Networked Disease

Networked Disease

A collection of writings by leading experts and newer researchers on the SARS outbreak and its relation to infectious disease management in progressively global and urban societies. Presents original contributions by scholars from seven countries on four continents Connects newer thinking on global cities, networks, and governance in a post-national era of public health regulations and neo-liberalization of state services Provides an important contribution to the global public debate on the challenges of emerging infectious disease in cities Examines the impact of globalization on future infectious disease threats on international and local politics and culture Focuses on the ways pathogens interact with economic, political and social factors, ultimately presenting a threat to human development and global cities Employs an interdisciplinary approach to the SARS epidemic, clearly demonstrating the value of social scientific perspectives on the study of modern disease in a globalized world

Deep Learning for Biological Network Analysis

Network Bioscience, 2nd Edition

Network science has accelerated a deep and successful trend in research that influences a range of disciplines like mathematics, graph theory, physics, statistics, data science and computer science (just to name a few) and adapts the relevant techniques and insights to address relevant but disparate social, biological, technological questions. We are now in an era of 'big biological data' supported by cost-effective high-throughput genomic, transcriptomic, proteomic, metabolomic data collection techniques that allow one to take snapshots of the cells' molecular profiles in a systematic fashion. Moreover recently, also phenotypic data, data on diseases, symptoms, patients, etc. are being collected at nation-wide level thus giving us another source of highly related (causal) 'big data'. This wealth of data is usually modeled as networks (aka binary relations, graphs or webs) of interactions, (including protein-protein, metabolic, signaling and transcription-regulatory interactions). The network model is a key view point leading to the uncovering of mesoscale phenomena, thus providing an essential bridge between the observable phenotypes and 'omics' underlying mechanisms. Moreover, network analysis is a powerful 'hypothesis generation' tool guiding the scientific cycle of 'data gathering', 'data interpretation, 'hypothesis generation' and 'hypothesis testing'. A major challenge in contemporary research is the synthesis of deep insights coming from network science with the wealth of data (often noisy, contradictory, incomplete and difficult to replicate) so to answer meaningful biological questions, in a quantifiable way using static and dynamic properties of biological networks.