Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas

MicroRNA Expression Analysis: Clinical Advantage of Propranolol Reveals Key MicroRNAs in Myocardial Infarction

BACKGROUND: As playing important roles in gene regulation, microRNAs (miRNAs) are believed as indispensable involvers in the pathogenesis of myocardial infarction (MI) that causes significant morbidity and mortality. Working on a hypothesis that modulation of only some key members in the miRNA superfamily could benefit ischemic heart, we proposed a microarray based network biology approach to identify them with the recognized clinical effect of propranolol as a prompt. METHODS: A long-term MI model of rat was established in this study. The microarray technology was applied to determine the global miRNA expression change intervened by propranolol. Multiple network analyses were sequentially applied to evaluate the regulatory capacity, efficiency and emphasis of the miRNAs which dysexpression in MI were significantly reversed by propranolol. RESULTS: Microarray data analysis indicated that long-term propranolol administration caused 18 of the 31 dysregulated miRNAs in MI undergoing reversed expression, implying that intentional modulation of miRNA expression might show favorable effects for ischemic heart. Our network analysis identified that, among these miRNAs, the prime players in MI were miR-1, miR-29b and miR-98. Further finding revealed that miR-1 focused on regulation of myocyte growth, yet miR-29b and miR-98 stressed on fibrosis and inflammation, respectively. CONCLUSION: Our study illustrates how a combination of microarray technology and functional protein network analysis can be used to identify disease-related key miRNAs

Which clustering algorithm is better for predicting protein complexes?

<p>Abstract</p> <p>Background</p> <p>Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.</p> <p>Results</p> <p>In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.</p> <p>Conclusions</p> <p>While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: <url>http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm</url></p

An in silico method for detecting overlapping functional modules from composite biological networks

<p>Abstract</p> <p>Background</p> <p>The ever-increasing flow of gene expression and protein-protein interaction (PPI) data has assisted in understanding the dynamics of the cell. The detection of functional modules is the first step in deciphering the apparent modularity of biological networks. However, most network-partitioning algorithms consider only the topological aspects and ignore the underlying functional relationships.</p> <p>Results</p> <p>In the current study we integrate proteomics and microarray data of yeast, in the form of a weighted PPI graph. We partition the enriched PPI network with the novel DetMod algorithm and we identify 335 modules. One of the main advantages of DetMod is that it manages to capture the inter-module cross-talk by allowing a controlled degree of overlap among the detected modules. The obtained modules are densely connected in terms of protein interactions, while their members share up to a high degree similar biological process GO terms.</p> <p>Moreover, known protein complexes are largely incorporated in the assessed modules. Finally, we display the prevalence of our method against modules resulting from other computational approaches.</p> <p>Conclusion</p> <p>The successful integration of heterogeneous data and the concept of the proposed algorithm provide confident functional modules. We also proved that our approach is superior to methods restricted to PPI data only.</p