Changsoon Park

ESG: extended similarity group method for automated protein function prediction

MOTIVATION Importance of accurate automatic protein function prediction is ever increasing in the face of a large number of newly sequenced genomes and proteomics data that are awaiting biological interpretation. Conventional methods have focused on high sequence similarity-based annotation transfer which relies on the concept of homology. However, many cases have been reported that simple transfer of function from top hits of a homology search causes erroneous annotation. New methods are required to handle the sequence similarity in a more robust way to combine together signals from strongly and weakly similar proteins for effectively predicting function for unknown proteins with high reliability. RESULTS We present the extended similarity group (ESG) method, which performs iterative sequence database searches and annotates a query sequence with Gene Ontology terms. Each annotation is assigned with probability based on its relative similarity score with the multiple-level neighbors in the protein similarity graph. We will depict how the statistical framework of ESG improves the prediction accuracy by iteratively taking into account the neighborhood of query protein in the sequence similarity space. ESG outperforms conventional PSI-BLAST and the protein function prediction (PFP) algorithm. It is found that the iterative search is effective in capturing multiple-domains in a query protein, enabling accurately predicting several functions which originate from different domains. AVAILABILITY ESG web server is available for automated protein function prediction at http://dragon.bio.purdue.edu/ESG/.

Economic Design of a Variable Sampling Rate X̄ Chart

An economic model is developed for a variable sampling rate (VSR) Shewhart X-bar chart in which sample size and sampling interval for the next sample depend on the current sample mean. The model expresses long-run cost per hour of the VSR chart as a fu..

Economic design of a variable sample size -chart

An economic design model for an -chart which uses a variable sample size feature is developed in this paper. In a variable sample size control chart the sample size at each sampling time depends on the value of the previous sample statistic, whereas the sample size is set to be fixed constant in traditional control charts. In order to detect shifts quickly, the variable sample size chart takes a larger sample if there is any indication that the process is running in an out-of-control state and a smaller sample otherwise. for practical purposes only two possible sample sizes are considered. The objective of the econo,ic design is to find the optimal sampling interval, control limit and sample sizes to minimize the expected cost per unit operating time. The determination of the optimal design requires the computation of the averge number of samples and the average number of observations taken when the process is in control and out of control. The characteristics can be computed using the Markov chain proper...

Economic design of a variable sampling rate EWMA chart

A Variable Sampling Rate (VSR) control chart is a control chart whose sampling scheme is to vary the sampling interval and the sample size for the next sample depending on the current chart statistic. A VSR EWMA chart is an EWMA chart with the VSR sampling scheme. An economic model, which was developed for a VSR chart, is also applied here to evaluate the efficiency of the VSR EWMA chart. The properties of the VSR EWMA chart are obtained by using a Markov chain approach. The model contains cost parameters which allow the specification of the costs associated with sampling, false alarms and operating off target as well as search and repair. This economic model can be used to quantify the cost saving that can be obtained by using a VSR chart instead of a Fixed Sampling Rate (FSR) chart and can also be used to gain insight into the way that a VSR chart should be designed to achieve optimal economic performance. It is shown that with some design parameter combinations the economically optimal VSR chart has a lower false alarm rate than the FSR chart.

Nonparametric procedures for monitoring a location parameter based on linear placement statistics

Nonparametric procedures are developed for the problem of monitoring the location parameter of a continuous process wnen the control value for the parameter is not specified. These procedures are based on linear placement statistics for comparing current samples with a standard sample taken wnen the process was operating properly. The linear placement statistics are used in versions of the Shewhart and cusum charts. Asymptotic approximations to the run length distribution are obtained and a new result for the Brownian motion process is derived for the case where there is an upper bound on the time that the process is observed.

An algorithm for the properties of the integrated process control with bounded adjustments and EWMA monitoring

An integrated process control (IPC) procedure is a scheme which simultaneously applies the engineering process control (EPC) and statistical process control (SPC) techniques to reduce the variation of a process. The EPC is performed by adjusting the process, which will continually wander away from the target by its inherent disturbances. The SPC is implemented by monitoring the process, which will be changed to an undesirable state by special causes. The wandering behaviour of the process is often well-fitted by an IMA(0,1,1) process and the occurrence of a special cause is considered to change the process level. For adjusting, the bounded adjustment scheme is used and for monitoring the EWMA chart is used. The performance of the IPC procedure is evaluated in terms of the expected cost per unit interval (ECU). In designing the IPC procedure for practical use, it is essential to derive its properties constituting the ECU, but no analytical solution has been known yet. As an alternative, an algorithm for calculation of the properties is derived by using a Markov chain approach when the process is in control and a Monte Carlo simulation when out of control.

