BACKGROUND: Nowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space. RESULTS: We developed a subspace clustering algorithm called Order Preserving Triclustering (OPTricluster), for 3D short time-series data mining. OPTricluster is able to identify 3D clusters with coherent evolution from a given 3D dataset using a combinatorial approach on the sample dimension, and the order preserving (OP) concept on the time dimension. The fusion of the two methodologies allows one to study similarities and differences between samples in terms of their temporal expression profile. OPTricluster has been successfully applied to four case studies: immune response in mice infected by malaria (Plasmodium chabaudi), systemic acquired resistance in Arabidopsis thaliana, similarities and differences between inner and outer cotyledon in Brassica napus during seed development, and to Brassica napus whole seed development. These studies showed that OPTricluster is robust to noise and is able to detect the similarities and differences between biological samples. CONCLUSIONS: Our analysis showed that OPTricluster generally outperforms other well known clustering algorithms such as the TRICLUSTER, gTRICLUSTER and K-means; it is robust to noise and can effectively mine the biological knowledge hidden in the 3D short time-series gene expression data.

During the last decades, the disciplines of Data Mining and Operations Research have been working mostly independent of each other. However, the increasing complexity of today's applications in areas such as business, medicine, and science requires m

We studied defense mechanism of the Arabidopsis thaliana subjected to Salicylic Acid (SA) treatment for 0, 1, and 8 hours using a broader application of the frequent itemset approach. Four genotypes of the plant were used in this study, Columbia wild type, mutant npr1-3, double mutant tga1 tga4 and triple mutant tga2 tga5 tga6. We defined the major patterns of transcription regulation governing pathogen defense mechanism, thereby creating a model of the Systemic Acquired Resistance (SAR) at three time points. The temporal model describes the relationships among the regulators and defines groups of genes that are subject to similar regulation. The results obtained offered a first glimpse into the temporal pattern of the transcription regulatory network during SAR in Arabidopsis thaliana. We found that most of the genes that responded to SA challenge are in fact dependent on one or more of the NPR1 and TGA factors tested in this study.

BACKGROUND: Modern high throughput experimental techniques such as DNA microarrays often result in large lists of genes. Computational biology tools such as clustering are then used to group together genes based on their similarity in expression profiles. Genes in each group are probably functionally related. The functional relevance among the genes in each group is usually characterized by utilizing available biological knowledge in public databases such as Gene Ontology (GO), KEGG pathways, association between a transcription factor (TF) and its target genes, and/or gene networks. RESULTS: We developed GOAL: Gene Ontology AnaLyzer, a software tool specifically designed for the functional evaluation of gene groups. GOAL implements and supports efficient and statistically rigorous functional interpretations of gene groups through its integration with available GO, TF-gene association data, and association with KEGG pathways. In order to facilitate more specific functional characterization of a gene group, we implement three GO-tree search strategies rather than one as in most existing GO analysis tools. Furthermore, GOAL offers flexibility in deployment. It can be used as a standalone tool, a plug-in to other computational biology tools, or a web server application. CONCLUSION: We developed a functional evaluation software tool, GOAL, to perform functional characterization of a gene group. GOAL offers three GO-tree search strategies and combines its strength in function integration, portability and visualization, and its flexibility in deployment. Furthermore, GOAL can be used to evaluate and compare gene groups as the output from computational biology tools such as clustering algorithms.

Traditionally, clinical data have been used as the only source of information to diagnose diseases. Nowadays, other types of information, such as various forms of omics data (e.g. DNA microarrays), are taken into account to improve diagnosis and even

Microarray technology has been used extensively for high throughput gene expression studies. Many bioinformatics tools are available for analysis of microarray data. In the data mining process, it is important to be goal oriented so that a set of proper tools can be assembled for the targeted knowledge discovery process. In this paper, we tackle this issue by using a microarray dataset from Brassica endosperm together with EST data to validate our process. We were most interested in which genes are highly expressed in Brassica endosperm and their variations and functions over various stages in embryo development. We also performed gene characterization based on gene ontology analysis. Our results indicate that designing a specific data mining workflow that considers both the log ratio and signal intensity enhances knowledge discovery process. Through this approach, we were able to find the regulatory relationship between two most important transcription factors, LEC1 and WR11 in the endosperm of Brassica napus.

Gene ontology (GO) is organized in three principles, cellular component, biological process and molecular function. analysis of GO annotations of a list of differentially expressed genes on microarrays became a common approach in helping with their biological interpretation. Earlier studies in GO analysis are based on a single principle, mostly Biological Process; valuable information in the other two principles is neglected. This paper proposes a novel approach to investigate gene co-regulation based on GO annotations from all three principles. We used the semantic similarity of GO annotations as a measure to partition genes into functionally related clusters and developed a performance index (PI) that consolidates GO annotations from all three principles to measure the quality of each cluster. We successfully applied our algorithm to yeast dataset. Our results indicate that PI is a good measure of the likelihood of a cluster being co-regulated by one or more TFs. Another analysis based on individual GO principle indicates that gene annotations in biological process are the most informative and those in cellular component are the least informative with regard of gene co-regulation. However, none of the analyses based on an individual principle could provide satisfactory classification. It is important to consider gene annotations in all three principles

MOTIVATION: A measurement of cluster quality is needed to choose potential clusters of genes that contain biologically relevant patterns of gene expression. This is strongly desirable when a large number of gene expression profiles have to be analyzed and proper clusters of genes need to be identified for further analysis, such as the search for meaningful patterns, identification of gene functions or gene response analysis. RESULTS: We propose a new cluster quality method, called stability, by which unsupervised learning of gene expression data can be performed efficiently. The method takes into account a cluster's stability on partition. We evaluate this method and demonstrate its performance using four independent, real gene expression and three simulated datasets. We demonstrate that our method outperforms other techniques listed in the literature. The method has applications in evaluating clustering validity as well as identifying stable clusters. AVAILABILITY: Please contact the first author.

NRC publication: Yes

This paper describes the use of statistics and machine learning techniques to monitor the performance of commercial aircraft operation. The purpose of this research is to develop methods that can be used to generate reliable and timely alerts so that engineers and fleet specialists become aware of abnormal situations in a large fleet of commercial aircraft that they manage. We introduce three approaches that we have used for monitoring engines and generating alerts. We also explain how additional information can be generated from machine learning experiments so that the parameters influencing the particular abnormal situation and their ranges are also identified and reported. Various benefits of fleet monitoring are explained in the paper.

This paper first provides an overview of data preprocessing, focusing on problems of real world data. These are primarily problems that have to be carefully understood and solved before any data analysis process can start. The paper discusses in detail two main reasons for performing data preprocessing: (i) problems with the data and (ii) preparation for data analysis. The paper continues with details of data preprocessing techniques achieving each of the above mentioned objectives. A total of 14 techniques are discussed. Two examples of data preprocessing applications from two of the most data rich domains are given at the end. The applications are related to semiconductor manufacturing and aerospace domains where large amounts of data are available, and they are fairly reliable. Future directions and some challenges are discussed at the end.