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author:("Sun, yixian")
1.  Online Phenotype Discovery based on Minimum Classification Error Model 
Pattern recognition  2009;42(4):509-522.
Identifying and validating novel phenotypes from images inputting online is a major challenge against high-content RNA interference (RNAi) screening. Newly discovered phenotypes should be visually distinct from existing ones and make biological sense. An online phenotype discovery method featuring adaptive phenotype modeling and iterative cluster merging using improved gap statistics is proposed. Clustering results based on compactness criteria and Gaussian mixture models (GMM) for existing phenotypes iteratively modify each other by multiple hypothesis test and model optimization based on minimum classification error (MCE). The method works well on discovering new phenotypes adaptively when applied to both of synthetic datasets and RNAi high content screen (HCS) images with ground truth labels.
PMCID: PMC2707088  PMID: 20161245
online phenotype discovery; RNA interference; high content screen; gap statistics; minimum classification error
2.  Impact of Link Unreliability and Asymmetry on the Quality of Connectivity in Large-scale Sensor Networks 
Sensors (Basel, Switzerland)  2008;8(10):6674-6691.
Connectivity is a fundamental issue in research on wireless sensor networks. However, unreliable and asymmetric links have a great impact on the global quality of connectivity (QoC). By assuming the deployment of nodes a homogeneous Poisson point process and eliminating the border effect, this paper derives an explicit expression of node non-isolation probability as the upper bound of one-connectivity, based on an analytical link model which incorporates important parameters such as path loss exponent, shadowing variance of channel, modulation, encoding method etc. The derivation has built a bridge over the local link property and the global network connectivity, which makes it clear to see how various parameter impact the QoC. Numerical results obtained further confirm the analysis and can be used as reference for practical design and simulation of wireless ad hoc and sensor networks. Besides, we find giant component size a good relaxed measure of connectivity in some applications that do not require full connectivity.
PMCID: PMC3707473
Sensor networks; connectivity; link model; node isolation probability; giant component; critical node density
3.  Cross-Layer Adaptive Feedback Scheduling of Wireless Control Systems 
Sensors (Basel, Switzerland)  2008;8(7):4265-4281.
There is a trend towards using wireless technologies in networked control systems. However, the adverse properties of the radio channels make it difficult to design and implement control systems in wireless environments. To attack the uncertainty in available communication resources in wireless control systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS) scheme is developed, which takes advantage of the co-design of control and wireless communications. By exploiting cross-layer design, CLAFS adjusts the sampling periods of control systems at the application layer based on information about deadline miss ratio and transmission rate from the physical layer. Within the framework of feedback scheduling, the control performance is maximized through controlling the deadline miss ratio. Key design parameters of the feedback scheduler are adapted to dynamic changes in the channel condition. An event-driven invocation mechanism for the feedback scheduler is also developed. Simulation results show that the proposed approach is efficient in dealing with channel capacity variations and noise interference, thus providing an enabling technology for control over WLAN.
PMCID: PMC3697173
wireless control systems; feedback scheduling; cross-layer; event-triggered
4.  Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens 
BMC Bioinformatics  2008;9:264.
The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens.
Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms.
We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens.
PMCID: PMC2443381  PMID: 18534020
5.  LQER: A Link Quality Estimation based Routing for Wireless Sensor Networks 
Sensors (Basel, Switzerland)  2008;8(2):1025-1038.
Routing protocols are crucial to self-organize wireless sensor networks (WSNs), which have been widely studied in recent years. For some specific applications, both energy aware and reliable data transmission need to be considered together. Historical link status should be captured and taken into account in making data forwarding decisions to achieve the data reliability and energy efficiency tradeoff. In this paper, a dynamic window concept (m, k) is presented to record the link historical information and a link quality estimation based routing protocol (LQER) are proposed, which integrates the approach of minimum hop field and (m, k). The performance of LQER is evaluated by extensive simulation experiments to be more energy-aware, with lower loss rate and better scalability than MHFR [1] and MCR [2]. Thus the WSNs with LQER get longer lifetime of networks and better link quality.
PMCID: PMC3927531
Wireless Sensor Networks; Energy Efficiency; Dynamic Window (m, k); Link Quality Estimation; Scalability
6.  Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks 
Sensors (Basel, Switzerland)  2007;7(12):3179-3191.
Wireless sensor/actuator networks (WSANs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSANs. In particular, quality-of-service (QoS) management in WSANs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a fuzzy logic control based QoS management (FLC-QM) scheme for WSANs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSANs. It utilizes a fuzzy logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the actuator. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The FLC-QM has the advantages of generality, scalability, and simplicity. Simulation results show that the FLC-QM can provide WSANs with QoS support.
PMCID: PMC3841889
wireless sensor; actuator network; quality of service; adaptive resource management; fuzzy logic control
7.  Novel Deployment Schemes for Mobile Sensor Networks 
Sensors (Basel, Switzerland)  2007;7(11):2907-2919.
Virtual Force Algorithm (VFA) is becoming a main solution to area coverage for homogeneous wireless sensor networks with random distribution of mobile sensor nodes. Consider the factors of the convergence, the boundary in Region Of Interest (ROI), effective distance of acting force and useless moving, etc, VFA is improved to overcome the above problems. Furthermore, an expression of exponential function for the relationship of virtual force is proposed to converge rapidly. Extensive simulation results indicate that these improved VFA get better performance in coverage rate, moving energy consumption, convergence etc. than original VFA.
PMCID: PMC3965240
Virtual Force Algorithm; Deployment; Coverage; Mobile Sensor Networks
8.  Wireless Sensor/Actuator Network Design for Mobile Control Applications 
Sensors (Basel, Switzerland)  2007;7(10):2157-2173.
Wireless sensor/actuator networks (WSANs) are emerging as a new generation of sensor networks. Serving as the backbone of control applications, WSANs will enable an unprecedented degree of distributed and mobile control. However, the unreliability of wireless communications and the real-time requirements of control applications raise great challenges for WSAN design. With emphasis on the reliability issue, this paper presents an application-level design methodology for WSANs in mobile control applications. The solution is generic in that it is independent of the underlying platforms, environment, control system models, and controller design. To capture the link quality characteristics in terms of packet loss rate, experiments are conducted on a real WSAN system. From the experimental observations, a simple yet efficient method is proposed to deal with unpredictable packet loss on actuator nodes. Trace-based simulations give promising results, which demonstrate the effectiveness of the proposed approach.
PMCID: PMC3864515
wireless sensor/actuator network; sensor network; control application; link quality; packet loss
9.  Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection 
We investigate the problems of multiclass cancer classification with gene selection from gene expression data. Two different constructed multiclass classifiers with gene selection are proposed, which are fuzzy support vector machine (FSVM) with gene selection and binary classification tree based on SVM with gene selection. Using F test and recursive feature elimination based on SVM as gene selection methods, binary classification tree based on SVM with F test, binary classification tree based on SVM with recursive feature elimination based on SVM, and FSVM with recursive feature elimination based on SVM are tested in our experiments. To accelerate computation, preselecting the strongest genes is also used. The proposed techniques are applied to analyze breast cancer data, small round blue-cell tumors, and acute leukemia data. Compared to existing multiclass cancer classifiers and binary classification tree based on SVM with F test or binary classification tree based on SVM with recursive feature elimination based on SVM mentioned in this paper, FSVM based on recursive feature elimination based on SVM can find most important genes that affect certain types of cancer with high recognition accuracy.
PMCID: PMC1184049  PMID: 16046822

Results 1-9 (9)