Sub-AQUA: real-value quality assessment of protein structure models

Computational protein tertiary structure prediction has made significant progress over the past years. However, most of the existing structure prediction methods are not equipped with functionality to predict accuracy of constructed models. Knowing the accuracy of a structure model is crucial for its practical use since the accuracy determines potential applications of the model. Here we have developed quality assessment methods, which predict real value of the global and local quality of protein structure models. The global quality of a model is defined as the root mean square deviation (RMSD) and the LGA score to its native structure. The local quality is defined as the distance between the corresponding Calpha positions of a model and its native structure when they are superimposed. Three regression methods are employed to combine different types of quality assessment measures of models, including alignment-level scores, residue-position level scores, atomic-detailed structure level scores and composite scores. The regression models were tested on a large benchmark data set of template-based protein structure models of various qualities. In predicting RMSD and the LGA score, a combination of two terms, length-normalized SPAD, a score that assesses alignment stability by considering suboptimal alignments, and Verify3D normalized by the square of the model length shows a significant performance, achieving 97.1 and 83.6% accuracy in identifying models with an RMSD of <2 and 6 A, respectively. For predicting the local quality of models, we find that a two-step approach, in which the global RMSD predicted in the first step is further combined with the other terms, can dramatically increase the accuracy. Finally, the developed regression equations are applied to assess the quality of structure models of whole E. coli proteome.

Estimation of the Change Point in Monitoring the Process Mean and Variance

Knowing the time of a process change could lead to quicker identification of the special cause and less process down time, as well as help to reduce the probability of incorrectly identifying the special cause. In this article, we propose the maximum likelihood estimator (MLE) for the process change point when a control chart with the fixed sampling rate (FSR) scheme or the variable sampling rate (VSR) scheme is used in monitoring a process to detect changes in the process mean and/or variance of a normal quality variable. We investigate the performance of this estimator when it is used in various types of control charts.

CUSUM charts for detecting special causes in integrated process control

This paper investigates control charts for detecting special causes in an ARIMA(0, 1, 1) process that is being adjusted automatically after each observation using a minimum mean-squared error adjustment policy. It is assumed that the adjustment mechanism is designed to compensate for the inherent variation due to the ARIMA(0, 1, 1) process, but it is desirable to detect and eliminate special causes that occur occasionally and produce additional process variation. It is assumed that these special causes can change the process mean, the process variance, the moving average parameter, or the effect of the adjustment mechanism. Expressions are derived for the process deviation from target for all of these process parameter changes. Numerical results are presented for sustained shifts, transient shifts, and sustained drifts in the process parameters. The objective is to find control charts or combinations of control charts that will be effective for detecting special causes that result in any of these types of parameter changes in any or all of the parameters. CUSUM charts designed for detecting specific parameter changes are considered. It is shown that combinations of CUSUM charts that include a CUSUM chart designed to detect mean shifts and a CUSUM chart of squared deviations from target give good overall performance in detecting a wide range of process changes. Copyright

Threading without optimizing weighting factors for scoring function

Optimizing weighting factors for a linear combination of terms in a scoring function is a crucial step for success in developing a threading algorithm. Usually weighting factors are optimized to yield the highest success rate on a training dataset, and the determined constant values for the weighting factors are used for any target sequence. Here we explore completely different approaches to handle weighting factors for a scoring function of threading. Throughout this study we use a model system of gapless threading using a scoring function with two terms combined by a weighting factor, a main chain angle potential and a residue contact potential. First, we demonstrate that the optimal weighting factor for recognizing the native structure differs from target sequence to target sequence. Then, we present three novel threading methods which circumvent training dataset‐based weighting factor optimization. The basic idea of the three methods is to employ different weighting factor values and finally select a template structure for a target sequence by examining characteristics of the distribution of scores computed by using the different weighting factor values. Interestingly, the success rate of our approaches is comparable to the conventional threading method where the weighting factor is optimized based on a training dataset. Moreover, when the size of the training set available for the conventional threading method is small, our approach often performs better. In addition, we predict a target‐specific weighting factor optimal for a target sequence by an artificial neural network from features of the target sequence. Finally, we show that our novel methods can be used to assess the confidence of prediction of a conventional threading with an optimized constant weighting factor by considering consensus prediction between them. Implication to the underlined energy landscape of protein folding is discussed. Proteins 2008.