Biomedical research is one of the largest areas of present-day science and embeds the hope and potential to improve the lives of the general public. In order to understand how individual scientists choose individual research questions, we study why certain genes are well studied but others are not. While it has been previously observed that most research on human genes only concentrates on approximately 2,000 of the 19,000 genes of the human genome, the reasons for this ignorance are largely unknown. We systematically test explanations for this observation by compiling an extensive resource that characterizes biomedical research, including but not limited to hundreds of chemical and biological properties of gene-encoded proteins, and the published scientific literature on individual genes. Using machine learning methods, we can predict the number of publications on individual genes, the year of the first publication about them, the extent of funding by the National Institutes of Health, and the existence of related medical drugs. We find that biomedical research is primarily guided by a handful of generic chemical and biological characteristics of genes, which facilitated experimentation during the 1980s and 1990s, rather than the physiological importance of individual genes or their relevance to human disease.
Stoeger T, Gerlach M, Morimoto RI, Nunes Amaral LA (2018) Large-scale investigation of the reasons why potentially important genes are ignored. PLoS Biol 16(9): e2006643. https://doi.org/10.1371/journal.pbio.2006643
A new layer of complexity, constituted of networks of information token recurrence, has been identified in socio-technical systems such as the Wikipedia online community and the Zooniverse citizen science platform. The identification of this complexity reveals that our current understanding of the actual structure of those systems, and consequently the structure of the entire World Wide Web, is incomplete, which raises novel questions for data science research but also from the perspective of social epistemology. Here we establish the principled foundations and practical advantages of analyzing information diffusion within and across Web systems with Transcendental Information Cascades, and outline resulting directions for future study in the area of socio-technical systems. We also suggest that Transcendental Information Cascades may be applicable to any kind of time-evolving system that can be observed using digital technologies, and that the structures found in such systems comprise properties common to all naturally occurring complex systems.
Luczak-Roesch, M., O’Hara, K., Dinneen, J.D. et al. Minds & Machines (2018). https://doi.org/10.1007/s11023-018-9478-1
The Internet of Things equips citizens with phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance their resource consump- tion and production, while these choices have a collective system-wide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This paper envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning. This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections, accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that in- volve non-local brute-force operations or exchange full information. This paper contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic trade-offs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies.
Decentralized Collective Learning for Self-managed Sharing Economies
EVANGELOS POURNARAS, PETER PILGERSTORFER, and THOMAS ASIKIS,
Income is a primary determinant of social mobility, career progression, and personal happiness. It has been shown to vary with demographic variables like age and education, with more oblique variables such as height, and with behaviors such as delay discounting, i.e., the propensity to devalue future rewards. However, the relative contribution of each these salary-linked variables to income is not known. Further, much of past research has often been underpowered, drawn from populations of convenience, and produced findings that have not always been replicated. Here we tested a large (n = 2,564), heterogeneous sample, and employed a novel analytic approach: using three machine learning algorithms to model the relationship between income and age, gender, height, race, zip code, education, occupation, and discounting. We found that delay discounting is more predictive of income than age, ethnicity, or height. We then used a holdout data set to test the robustness of our findings. We discuss the benefits of our methodological approach, as well as possible explanations and implications for the prominent relationship between delay discounting and income.
Good Things for Those Who Wait: Predictive Modeling Highlights Importance of Delay Discounting for Income Attainment
William H. Hampton, Nima Asadi, and Ingrid R. Olson
Front. Psychol., 03 September 2018 | https://doi.org/10.3389/fpsyg.2018.01545
New Turing-universality results in Elementary Cellular Automata in recent published paper: "Rule Primality, Minimal Generating Sets and Turing-Universality in the Causal Decomposition of Elementary Cellular Automata" by Jürgen Riedel and Hector Zenil
New paper discovers and proves new universality results in ECA, namely, that the Boolean composition of ECA rules 51 and 118, and 170, 15 and 118 can emulate ECA rule 110 and are thus Turing-universal coupled systems. It is introduced a 4-colour Turing-universal cellular automaton that carries the Boolean composition of 2 and 3 ECA rules emulating ECA rule 110 under multi-scale coarse-graining. They find that rules generating the ECA rulespace by Boolean composition are of low complexity and comprise prime rules implementing basic operations that when composed enable complex behaviour. They also found a candidate minimal set with only 38 ECA prime rules — and several other small sets — capable of generating all other (non-trivially symmetric) 88 ECA rules under Boolean composition.