LEADER 04402oam 2200613 450 001 9910137237103321 005 20230803213740.0 035 $a(CKB)3710000000506267 035 $a(SSID)ssj0001666884 035 $a(PQKBManifestationID)16455890 035 $a(PQKBTitleCode)TC0001666884 035 $a(PQKBWorkID)15000976 035 $a(PQKB)11052260 035 $a(WaSeSS)IndRDA00056569 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/43686 035 $a(EXLCZ)993710000000506267 100 $a20160829h20142014 fy 0 101 0 $aeng 135 $aurm|#---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aComprehensive systems biomedicine$b[electronic resource] /$ftopic editors Enrico Capobianco and Pietro Lió 210 $cFrontiers Media SA$d2014 210 1$a[Lausanne, Switzerland] :$cFrontiers Media SA,$d2014. 210 4$d©2014 215 $a1 online resource (113 pages) $cillustrations; digital, PDF file(s) 225 0 $aFrontiers research topics 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a2-88919-374-8 320 $aIncludes bibliographical references. 330 $aSystems Biomedicine is a field in perpetual development. By definition a translational discipline, it emphasizes the role of quantitative systems approaches in biomedicine and aims to offer solutions to many emerging problems characterized by levels and types of complexity and uncertainty unmet before. Many factors, including technological and societal ones, need to be considered. In particular, new technologies are providing researchers with the data deluge whose management and exploitation requires a reinvention of cross-disciplinary team efforts. The advent of ?omics? and high-content imaging are examples of advances de facto establishing the necessity of systems approaches. Hypothesis-driven models and in silico validation tools in support to all the varieties of experimental applications call for a profound revision. The focus on phases like mining and assimilating the data has substantially increased so to allow for interpretable knowledge to be inferred. Notably, to be able to tackle the newly generated data dimensionality, heterogeneity and complexity, model-free and data-driven intensive applications are increasingly shaping the computational pipelines and architectures that quant specialists set aside of the high-throughput genomics, transcriptomics, proteomics platforms. As for the societal aspects, in many advanced societies health care needs now more than in the past to address the problem of managing ageing populations and their complex morbidity patterns. In parallel, there is a growing research interest on the impact that cross-disciplinary clinical, epidemiological and quantitative modelling studies can have in relation to outcomes potentially affecting the quality of life of many people. Complex systems, including those characterizing biomedicine, are assessed in both their functionality and stability, and also relatively to the capacity of generating information from diversity, variation, and complexity. Due to the combined interactions and effects, such systems embed prediction power available for instance in both target identification or marker discovery, or more generally for conducting inference about patients? pathological states, i.e. normal versus disease, diagnostic or prognostic analysis, and preventive assessment (e.g., risk evaluation). The ultimate goal, personalized medicine, will be achieved based on the confluence of the system?s predictive power to patient-specific profiling. 606 $aGenetics 606 $aBiology - General$2HILCC 606 $aBiology$2HILCC 606 $aHealth & Biological Sciences$2HILCC 610 $ainference 610 $asystems biomedicine 610 $abig data 610 $atranslational science 610 $aparadigm shift 615 0$aGenetics. 615 7$aBiology - General 615 7$aBiology 615 7$aHealth & Biological Sciences 700 $aPietro Lio$4auth$01376286 702 $aCapobianco$b Enrico 702 $aLió$b Pietro 801 0$bPQKB 801 2$bUkMaJRU 906 $aBOOK 912 $a9910137237103321 996 $aComprehensive systems biomedicine$93411844 997 $aUNINA