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    • br miRNAs are small non coding

    2018-10-23


    miRNAs are small non-coding RNAs affecting the expression of many genes and are regarded as crucial regulators involved in the fine-tuning of all cellular processes. Organ- and disease-specific miRNAs have been described. They are not only found within nfps but also in body fluids, where they are stable and can easily be detected, e.g. by RNA-Seq or qPCR. Thus, miRNAs seem ideal biomarkers for diagnosis and prognosis of a variety of diseases, including cancer. This applies in particular to cell-free miRNAs from body fluids because they are readily accessible in a cost-effective way (see e.g., ). The study by , published in this issue of , describes a panel of miRNAs as potential biomarkers in melanoma, for diagnosing early recurrence and for estimating survival. Diagnostic miRNA biomarkers might be particularly useful as a screening tool for developing metastatic lesions and for the follow up of stage III patients. Moreover, miRNA biomarkers with predictive value could be supportive in the clinical management of melanoma: e. g. patients with a low-risk score could be spared from aggressive adjuvant treatment and expensive imaging surveillance. The authors started with the analysis of 17 miRNAs, the selection criterion being “high enrichment in melanoma”: most of the included miRNAs were expressed at least 15-fold higher in a set of 55 melanoma cell lines compared to 34 other solid cancer cell lines (). When analyzing stage III and stage IV melanoma tissues, Stark and colleagues could identify miRNAs which were predictors of tumor stage, recurrence and overall survival. A subset of 7 miRNAs (MELmiR-7 panel) was later derived, which was particularly informative when analyzing the sera of melanoma patients. Within the MELmiR-7 panel, combinations of 4 or 5 (serum) miRNAs were identified which allowed for discrimination of different disease stages with high sensitivity and specificity and, importantly, with a better diagnostic score than currently applied serological tests based on LDH and S100B.
    Collective intelligence is intelligence arising from multiple individuals, working either independently or not. Crowdsourcing is the form of group aggregate work characterised by the aggregate of the individual decisions that are made independently. A key feature is that no member to member communication exists. For humans, the independent aggregation of multiple opinions through simple averaging, majority rules or market based algorithms leads to a marked improvement in decision accuracy. This was first recognised by Francis Galton who analysed the opinions of 787 people about the weight of an ox and found combining their numerical estimates resulted in a median estimate that was remarkably near the true weight of the ox (). The key feature of this work is not only aggregate decision accuracy but also that a minority of individuals such as 2% or 4% can perform better than the group average (). There is a greater opportunity for diversity and member errors that are uncorrelated with other members\' individual errors. Another advantage is that the collective group size can be very large maximising cognitive diversity, a key element in enhancing group performance (). Even as the average amount of expertise decreases when the crowd grows, it may more than make up for it with increased diversity (). Here, Candido-do-Reis et al. report an evaluation of the test performance of the post aggregation performance of 98,293 citizen scientists who scored over 180,000 sub-images, pathology images for cancer cells, identification and oestrogen receptor status, by comparing their performance against trained pathologists (). The investigation found that very similar results were obtained for both citizen scientists and the trained pathologists. The study was well conducted although the actual task differed a little between the citizen scientists and the pathologists in that the colours were transformed for the former (). It was noted that the citizen scientist performance was not improved by weighting for user performance score (). However this has not been the case in other crowdsourcing studies. For example, a study examining the use of probabilistic coherence weighting to aggregate judgements of multiple forecasters was able to show improvements of up to 30% over the established benchmark of a simple equal weighted averaging of forecasts (). In the paper by Candido-do-Reis et al., the human only crowdsourcing post-aggregation performance was superior to the machine-learning model () and this is an area of current interest. In some settings, a hybrid of both human and machine crowdsourcing may be beneficial () and hybrid forms require further evaluation.