Integrative methods for analyzing big data in precision medicine

Gligorijević, Vladimir; Malod-Dognin, Noël; Pržulj, Nataša

doi:10.1002/pmic.201500396

Cited by 165 publications

(116 citation statements)

References 175 publications

(239 reference statements)

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“…The rate at which high-throughput and heterogeneous molecular and medical data are being produced forecasts a data-driven future for biology and medicine [13]. In consequence, computational biology will be one of the key players in the integration, analysis and interpretation of these complex datasets.…”

Section: Resultsmentioning

confidence: 99%

Protein Interaction Networks and Disease: Highlights of the 3rd Challenges in Computational Biology Meeting

Alanis-Lobato

Petrakis

2017

Genomics Comput Biol

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Cellular functions are managed by a complex network of protein interactions, the malfunction of which may derive in disease phenotypes.In spite of the incompleteness and noise present in our current protein interaction maps, computational biologists are making strenuous efforts to extract knowledge from these intricate networks and, through their integration with other types of biological data, expedite the development of novel and more effective treatments against human disorders.The 3rd Challenges in Computational Biology meeting revolved around the Protein Interaction Networks and Disease subject, bringing expert network biologists to the city of Mainz, Germany to debate the current status and limitations of protein interaction data and computational resources. This editorial outlines the meeting's background and programme, putting special emphasis on the extended abstracts of contributed talks collected in the present issue of Genomics and Computational Biology.

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Section: Resultsmentioning

confidence: 99%

Protein Interaction Networks and Disease: Highlights of the 3rd Challenges in Computational Biology Meeting

Alanis-Lobato

Petrakis

2017

Genomics Comput Biol

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“…The development of new experimental tools (e.g. high-throughput next-generation sequencing, mass spectrometry-based flow cytometry or real-time molecular imaging) will generate new information but, at the same time, massive amounts of (big) data that will have to be adequately handled, analysed and interpreted [112][113][114]. In this context, RIEKEBERG and POWERS [115] recently reviewed the methodological advancements and successful applications of metabolomics, one the newest omic fields.…”

Section: Technical Challengesmentioning

confidence: 99%

From systems biology to P4 medicine: applications in respiratory medicine

2018

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Human health and disease are emergent properties of a complex, nonlinear, dynamic multilevel biological system: the human body. Systems biology is a comprehensive research strategy that has the potential to understand these emergent properties holistically. It stems from advancements in medical diagnostics, "omics" data and bioinformatic computing power. It paves the way forward towards "P4 medicine" ( predictive, preventive, personalised and participatory), which seeks to better intervene preventively to preserve health or therapeutically to cure diseases. In this review, we: 1) discuss the principles of systems biology; 2) elaborate on how P4 medicine has the potential to shift healthcare from reactive medicine (treatment of illness) to predict and prevent illness, in a revolution that will be personalised in nature, probabilistic in essence and participatory driven; 3) review the current state of the art of network (systems) medicine in three prevalent respiratory diseases (chronic obstructive pulmonary disease, asthma and lung cancer); and 4) outline current challenges and future goals in the field.

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“…Biomedical big data is closely embedded with a series of national life science projects, and is also a curial concept underlying precision medicine [8,9], a initiative advocated by US President Obama in 2015 to develop tailor-made treatments based on an individual's genetic content or other molecular or cellular analysis. Under this initiative, healthcare will be profoundly affected by digital management and interventions resulted from the wide and deep application of biomedical big data.…”

Section: Scientific Significance Of Biomedical Big Datamentioning

confidence: 99%

Strategic planning for national biomedical big data infrastructure in China

Wang

Zefeng

2017

Quant. Biol.

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The promise that big data will revolutionize scientific discovery and technology innovation is now being widely recognized. With the explosive growth of biomedical data, life science is being transformed into a digital science in which novel insights are gained from in-depth data analysis and modeling. Extensive and innovative utilization of biomedical big data is a key to the success of precision medicine. Therefore, constructing a centralized national-level biomedical big data infrastructure becomes crucial and urgent for China. Such infrastructure should achieve superb capacity of safe data storage, standardized data processing and quality control, systematic data integration across multiple types, and in-depth data mining and effective data sharing. Full data chain service including information retrieval, knowledge discovery and technology support can be provided to data centers, research institutes and healthcare industries. Relying on Shanghai Institutes for Biological Sciences, agreements have been signed that a main node of the infrastructure will be located in Shanghai, and a backup node will be set up in Guizhou Province. After a construction period of five years, the infrastructure should greatly enhance China's core competence in collection, interpretation and application of biomedical big data.

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Integrative methods for analyzing big data in precision medicine

Cited by 165 publications

References 175 publications

Protein Interaction Networks and Disease: Highlights of the 3rd Challenges in Computational Biology Meeting

Protein Interaction Networks and Disease: Highlights of the 3rd Challenges in Computational Biology Meeting

From systems biology to P4 medicine: applications in respiratory medicine

Strategic planning for national biomedical big data infrastructure in China

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