Fast whole-genome phylogeny of the COVID-19 virus SARS-CoV-2 by compression

We analyze the whole genome phylogeny and taxonomy of the SARS-CoV-2 virus using compression. This is a new fast alignment-free method called the “normalized compression distance” (NCD) method. It discovers all effective similarities based on Kolmogorov complexity. The latter being incomputable we approximate it by a good compressor such as the modern zpaq. The results comprise that the SARS-CoV-2 virus is closest to the RaTG13 virus and similar to two bat SARS-like coronaviruses bat-SL-CoVZXC21 and bat-SL-CoVZC4. The similarity is quantified and compared with the same quantified similarities among the mtDNA of certain species. We treat the question whether Pangolins are involved in the SARS-CoV-2 virus. The compression method is simpler and possibly faster than any other whole genome method, which makes it the ideal tool to explore phylogeny

A new quartet tree heuristic for hierarchical clustering

We present a new quartet tree heuristic for hierarchical clustering from weighted quartet topologies, and a standard manner to derive those from a given distance matrix. We do not assume that there is a true ternary tree that generated the quartet topologies or distances which we wish to recover as closely as possible. Our aim is to just model the input data as faithfully as possible by the quartet tree. Our method is capable of handling up to 60–80 objects in a matter of hours, while no existing quartet heuristic can directly compute a quartet tree of more than about 20–30 objects without running for years. The method is implemented and available as public software

Fast phylogeny of SARS-CoV-2 by compression

The compression method to assess similarity, in the sense of having a small normalized compression distance (NCD), was developed based on algorithmic information theory to quantify the similarity in files ranging from words and languages to genomes and music pieces. It has been validated on objects from different domains always using essentially the same software. We analyze the whole-genome phylogeny and taxonomy of the SARS-CoV-2 virus, which is responsible for causing the COVID-19 disease, using the alignment-free compression method to assess similarity. We compare the SARS-CoV-2 virus with a database of over 6500 viruses. The results suggest that the SARS-CoV-2 virus is closest in that database to the RaTG13 virus and rather close to the bat SARS-like coronaviruses bat-SL-CoVZXC21 and bat-SL-CoVZC45. Over 6500 viruses are identified (given by their registration code) with larger NCDs. The NCDs are compared with the NCDs between the mtDNA of familiar species. We address the question of whether pangolins are involved in the SARS-CoV-2 virus. The compression method is simpler and possibly faster than any other whole-genome method, which makes it the ideal tool to explore phylogeny. Here, we use it for the complex case of determining this similarity between the COVID-19 virus, SARS-CoV-2 and many other viruses. The resulting phylogeny and taxonomy closely resemble earlier results from by alignment-based methods and a machine-learning method, providing the most compelling evidence to date for the compression method, showing that one can achieve equivalent results both simply and quickly

Finding co-solvers on Twitter, with a little help from Linked Data

In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com

Discovery of Novel Term Associations in a Document Collection

Non peer reviewe

Semantic disambiguation and contextualisation of social tags

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-28509-7_18This manuscript is an extended version of the paper ‘cTag: Semantic Contextualisation of Social Tags’, presented at the 6th International Workshop on Semantic Adaptive Social Web (SASWeb 2011).We present an algorithmic framework to accurately and efficiently identify the semantic meanings and contexts of social tags within a particular folksonomy. The framework is used for building contextualised tag-based user and item profiles. We also present its implementation in a system called cTag, with which we preliminary analyse semantic meanings and contexts of tags belonging to Delicious and MovieLens folksonomies. The analysis includes a comparison between semantic similarities obtained for pairs of tags in Delicious folksonomy, and their semantic distances in the whole Web, according to co-occurrence based metrics computed with results of a Web search engine.This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), and Universidad Autónoma de Madrid (CCG10-UAM/TIC-5877

The Complex Dynamics of Sponsored Search Markets

This paper provides a comprehensive study of the structure and dynamics of online advertising markets, mostly based on techniques from the emergent discipline of complex systems analysis. First, we look at how the display rank of a URL link influences its click frequency, for both sponsored search and organic search. Second, we study the market structure that emerges from these queries, especially the market share distribution of different advertisers. We show that the sponsored search market is highly concentrated, with less than 5% of all advertisers receiving over 2/3 of the clicks in the market. Furthermore, we show that both the number of ad impressions and the number of clicks follow power law distributions of approximately the same coefficient. However, we find this result does not hold when studying the same distribution of clicks per rank position, which shows considerable variance, most likely due to the way advertisers divide their budget on different keywords. Finally, we turn our attention to how such sponsored search data could be used to provide decision support tools for bidding for combinations of keywords. We provide a method to visualize keywords of interest in graphical form, as well as a method to partition these graphs to obtain desirable subsets of search terms