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Biomedical subjects

Rency S Varghese

Publications and source records attributed to Rency S Varghese.

3 recordsLinked to original sources

Enrichment of low molecular weight fraction of serum for MS analysis of peptides associated with hepatocellular carcinoma.

A challenging aspect of biomarker discovery in serum is the interference of abundant proteins with identification of disease-related proteins and peptides. This study describes enrichment of serum by denaturing ultrafiltration, which enables an efficient profiling and identification of peptides up to 5 kDa. We consistently detect several hundred peptide-peaks in MALDI-TOF and SELDI-TOF spectra of enriched serum. The sample preparation is fast and reproducible with an average CV for all 276 peaks in the MALDI-TOF spectrum of 11%. Compared to unenriched serum, the number of peaks in enriched spectra is 4 times higher at an S/N ratio of 5 and 20 times higher at an S/N ratio of 10. To demonstrate utility of the methods, we compared 20 enriched sera of patients with hepatocellular carcinoma (HCC) and 20 age-matched controls using MALDI-TOF. The comparison of 332 peaks at p < 0.001 identified 45 differentially abundant peaks that classified HCC with 90% accuracy in this small pilot study. Direct TOF/TOF sequencing of the most abundant peptide matches with high probability des-Ala-fibrinopeptide A. This study shows that enrichment of the low molecular weight fraction of serum facilitates an efficient discovery of peptides that could serve as biomarkers for detection of HCC as well as other diseases.

Amino Acid Sequence↗

Analysis of mass spectral serum profiles for biomarker selection.

MOTIVATION: Mass spectrometric profiles of peptides and proteins obtained by current technologies are characterized by complex spectra, high dimensionality and substantial noise. These characteristics generate challenges in the discovery of proteins and protein-profiles that distinguish disease states, e.g. cancer patients from healthy individuals. We present low-level methods for the processing of mass spectral data and a machine learning method that combines support vector machines, with particle swarm optimization for biomarker selection. RESULTS: The proposed method identified mass points that achieved high prediction accuracy in distinguishing liver cancer patients from healthy individuals in SELDI-QqTOF profiles of serum. AVAILABILITY: MATLAB scripts to implement the methods described in this paper are available from the HWR's lab website http://lombardi.georgetown.edu/labpage

Algorithms↗

Increasing the efficiency of fuzzy logic-based gene expression data analysis.

DNA microarray technology can accommodate a multifaceted analysis of the expression of genes in an organism. The wealth of spatiotemporal data generated by this technology allows researchers to potentially reverse engineer a particular genetic network. "Fuzzy logic" has been proposed as a method to analyze the relationships between genes and help decipher a genetic network. This method can identify interacting genes that fit a known "fuzzy" model of gene interaction by testing all combinations of gene expression profiles. This paper introduces improvements made over previous fuzzy gene regulatory models in terms of computation time and robustness to noise. Improvement in computation time is achieved by using a cluster analysis as a preprocessing method to reduce the total number of gene combinations analyzed. This approach speeds up the algorithm by a factor of 50% with minimal effect on the results. The model's sensitivity to noise is reduced by implementing appropriate methods of "fuzzy rule aggregation" and "conjunction" that produce reliable results in the face of minor changes in model input.

Animals↗