title: The role of visual analytics in asthma phenotyping and biomarker discovery.
abstract: The exponential growth of biomedical data related to diseases such as asthma far exceeds our cognitive abilities to comprehend it for tasks such as biomarker discovery, pathway identification, and molecular-based phenotyping. This chapter discusses the cognitive and task-based reasons for why methods from visual analytics can help in analyzing such large and complex asthma data, and demonstrates how one such approach called network visualization and analysis can be used to reveal important translational insights related to asthma. The demonstration of the method helps to identify the strengths and limitations of network analysis, in addition to areas for future research that can enhance the use of networks to analyze vast and complex biomedical datasets related to diseases such as asthma.
authors.affiliation: Institute for Translational Sciences, University of Texas Medical Branch, 6.168 Research Building 6, 301 University Blvd, Galveston, TX, USA, skbhavnani@gmail.com.
authors.forename: Suresh K
authors.initials: SK
authors.lastname: Bhavnani
authors.forename: Justin
authors.initials: J
authors.lastname: Drake
authors.forename: Rohit
authors.initials: R
authors.lastname: Divekar
mesh_terms: D000465:Algorithms; D001249:Asthma / Q000145:classification / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism; D015415:Biomarkers / Q000378:metabolism; D016000:Cluster Analysis; D003196:Computer Graphics; D016207:Cytokines / Q000235:genetics / Q000378:metabolism*; D057225:Data Mining / Q000379:methods; D020869:Gene Expression Profiling; D005838:Genotype; D006801:Humans; D016571:Neural Networks, Computer; D010641:Phenotype; D012720:Severity of Illness Index; D014584:User-Computer Interface
chemical_list: D015415:Biomarkers; D016207:Cytokines