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Biomedical subjects

Hassan M Fathallah-Shaykh

Publications and source records attributed to Hassan M Fathallah-Shaykh.

8 recordsLinked to original sources
PubMed2005

Logical networks inferred from highly specific discovery of transcriptionally regulated genes predict protein states in cultured gliomas.

Cultured glioma cells are motile and invasive. The phenotype of tumor cell motility is likely created by a complex system of molecular interactions because it requires the orchestration of molecular and physical events that modify the cytoskeleton, cell membrane, extracellular matrix, and signaling. Recent reports have described an algorithm for microarray data analysis that generates highly specific genome-scale discovery; these methods identify states of differential gene expression that are true to a high degree of certainty. Here, highly specific discovery of transcriptionally regulated genes combined with logical networks inferred from the functions of known genes predicts states of protein activation, which are validated in cultured glioma cells by independent laboratories. Highly specific discovery of transcriptionally regulated genes facilitates functional genomics of complex molecular systems.

Actins↗
PubMed2005

Noise and rank-dependent geometrical filter improves sensitivity of highly specific discovery by microarrays.

SUMMARY: MASH is a mathematical algorithm that discovers highly specific states of expression from genomic profiling by microarrays. The goal at the outset of this analysis was to improve the sensitivity of MASH. The geometrical representations of microarray datasets in the 3D space are rank-dependent and unique to each dataset. The first filter (F1) of MASH defines a zone of instability whose F1-sensitive ratios have large variations. A new filter (Fs) constructs in the 3D space rank-dependent lower and upper-bound contour surfaces, which are modeled based on the geometry of the unique noise intrinsic to each dataset. As compared with MASH, Fs increases sensitivity significantly without lowering the high specificity of discovery. Fs facilitates studies in functional genomics and systems biology.

Algorithms↗
PubMed2005

Microarrays: applications and pitfalls.

Microarrays are simple assays that measure the relative expression levels of tens of thousands of genes. Excitement about their importance and potential contributions to biology and medicine has been intense. Nonetheless, recent insights into the limitations and pitfalls of microarrays have led to caution about data interpretation. Microarrays are very useful but they are also very misleading; better data analysis tools are needed to improve accuracy.

Animals↗
PubMed2005

Genomic discovery reveals a molecular system for resistance to oxidative and endoplasmic reticulum stress in cultured glioma.

Oxygen is required for respiration and the energetic processes that enable aerobic life. Reactive oxygen species and free radicals, by-products of oxygen use, cause DNA damage and induce endoplasmic reticulum (ER) stress and apoptosis. However, rapidly multiplying cancer cells are resistant to ER and oxidative stress-induced apoptosis. The present article reports the results of highly specific genome-scale expression discovery used to identify genes differentially expressed in cultured glioma cells vs normal brain tissue. The discovered states of expression reveal a cohesive molecular system that protects rapidly growing glioma cells from ER and oxidative stress-induced apoptosis.

Brain↗
PubMed2004

Mathematical algorithm for discovering states of expression from direct genetic comparison by microarrays.

Highly specific direct genome-scale expression discovery from two biological samples facilitates functional discovery of molecular systems. Here, expression data from cDNA arrays are ranked and curve-fitted. The algorithm uses filters based on the derivatives (slopes) of the curve fits. The rules are set to (i) filter the largest number of artifactual ratios from same-to-same datasets and (ii) maximize discovery from direct comparisons of different samples. The unsupervised discovery is optimized without lowering specificity. The false discovery rates are significantly lower than other methods. The discovered states of genetic expression facilitate functional discovery and are validated by real-time RT-PCR. Better quality improves sensitivity.

Algorithms↗
PubMed2003

Genomic expression discovery predicts pathways and opposing functions behind phenotypes.

Discovering states of genetic expression that are true to a high degree of certainty is likely to predict gene function behind biological phenotypes. The states of expression (up- or down-regulated) of 19200 cDNAs in 10 meningiomas are compared with normal brain by an algorithm that detects only 1 false measurement per 192000; 364 genes are discovered. The expression data accurately predict activation of signaling pathways and link gene function to specific phenotypes. Meningiomas appear to acquire aberrant phenotypes by disturbing the balanced expression of molecules that promote opposing functions. The findings expose interconnected genes and propose a role of genomic expression discovery in functional genomics of living systems.

Algorithms↗
PubMed2002

Mathematical modeling of noise and discovery of genetic expression classes in gliomas.

The microarray array experimental system generates noisy data that require validation by other experimental methods for measuring gene expression. Here we present an algebraic modeling of noise that extracts expression measurements true to a high degree of confidence. This work profiles the expression of 19 200 cDNAs in 35 human gliomas; the experiments are designed to generate four replicate spots/gene with switching of probes. The validity of the extracted measurements is confirmed by: (1) cluster analysis that generates a molecular classification differentiating glioblastoma from lower-grade tumors and radiation necrosis; (2) By what other investigators have reported in gliomas using paradigms for assaying molecular expression other than gene profiling; and (3) Real-time RT-PCR. The results yield a genetic analysis of gliomas and identify classes of genetic expression that link novel genes to the biology of gliomas.

Brain Neoplasms↗