that governs, for example, the development of an organism, or the response of the organism to the changes in the external environment. Some of these investigations also entail measurement of gene expression over a time course after perturbing the organism. This is usually achieved by measuring changes in gene expression levels over time in response to an initial stimulation such as environmental pressure or drug addition. The data collected from time-course experiments are subjected to cluster analysis to identify patterns of expression triggered by the perturbation [12]. A fundamental assumption is that genes sharing similar expression patterns are commonly regulated, and that the genes are involved in related biological functions. Biologists refer to this as "guilt by association." Some frequently used clustering methods for finding coregulated genes are hierarchical clustering, trajectory clustering, -means clustering, principal component analysis (PCA), and self-organizing maps (SOMs). A general review of these clustering techniques is presented by Belacel et al. [3].A gene network derived by the above clustering methods is often represented as a wiring diagram. Cluster analysis groups genes with similar time-based expression patterns (i.e., trajectories) and infers shared regulatory control of the genes. The clustering result allows one to find the part-to-part correspondences between genes. The extents of gene-gene interactions are captured by heuristic distances generated by the analysis. The network diagram produced provides insights into the underlying molecular interaction network structure.Two major limitations of conventional clustering methods are that they cannot capture the effects of regulato
Iteration method for predicting essential proteins based on orthology and protein-protein interaction networks
Wei Peng, Jianxin Wang, Weiping Wang, Qing Liu, Fang-Xiang Wu, Yi Pan
BMC Systems Biology , 2012, DOI: 10.1186/1752-0509-6-87
Abstract: By considering the facts that essential proteins are more evolutionarily conserved than nonessential proteins and essential proteins frequently bind each other, we propose an iteration method for predicting essential proteins by integrating the orthology with PPI networks, named by ION. Differently from other methods, ION identifies essential proteins depending on not only the connections between proteins but also their orthologous properties and features of their neighbors. ION is implemented to predict essential proteins in S. cerevisiae. Experimental results show that ION can achieve higher identification accuracy than eight other existing centrality methods in terms of area under the curve (AUC). Moreover, ION identifies a large amount of essential proteins which have been ignored by eight other existing centrality methods because of their low-connectivity. Many proteins ranked in top 100 by ION are both essential and belong to the complexes with certain biological functions. Furthermore, no matter how many reference organisms were selected, ION outperforms all eight other existing centrality methods. While using as many as possible reference organisms can improve the performance of ION. Additionally, ION also shows good prediction performance in E. coli K-12.The accuracy of predicting essential proteins can be improved by integrating the orthology with PPI networks.Essential proteins (also known as lethal proteins) are indispensable to life as without them the lethality or infertility is caused. Identification of essential proteins has been the pursuit of biologists for two main purposes. From the theoretical perspective, identification of essential proteins provides insight in understanding minimal requirements for cellular survival and development. It also plays a significant role in the emerging science of synthetic biology which aims to create a cell with minimal genome [1]. From the practical perspective, essential proteins are drug targets for new antibio
A novel approach to denoising ion trap tandem mass spectra
Jiarui Ding, Jinhong Shi, Guy G Poirier, Fang-Xiang Wu
Proteome Science , 2009, DOI: 10.1186/1477-5956-7-9
Abstract: We propose a novel approach to denoising tandem mass spectra. The proposed approach consists of two modules: spectral peak intensity adjustment and intensity local maximum extraction. In the spectral peak intensity adjustment module, we introduce five features to describe the quality of each peak. Based on these features, a score is calculated for each peak and is used to adjust its intensity. As a result, the intensity will be adjusted to a local maximum if a peak is a signal peak, and it will be decreased if the peak is a noisy one. The second module uses a morphological reconstruction filter to remove the peaks whose intensities are not the local maxima of the spectrum. Experiments have been conducted on two ion trap tandem mass spectral datasets: ISB and TOV. Experimental results show that our algorithm can remove about 69% of the peaks of a spectrum. At the same time, the number of spectra that can be identified by Mascot algorithm increases by 31.23% and 14.12% for the two tandem mass spectra datasets, respectively.The proposed denoising algorithm can be integrated into current popular peptide identification algorithms such as Mascot to improve the reliability of assigning peptides to spectra.The software created from this work is available upon request.Nowadays, two approaches are widely used for peptide identification from tandem mass (MS/MS) spectra: database searching and de-novo sequencing [1,2]. De-novo sequencing algorithms assign peptides to MS/MS spectra based on the spectra alone. Therefore, these algorithms are invaluable for the identification of both known and unknown peptides. However, de-novo algorithms are most useful when spectra have complete or nearly complete fragment peaks and less noisy peaks, because these algorithms rely on the presence of successive b- or y- ions to find a whole peptide sequence or a sequence tag. De-novo algorithms may find ambiguous sequence for real-world spectra because many spectra are far from complete. On the ot
第1页/共89142条
每页显示 条


Home
Copyright © 2008-2020 Open Access Library. All rights reserved.