Biomedical subjects

Nabil Belacel

Publications and source records attributed to Nabil Belacel.

6 recordsLinked to original sources

PubMed2006

Data analysis of alternative splicing microarrays.

The importance of alternative splicing in drug and biomarker discovery is best understood through several example genes. For most genes, the identification, detection and particularly quantification of isoforms in different tissues and conditions remain to be carried out. As a result, the focus in drug and biomarker development is increasingly on high-throughput studies of alternative splicing. Initial strategies for the parallel analysis of alternative splicing by microarrays have been recently published. The design specificities and goals of alternative splicing microarrays, in terms of identification and quantification of multiple mRNAs from one gene, are promoting the development of novel methods of analysis.

Algorithms↗

PubMed2006

Microarray analysis of alternative splicing.

Alternative splicing, defined as the generation of multiple RNA transcript species from a common mRNA precursor, is one of the mechanisms for the diversification and expansion of cellular proteins from a smaller set of genes. Current estimates indicate that at least 60% of genes in the human genome exhibit alternative splicing. Over the past decade, alternative splicing has increasingly been recognized as a major regulatory process with a critical role in normal development. Furthermore, the importance of alternative splicing in disease development and treatment is starting to be appreciated. Therefore, an increasing number of high-throughput genomics and proteomics studies are being performed in order to delineate (a) the changes in alternative splicing under various conditions; (b) the properties and functions of protein isoforms; and (c) the splicing and alternative splicing regulation process. Strategies for the parallel analysis of alternative splice forms by microarray experiments have been conceived, and examples have been published. In addition to the differences in microarray probe design, the analysis of microarrays with probes for exons, exon/exon junctions as well as specific splice forms is significantly different from the standard experiment. Several methods are being developed in order to address the particular needs of alternative splicing microarrays. Many reviews have already dealt with alternative splicing. However, high-throughput analysis methods that are becoming increasingly popular have not received much attention. Here, we will provide an overview of the tools and analysis methods that were developed specifically for alternative splicing microarrays described in terms of specific experiments.

Alternative Splicing↗

PubMed2006

Clustering methods for microarray gene expression data.

Within the field of genomics, microarray technologies have become a powerful technique for simultaneously monitoring the expression patterns of thousands of genes under different sets of conditions. A main task now is to propose analytical methods to identify groups of genes that manifest similar expression patterns and are activated by similar conditions. The corresponding analysis problem is to cluster multi-condition gene expression data. The purpose of this paper is to present a general view of clustering techniques used in microarray gene expression data analysis.

Animals↗

PubMed2005

Determination of tumour marker genes from gene expression data.

Cancer classification has traditionally been based on the morphological study of tumours. However, tumours with similar histological appearances can exhibit different responses to therapy, indicating differences in tumour characteristics on the molecular level. Thus, development of a novel, reliable and precise method for classification of tumours is essential for more successful diagnosis and treatment. The high-throughput gene expression data obtained using microarray technology are currently being investigated for diagnostic applications. However, these large datasets introduce a range of challenges, making data analysis a major part of every experiment for any application, including cancer classification and diagnosis. One of the major concerns in the application of microarrays to tumour diagnostics is the fact that the expression levels of many genes are not measurably affected by carcinogenic changes in the cells. Thus, a crucial step in the application of microarrays to cancer diagnostics is the selection of diagnostic marker genes from the gene expression profiles. These molecular markers give valuable additional information for tumour diagnosis, prognosis and therapy development.

Biomarkers, Tumor↗

PubMed2005

Web-integration PROAFTN methodology for acute leukemia diagnosis.

OBJECTIVE: To develop and test a web-based Clinical Decision Support System (CDSS) tool, which integrated a new fuzzy multiple criteria classification methodology named PROAFTN in acute leukemia (AL) diagnosis. METHODS: We have integrated a PROAFTN method and developed a web-based clinical decision support system using standard JSP, servlets, and XML technologies. All website data are database-driven; and the database system can handle data store, update, and retrieval instantly. Since the system was moved to a web server, we have started our experimental testing on 191 AL cases. RESULTS: The percentage of correct classification in this experimental testing was consistent with the proposed prototype. 96.4% of AL cases were correctly classified, proving that web-integration can be a promising tool for dissemination of CDSS tools. We found our system to be robust and capable of deployment for referring physicians. CONCLUSIONS: Our experimental results suggest that the Internet has promise as a means for distribution of CDSS tools. This system will help to: 1) make a "virtual" diagnosis and to compare its performances with given clinical diagnosis; 2) exchange health information between physicians and hematologists at the location and time of need; 3) assist online learning and simulate cases for training practitioners; 4) implement a strict security and access control for transmission of electronic health data through the Internet. The method will not replace specialists, but was developed to assist biologist-hematologists and general practitioners remotely in making decisions on medical diagnosis.

Acute Disease↗

PubMed2004

Fuzzy J-Means and VNS methods for clustering genes from microarray data.

MOTIVATION: In the interpretation of gene expression data from a group of microarray experiments that include samples from either different patients or conditions, special consideration must be given to the pleiotropic and epistatic roles of genes, as observed in the variation of gene coexpression patterns. Crisp clustering methods assign each gene to one cluster, thereby omitting information about the multiple roles of genes. RESULTS: Here, we present the application of a local search heuristic, Fuzzy J-Means, embedded into the variable neighborhood search metaheuristic for the clustering of microarray gene expression data. We show that for all the datasets studied this algorithm outperforms the standard Fuzzy C-Means heuristic. Different methods for the utilization of cluster membership information in determining gene coregulation are presented. The clustering and data analyses were performed on simulated datasets as well as experimental cDNA microarray data for breast cancer and human blood from the Stanford Microarray Database. AVAILABILITY: The source code of the clustering software (C programming language) is freely available from Nabil.Belacel@nrc-cnrc.gc.ca

Algorithms↗