Biomedical subjects

Fang-Xiang Wu

Publications and source records attributed to Fang-Xiang Wu.

4 recordsLinked to original sources

PubMed2006

Determination of the minimum number of microarray experiments for discovery of gene expression patterns.

BACKGROUND: One type of DNA microarray experiment is discovery of gene expression patterns for a cell line undergoing a biological process over a series of time points. Two important issues with such an experiment are the number of time points, and the interval between them. In the absence of biological knowledge regarding appropriate values, it is natural to question whether the behaviour of progressively generated data may by itself determine a threshold beyond which further microarray experiments do not contribute to pattern discovery. Additionally, such a threshold implies a minimum number of microarray experiments, which is important given the cost of these experiments. RESULTS: We have developed a method for determining the minimum number of microarray experiments (i.e. time points) for temporal gene expression, assuming that the span between time points is given and the hierarchical clustering technique is used for gene expression pattern discovery. The key idea is a similarity measure for two clusterings which is expressed as a function of the data for progressive time points. While the experiments are underway, this function is evaluated. When the function reaches its maximum, it indicates the set of experiments reach a saturated state. Therefore, further experiments do not contribute to the discrimination of patterns. CONCLUSION: The method has been verified with two previously published gene expression datasets. For both experiments, the number of time points determined with our method is less than in the published experiments. It is noted that the overall approach is applicable to other clustering techniques.

Algorithms↗

PubMed2006

RT-PSM, a real-time program for peptide-spectrum matching with statistical significance.

The analysis of complex biological peptide mixtures by tandem mass spectrometry (MS/MS) produces a huge body of collision-induced dissociation (CID) MS/MS spectra. Several methods have been developed for identifying peptide-spectrum matches (PSMs) by assigning MS/MS spectra to peptides in a database. However, most of these methods either do not give the statistical significance of PSMs (e.g., SEQUEST) or employ time-consuming computational methods to estimate the statistical significance (e.g., PeptideProphet). In this paper, we describe a new algorithm, RT-PSM, which can be used to identify PSMs and estimate their accuracy statistically in real time. RT-PSM first computes PSM scores between an MS/MS spectrum and a set of candidate peptides whose masses are within a preset tolerance of the MS/MS precursor ion mass. Then the computed PSM scores of all candidate peptides are employed to fit the expectation value distribution of the scores into a second-degree polynomial function in PSM score. The statistical significance of the best PSM is estimated by extrapolating the fitting polynomial function to the best PSM score. RT-PSM was tested on two pairs of MS/MS spectrum datasets and protein databases to investigate its performance. The MS/MS spectra were acquired using an ion trap mass spectrometer equipped with a nano-electrospray ionization source. The results show that RT-PSM has good sensitivity and specificity. Using a 55,577-entry protein database and running on a standard Pentium-4, 2.8-GHz CPU personal computer, RT-PSM can process peptide spectra on a sequential, one-by-one basis in 0.047 s on average, compared to more than 7 s per spectrum on average for Sequest and X!Tandem, in their current batch-mode processing implementations. RT-PSM is clearly shown to be fast enough for real-time PSM assignment of MS/MS spectra generated every 3 s or so by a 3D ion trap or by a QqTOF instrument.

Algorithms↗

PubMed2006

Optimally-connected hidden markov models for predicting MHC-binding peptides.

Hidden Markov models (HMMs) are one of various methods that have been applied to prediction of major histo-compatibility complex (MHC) binding peptide. In terms of model topology, a fully-connected HMM (fcHMM) has the greatest potential to predict binders, at the cost of intensive computation. While a profile HMM (pHMM) performs dramatically fewer computations, it potentially merges overlapping patterns into one which results in some patterns being missed. In a profile HMM a state corresponds to a position on a peptide while in an fcHMM a state has no specific biological meaning. This work proposes optimally-connected HMMs (ocHMMs), which do not merge overlapping patterns and yet, by performing topological reductions, a model's connectivity is greatly reduced from an fcHMM. The parameters of ocHMMs are initialized using a novel amino acid grouping approach called "multiple property grouping." Each group represents a state in an ocHMM. The proposed ocHMMs are compared to a pHMM implementation using HMMER, based on performance tests on two MHC alleles HLA (Human Leukocyte Antigen)-A*0201 and HLA-B*3501. The results show that the heuristic approaches can be adjusted to make an ocHMM achieve higher predictive accuracy than HMMER. Hence, such obtained ocHMMs are worthy of trial for predicting MHC-binding peptides.

Amino Acid Sequence↗

PubMed2005

Dynamic model-based clustering for time-course gene expression data.

Microarray technology has produced a huge body of time-course gene expression data. Such gene expression data has proved useful in genomic disease diagnosis and genomic drug design. The challenge is how to uncover useful information in such data. Cluster analysis has played an important role in analyzing gene expression data. Many distance/correlation- and static model-based clustering techniques have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterize the data and that should be considered in cluster analysis so as to obtain high quality clustering. This paper proposes a dynamic model-based clustering method for time-course gene expression data. The proposed method regards a time-course gene expression dataset as a set of time series, generated by a number of stochastic processes. Each stochastic process defines a cluster and is described by an autoregressive model. A relocation-iteration algorithm is proposed to identity the model parameters and posterior probabilities are employed to assign each gene to an appropriate cluster. A bootstrapping method and an average adjusted Rand index (AARI) are employed to measure the quality of clustering. Computational experiments are performed on a synthetic and three real time-course gene expression datasets to investigate the proposed method. The results show that our method allows the better quality clustering than other clustering methods (e.g. k-means) for time-course gene expression data, and thus it is a useful and powerful tool for analyzing time-course gene expression data.

Algorithms↗