Humanizing Big Data: Recognizing the Human Aspect of Big Data

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

Simple Summary

The influence of data incompleteness on the correctness of conclusions about the structure and functions of the objects under study is widely discussed in the literature. It was noted that even a small percentage of missing data can lead to incorrect conclusions and imperfect knowledge. In particular, incompleteness can lead to critical errors in the qualitative and quantitative assessments of interactions in biological systems and a distorted understanding of the functioning mechanisms of living systems. In this brief review, we attempt to demonstrate the extent of this incompleteness in functional information about living systems using the best-studied examples. We suggest that this incompleteness may form seemingly insurmountable barriers in deciphering the mechanisms of the functioning of complex systems with unpredictable properties arising from the interaction of the system components.

Abstract

In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5–10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.

An active human role is essential in big data-led decisions and data-intensive science

Big data is transforming many sectors, with far-reaching consequences to how decisions are made and how knowledge is produced and shared. In the current move toward more data-led decisions and data-intensive science, we aim here to examine three issues that are changing the way data are read and used. First, there is a shift toward paradigms that involve a large amount of data. In such paradigms, the creation of complex data-led models becomes tractable and appealing to generate predictions and explanations. This necessitates for instance a rethinking of Occam's razor principle in the context of knowledge discovery. Second, there is a growing erosion of the human role in decision making and knowledge discovery processes. Human users' involvement is decreasing at an alarming rate, with no say on how to read, process, and summarize data. This makes legal responsibility and accountability hard to define. Third, thanks to its increasing popularity, big data is gaining a seductive allure, where volume and complexity of big data can de facto confer more persuasion and significance to knowledge or decisions that result from big-data-based processes. These issues call for an active human role by creating opportunities to incorporate, in the most unbiased way, human expertise and prior knowledge in decision making and knowledge production. This also requires putting in place robust monitoring and appraisal mechanisms to ensure that relevant data is answering the right questions. As the proliferation of data continues to grow, we need to rethink the way we interact with data to serve human needs.