Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships

Ontology is what makes data interesting: Interestingness framework for COVID-19 corpora

The COVID-19 pandemic has already shown to be a worldwide threat, demonstrating how susceptible humans may be. It has also inspired experts from a range of aspects and countries to find the potential solution to control the widespread. In line with this, our research proposes a novel framework for finding interesting facts from COVID-19 corpora using domain ontology. Since data mining with domain knowledge provides semantically rich facts, we use ontology in our proposed approaches. Most of the state-of-the-art methods rely on instance level or user intervention. These methods do not entirely exploit the richness of ontology. In this work, we demonstrate how to extract exciting rules from data at ontology’s schema and instance levels. Our experiments were carried out on two COVID-19 corpora that depict COVID-19 patients’ symptoms and drug information. The proposed framework outperformed the traditional methods by reducing the number of rules by 70% and generating semantic-rich rules that are more user-readable and quickly adopted by decision-makers. Furthermore, to support our claims, we compared the outcomes of the proposed framework with the most recent approach in the field. Also, statistically significant tests and domain expert evaluations are conducted to validate our framework.

Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications

Boosted by the exponential growth of microbiome-based studies, analyzing microbiome patterns is now a hot-topic, finding different fields of application. In particular, the use of machine learning techniques is increasing in microbiome studies, providing deep insights into microbial community composition. In this context, in order to investigate microbial patterns from 16S rRNA metabarcoding data, we explored the effectiveness of Association Rule Mining (ARM) technique, a supervised-machine learning procedure, to extract patterns (in this work, intended as groups of species or taxa) from microbiome data. ARM can generate huge amounts of data, making spurious information removal and visualizing results challenging. Our work sheds light on the strengths and weaknesses of pattern mining strategy into the study of microbial patterns, in particular from 16S rRNA microbiome datasets, applying ARM on real case studies and providing guidelines for future usage. Our results highlighted issues related to the type of input and the use of metadata in microbial pattern extraction, identifying the key steps that must be considered to apply ARM consciously on 16S rRNA microbiome data. To promote the use of ARM and the visualization of microbiome patterns, specifically, we developed microFIM (microbial Frequent Itemset Mining), a versatile Python tool that facilitates the use of ARM integrating common microbiome outputs, such as taxa tables. microFIM implements interest measures to remove spurious information and merges the results of ARM analysis with the common microbiome outputs, providing similar microbiome strategies that help scientists to integrate ARM in microbiome applications. With this work, we aimed at creating a bridge between microbial ecology researchers and ARM technique, making researchers aware about the strength and weaknesses of association rule mining approach.

Chromosomal Repositioning and Gene Regulation of Cells on a Micropillar Array

While various cell responses on material surfaces have been examined, relatively few reports are focused on significant self-deformation of cell nuclei and corresponding chromosomal repositioning. Herein, we prepared a micropillar array of poly(lactide-co-glycolide) (PLGA) and observed significant nuclear deformation of HeLa cells on the polymeric micropillars. In particular, we detected the territory positioning of chromosomes 18 and 19 and gene expression profiles of HeLa cells on the micropillar array using fluorescence in situ hybridization and a DNA microarray. Chromosome 18 was found to be translocated closer to the nuclear periphery than chromosome 19 on the micropillar array. With the repositioning of chromosomal territories, HeLa cells changed their gene expressions on the micropillar array with 180 genes upregulated and 255 genes downregulated for all of the 23 pairs of chromosomes under the experimental conditions and the employed Bioinformatics criteria. Hence, this work deepens the understanding on cell-material interactions by revealing that material surface topography can probably influence chromosomal repositioning in the nuclei and gene expressions of cells.

Using GO-WAR for mining cross-ontology weighted association rules

The Gene Ontology (GO) is a structured repository of concepts (GO terms) that are associated to one or more gene products. The process of association is referred to as annotation. The relevance and the specificity of both GO terms and annotations are evaluated by a measure defined as information content (IC). The analysis of annotated data is thus an important challenge for bioinformatics. There exist different approaches of analysis. From those, the use of association rules (AR) may provide useful knowledge, and it has been used in some applications, e.g. improving the quality of annotations. Nevertheless classical association rules algorithms do not take into account the source of annotation nor the importance yielding to the generation of candidate rules with low IC. This paper presents GO-WAR (Gene Ontology-based Weighted Association Rules) a methodology for extracting weighted association rules. GO-WAR can extract association rules with a high level of IC without loss of support and confidence from a dataset of annotated data. A case study on using of GO-WAR on publicly available GO annotation datasets is used to demonstrate that our method outperforms current state of the art approaches.

Methodology for the inference of gene function from phenotype data

Background

Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures.

Results

We have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function.

We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes.

Conclusions

We show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and phenotypes that would be overlooked by a semantics-based approach. Future work will include the implementation of the described algorithms for a variety of other model organism databases, taking full advantage of the abundance of available high quality curated data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0405-z) contains supplementary material, which is available to authorized users.

Mining rare associations between biological ontologies

The constantly increasing volume and complexity of available biological data requires new methods for their management and analysis. An important challenge is the integration of information from different sources in order to discover possible hidden relations between already known data. In this paper we introduce a data mining approach which relates biological ontologies by mining cross and intra-ontology pairwise generalized association rules. Its advantage is sensitivity to rare associations, for these are important for biologists. We propose a new class of interestingness measures designed for hierarchically organized rules. These measures allow one to select the most important rules and to take into account rare cases. They favor rules with an actual interestingness value that exceeds the expected value. The latter is calculated taking into account the parent rule. We demonstrate this approach by applying it to the analysis of data from Gene Ontology and GPCR databases. Our objective is to discover interesting relations between two different ontologies or parts of a single ontology. The association rules that are thus discovered can provide the user with new knowledge about underlying biological processes or help improve annotation consistency. The obtained results show that produced rules represent meaningful and quite reliable associations.

Ontology in association rules

Data mining has emerged to address the problem of transforming data into useful knowledge. Although most data mining techniques, such as the use of association rules, may substantially reduce the search effort over large data sets, often, the consequential outcomes surpass the amount of information humanly manageable. On the other hand, important association rules may be overlooked owing to the setting of the support threshold, which is a very subjective metric, but rooted in most data mining techniques. This paper presents a study on the effects, in terms of precision and recall, of using a data preparation technique, called SemPrune, which is built on domain ontology. SemPrune is intended for pre- and post-processing phases of data mining. Identifying generalization/specialization relations, as well as composition/decomposition relations, is the key to successfully applying SemPrune.