Evidence map›Paper›PMID 26645377›Full record

ReviewJournal of neural transmission (Vienna, Austria : 1996)2017

Personalized medicine beyond genomics: alternative futures in big data-proteomics, environtome and the social proteome.

Vural Özdemir, Edward S Dove, Ulvi K Gürsoy, Semra Şardaş, Arif Yıldırım, Şenay Görücü Yılmaz, I Ömer Barlas, Kıvanç Güngör, Alper Mete, Sanjeeva Srivastava

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of neural transmission (Vienna, Austria : 1996), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
2.7field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 43 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 7 institutions in 4 countries.

Vural ÖzdemirDepartment of Public Relations, Faculty of Communications, Department of Industrial Engineering, Faculty of Engineering, and the Office of the President, International Technology and Innovation Policy, Gaziantep University, Gaziantep, 27300, Turkey. vural.ozdemir@alumni.utoronto.ca.
Edward S DoveJ. Kenyon Mason Institute for Medicine, Life Sciences and the Law, School of Law, University of Edinburgh, Old College, South Bridge, Edinburgh, EH8 9YL, UK.
Ulvi K GürsoyInstitute of Dentistry, University of Turku, Turku, Finland.
Semra ŞardaşFaculty of Pharmacy, Marmara University, Istanbul, Turkey.
Arif YıldırımDepartment of Cinema-TV, Namık Kemal University, Tekirdağ, Turkey.
Şenay Görücü YılmazFaculty of Health Sciences, Gaziantep University, Gaziantep, Turkey.
I Ömer BarlasDepartment of Medical Biology and Genetics, Faculty of Medicine, Mersin University, Mersin, Turkey.
Kıvanç GüngörFaculty of Health Sciences, Gaziantep University, Gaziantep, Turkey.
Alper MeteDepartment of Ophthalmology, Faculty of Medicine, Gaziantep University Hospital, Gaziantep, Turkey.
Sanjeeva SrivastavaDepartment of Biosciences and Bioengineering, Proteomics Laboratory, Indian Institute of Technology Bombay, Powai, Mumbai, India.
Gaziantep University · TRIndian Institute of Technology Bombay · INMarmara University · TRMersin Üniversitesi · TRNamık Kemal University · TRUniversity of Edinburgh · GBUniversity of Turku · FI

Funding

Wellcome Trust 103360
6 · The paper itself

Abstract

No field in science and medicine today remains untouched by Big Data, and psychiatry is no exception. Proteomics is a Big Data technology and a next generation biomarker, supporting novel system diagnostics and therapeutics in psychiatry. Proteomics technology is, in fact, much older than genomics and dates to the 1970s, well before the launch of the international Human Genome Project. While the genome has long been framed as the master or "elite" executive molecule in cell biology, the proteome by contrast is humble. Yet the proteome is critical for life-it ensures the daily functioning of cells and whole organisms. In short, proteins are the blue-collar workers of biology, the down-to-earth molecules that we cannot live without. Since 2010, proteomics has found renewed meaning and international attention with the launch of the Human Proteome Project and the growing interest in Big Data technologies such as proteomics. This article presents an interdisciplinary technology foresight analysis and conceptualizes the terms "environtome" and "social proteome". We define "environtome" as the entire complement of elements external to the human host, from microbiome, ambient temperature and weather conditions to government innovation policies, stock market dynamics, human values, political power and social norms that collectively shape the human host spatially and temporally. The "social proteome" is the subset of the environtome that influences the transition of proteomics technology to innovative applications in society. The social proteome encompasses, for example, new reimbursement schemes and business innovation models for proteomics diagnostics that depart from the "once-a-life-time" genotypic tests and the anticipated hype attendant to context and time sensitive proteomics tests. Building on the "nesting principle" for governance of complex systems as discussed by Elinor Ostrom, we propose here a 3-tiered organizational architecture for Big Data science such as proteomics. The proposed nested governance structure is comprised of (a) scientists, (b) ethicists, and (c) scholars in the nascent field of "ethics-of-ethics", and aims to cultivate a robust social proteome for personalized medicine. Ostrom often noted that such nested governance designs offer assurance that political power embedded in innovation processes is distributed evenly and is not concentrated disproportionately in a single overbearing stakeholder or person. We agree with this assessment and conclude by underscoring the synergistic value of social and biological proteomes to realize the full potentials of proteomics science for personalized medicine in psychiatry in the present era of Big Data.

Indexed as

Precision MedicineProteomeHumansMental DisordersProteomicsPsychiatryProteomeBig dataFutures studiesInnovation management systemsPrecision medicineProteomicsTechnology foresight

Identifiers

PMID26645377
OpenAlexW2326434742

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.