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To address this, and to identify subgroups of related nsSNP (or for MSP1, amino acid) haplotypes that are genetically and thus potentially antigenically distinct, we also used a Bayesian clustering algorithm [26], [27] and confirmed the results using network analysis (see Materials and Methods).

Computational fluid dynamics, circuit simulation, power network analysis, and material science are just a few examples of the application areas in which large sparse linear systems need to be solved effectively.

To examine this gene network specifically in neurodevelopmental disorders, we searched human genetic data for variants within CISD2, PPT1, CLN3 and the other 19 human genes identified by network analysis in humans (supplementary material Table S6).

A coexpression network analysis was performed to obtain information about the relationships between the different miRNA (as explained in material and methods).

Complete details for the gene co-expression network analysis for each tissue and the corresponding measures of intramodular connectivity for each gene can be found in Supplementary materials (Additional file 2).

Beyond the material in [28], this paper provides a more comprehensive analysis of the competing anomaly detection techniques and variants of the specialized network analysis approach.

Artificial neural network analysis.

social network analysis.

Furthermore, network analysis using IPA analysis indicated that genes regulated by Gen represent vastly different primary networks than those regulated by E2 (see Supplemental Material, Table S2).

Gene network analysis.

Ontology and network analysis.

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