Evolutionary Mining Research Articles

Evolutionary mining and functional characterization of TnpB nucleases identify efficient miniature genome editors.

As the evolutionary ancestor of Cas12 nuclease, the transposon (IS200/IS605)-encoded TnpB proteins act as compact RNA-guided DNA endonucleases. To explore their evolutionary diversity and potential as genome editors, we screened TnpBs from 64 annotated IS605 members and identified 25 active in Escherichia coli, of which three are active in human cells. Further characterization of these 25 TnpBs enables prediction of the transposon-associated motif (TAM) and the right-end element RNA (reRNA) directly from genomic sequences. We established a framework for annotating TnpB systems in prokaryotic genomes and applied it to identify 14 additional candidates. Among these, ISAam1 (369 amino acids (aa)) and ISYmu1 (382 aa) TnpBs demonstrated robust editing activity across dozens of genomic loci in human cells. Both RNA-guided genome editors demonstrated similar editing efficiency as SaCas9 (1,053 aa) while being substantially smaller. The enormous diversity of TnpBs holds potential for the discovery of additional valuable genome editors.

Evolutionary mining of skyline clusters of attributed graph data

Graph clustering is one of the most important research topics in graph mining and network analysis. Given the abundance of data in many real-world applications, graph nodes and edges could be annotated with multiple sets of attributes that could be derived from heterogeneous data sources. The consideration of these attributes during graph clustering would facilitate the generation of graph clusters with balanced and cohesive intra-cluster structures and nodes with homogeneous properties. In this paper, we propose a graph clustering approach for mining skyline clusters over large attributed graphs based on the dominance relationship. Each skyline solution is optimized simultaneously for multiple fitness functions, each function is defined over the graph topology or over a particular set of attributes derived from multiple data sources. We evaluate our approach experimentally with a large protein-protein interaction network of the human interactome enriched with large sets of heterogeneous cancer-associated attributes. The results demonstrate the efficiency of our approach and show how integrating node attributes from multiple data sources can result in a more robust graph clustering than the consideration of the graph topology alone.

ARMGA: IDENTIFYING INTERESTING ASSOCIATION RULES WITH GENETIC ALGORITHMS

ABSTRACT Priori-like algorithms for association rules mining have relied on two user-specified thresholds: minimum support and minimum confidence. There are two significant challenges to applying these algorithms to real-world applications: database-dependent minimum-support and exponential search space. Database-dependent minimum-support means that users must specify suitable thresholds for their mining tasks though they may have no knowledge concerning their databases. To circumvent these problems, in this paper, we design an evolutionary mining strategy, namely the ARMGA model, based on a genetic algorithm. Like general genetic algorithms, our ARMGA model is effective for global searching, especially when the search space is so large that it is hardly possible to use deterministic searching method.

Adequacy of training data for evolutionary mining of trading rules

A crucial issue related to data mining on time-series is that of training period duration. The training horizon used impacts the nature of rules obtained and their predictability over time. Longer training horizons are generally sought, in order to discern sustained patterns with robust training data performance that extends well into the predictive period. However, in dynamic environments patterns that persist over time may be unavailable, and shorter-term patterns may hold higher predictive ability, albeit with shorter predictive periods. Such potentially useful shorter-term patterns may be lost when the training duration covers much longer periods. Too short a training duration can, of course, be susceptible to over-fitting to noise. We conduct experiments using different training horizons with daily-data for the S&P500 index and report the sensitivity of the performance of the obtained rules with respect to the training durations. We show that while the performance of the rules in the training period is important for inducing the “best” rules, it is not indicative of their performance in the test-period and propose alternative measures that can be used to help identify the appropriate training durations.