Ontology of core data mining entities

In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines themost essential datamining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend

Ménage à Trois: Unraveling the Mechanisms Regulating Plant–Microbe–Arthropod Interactions

Plant–microbe–arthropod (PMA) three-way interactions have important implications for plant health. However, our poor understanding of the underlying regulatory mechanisms hampers their biotechnological applications. To this end, we searched for potential common patterns in plant responses regarding taxonomic groups or lifestyles. We found that most signaling modules regulating two-way interactions also operate in three-way interactions. Furthermore, the relative contribution of signaling modules to the final plant response cannot be directly inferred from two-way interactions. Moreover, our analyses show that three-way interactions often result in the activation of additional pathways, as well as in changes in the speed or intensity of defense activation. Thus, detailed, basic knowledge of plant–microbe–arthropod regulation will be essential for the design of environmentally friendly crop management strategies. © 2020 Elsevier Lt

Ménage à Trois: Unraveling the Mechanisms Regulating Plant–Microbe–Arthropod Interactions

Plant–microbe–arthropod (PMA) three-way interactions have important implications for plant health. However, our poor understanding of the underlying regulatory mechanisms hampers their biotechnological applications. To this end, we searched for potential common patterns in plant responses regarding taxonomic groups or lifestyles. We found that most signaling modules regulating two-way interactions also operate in three-way interactions. Furthermore, the relative contribution of signaling modules to the final plant response cannot be directly inferred from two-way interactions. Moreover, our analyses show that three-way interactions often result in the activation of additional pathways, as well as in changes in the speed or intensity of defense activation. Thus, detailed, basic knowledge of plant–microbe–arthropod regulation will be essential for the design of environmentally friendly crop management strategies.This paper was inspired by scientific discussions among members of the COST Action FA1405. We thank the COST Action members that contributed to identify relevant publications. We thank Júlia Lidoy and Víctor Lidoy for drawing Figure 1. This work was financially supported by COST Action FA1405 and the Slovenian Research Agency’s research core funding No Z4-706, the Spanish Ministry of Science, Innovation and Universities (grants AGL2016-75819-C2-1-R and RTI2018-094350-B-C31), General Secreterial of Research and Technology, Greece co-financed with EU (T1- RCE-05301), and the Onassis Foundation (RZJ-003-2). A.M.M. Acknowledges funding from the program for attracting talent to Salamanca from the Fundación Salamanca Ciudad de Cultura y Saberes and Ayuntamiento de Salamanc

Network ranking assisted semantic data mining

Semantic data mining (sdm) uses annotated data and interconnected background knowledge to generate rules that are easily interpreted by the end user. However, the complexity of sdm algorithms is high, resulting in long running times even when applied to relatively small data sets. On the other hand, network analysis algorithms are among the most scalable data mining algorithms. This paper proposes an effective sdm approach that combines semantic data mining and network analysis. The proposed approach uses network analysis to extract the most relevant part of the interconnected background knowledge, and then applies a semantic data mining algorithm on the pruned background knowledge. The application on acute lymphoblastic leukemia data set demonstrates that the approach is well motivated, is more efficient and results in rules that are comparable or better than the rules obtained by applying the incorporated sdm algorithm without network reduction in data preprocessing