Biomedical subjects

Hesham H Ali

Publications and source records attributed to Hesham H Ali.

3 recordsLinked to original sources

PubMed2006

Link test--A statistical method for finding prostate cancer biomarkers.

We present a new method, link-test, to select prostate cancer biomarkers from SELDI mass spectrometry and microarray data sets. Biomarkers selected by link-test are supported by data sets from both mRNA and protein levels, and therefore results in improved robustness. Link-test determines the level of significance of the association between a microarray marker and a specific mass spectrum marker by constructing background mass spectra distributions estimated by all human protein sequences in the SWISS-PROT database. The data set consist of both microarray and mass spectrometry data from prostate cancer patients and healthy controls. A list of statistically justified prostate cancer biomarkers is reported by link-test. Cross-validation results show high prediction accuracy using the identified biomarker panel. We also employ a text-mining approach with OMIM database to validate the cancer biomarkers. The study with link-test represents one of the first cross-platform studies of cancer biomarkers.

Algorithms↗

PubMed2006

Joint learning of gene functions--a Bayesian network model approach.

In this paper, we develop a machine learning system for determining gene functions from heterogeneous data sources using a Weighted Naive Bayesian network (WNB). The knowledge of gene functions is crucial for understanding many fundamental biological mechanisms such as regulatory pathways, cell cycles and diseases. Our major goal is to accurately infer functions of putative genes or Open Reading Frames (ORFs) from existing databases using computational methods. However, this task is intrinsically difficult since the underlying biological processes represent complex interactions of multiple entities. Therefore, many functional links would be missing when only one or two sources of data are used in the prediction. Our hypothesis is that integrating evidence from multiple and complementary sources could significantly improve the prediction accuracy. In this paper, our experimental results not only suggest that the above hypothesis is valid, but also provide guidelines for using the WNB system for data collection, training and predictions. The combined training data sets contain information from gene annotations, gene expressions, clustering outputs, keyword annotations, and sequence homology from public databases. The current system is trained and tested on the genes of budding yeast Saccharomyces cerevisiae. Our WNB model can also be used to analyze the contribution of each source of information toward the prediction performance through the weight training process. The contribution analysis could potentially lead to significant scientific discovery by facilitating the interpretation and understanding of the complex relationships between biological entities.

Artificial Intelligence↗

PubMed2005

Computational approach involving use of the internal transcribed spacer 1 region for identification of Mycobacterium species.

The rapid and reliable identification of clinically significant Mycobacterium species is a challenge for diagnostic laboratories. This study evaluates a unique sequence-dependent identification algorithm called MycoAlign for the differential identification of Mycobacterium species. The MycoAlign system uses pan-Mycobacterium-specific primer amplification in combination with a customized database and algorithm. The results of testing were compared with conventional phenotypic assays and GenBank sequence comparisons using the 16S rRNA target. Discrepant results were retested and evaluated using a third independent database. The custom database was generated using the hypervariable sequences of the internal transcribed spacer 1 (ITS-1) region of the rRNA gene complex from characterized Mycobacterium species. An automated sequence-validation process was used to control quality and specificity of evaluated sequence. A total of 181 Mycobacterium strains (22 reference strains and 159 phenotypically identified clinical isolates) and seven nonmycobacterial clinical isolates were evaluated in a comparative study to validate the accuracy of the MycoAlign algorithm. MycoAlign correctly identified all referenced strains and matched species in 94% of the phenotypically identified Mycobacterium clinical isolates. The ITS-1 sequence target showed a higher degree of specificity in terms of Mycobacterium identification than the 16S rRNA sequence by use of GenBank BLAST. This study showed the MycoAlign algorithm to be a reliable and rapid approach for the identification of Mycobacterium species and confirmed the superiority of the ITS-1 region sequence over the 16S rRNA gene sequence as a target for sequence-based species identification.

Base Sequence↗