Gianvito Pio

Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach

BackgroundMicroRNAs (miRNAs) are small non-coding RNAs which play a key role in the post-transcriptional regulation of many genes. Elucidating miRNA-regulated gene networks is crucial for the understanding of mechanisms and functions of miRNAs in many biological processes, such as cell proliferation, development, differentiation and cell homeostasis, as well as in many types of human tumors. To this aim, we have recently presented the biclustering method HOCCLUS2, for the discovery of miRNA regulatory networks. Experiments on predicted interactions revealed that the statistical and biological consistency of the obtained networks is negatively affected by the poor reliability of the output of miRNA target prediction algorithms. Recently, some learning approaches have been proposed to learn to combine the outputs of distinct prediction algorithms and improve their accuracy. However, the application of classical supervised learning algorithms presents two challenges: i) the presence of only positive examples in datasets of experimentally verified interactions and ii) unbalanced number of labeled and unlabeled examples.ResultsWe present a learning algorithm that learns to combine the score returned by several prediction algorithms, by exploiting information conveyed by (only positively labeled/) validated and unlabeled examples of interactions. To face the two related challenges, we resort to a semi-supervised ensemble learning setting. Results obtained using miRTarBase as the set of labeled (positive) interactions and mirDIP as the set of unlabeled interactions show a significant improvement, over competitive approaches, in the quality of the predictions. This solution also improves the effectiveness of HOCCLUS2 in discovering biologically realistic miRNA:mRNA regulatory networks from large-scale prediction data. Using the miR-17-92 gene cluster family as a reference system and comparing results with previous experiments, we find a large increase in the number of significantly enriched biclusters in pathways, consistent with miR-17-92 functions.ConclusionThe proposed approach proves to be fundamental for the computational discovery of miRNA regulatory networks from large-scale predictions. This paves the way to the systematic application of HOCCLUS2 for a comprehensive reconstruction of all the possible multiple interactions established by miRNAs in regulating the expression of gene networks, which would be otherwise impossible to reconstruct by considering only experimentally validated interactions.

Hierarchical and overlapping co-clustering of mRNA: iRNA interactions

microRNAs (miRNAs) are an important class of regulatory factors controlling gene expressions at post-transcriptional level. Studies on interactions between different miRNAs and their target genes are of utmost importance to understand the role of miRNAs in the control of biological processes. This paper contributes to these studies by proposing a method for the extraction of co-clusters of miRNAs and messenger RNAs (mRNAs). Different from several already available co-clustering algorithms, our approach efficiently extracts a set of possibly overlapping, exhaustive and hierarchically organized co-clusters. The algorithm is well-suited for the task at hand since: i) mRNAs and miRNAs can be involved in different regulatory networks that may or may not be co-active under some conditions, ii) exhaustive co-clusters guarantee that possible co-regulations are not lost, iii) hierarchical browsing of co-clusters facilitates biologists in the interpretation of results. Results on synthetic and on real human miRNA:mRNA data show the effectiveness of the approach.

Multi-type clustering and classification from heterogeneous networks

Abstract Heterogeneous information networks consist of different types of objects and links. They can be found in several social, economic and scientific fields, ranging from the Internet to social sciences, including biology, epidemiology, geography, finance and many others. In the literature, several clustering and classification algorithms have been proposed which work on network data, but they are usually tailored for homogeneous networks, they make strong assumptions on the network structure (e.g. bi-typed networks or star-structured networks), or they assume that data are independently and identically distributed (i.i.d.). However, in real-world networks, objects can be of multiple types and several kinds of relationship can be identified among them. Moreover, objects and links in the network can be organized in an arbitrary structure where connected objects share some characteristics. This violates the i.i.d. assumption and possibly introduces autocorrelation. To overcome the limitations of existing works, in this paper we propose the algorithm HENPC, which is able to work on heterogeneous networks with an arbitrary structure. In particular, it extracts possibly overlapping and hierarchically-organized heterogeneous clusters and exploits them for predictive purposes. The different levels of the hierarchy which are discovered in the clustering step give us the opportunity to choose either more globally-based or more locally-based predictions, as well as to take into account autocorrelation phenomena at different levels of granularity. Experiments on real data show that HENPC is able to significantly outperform competitor approaches, both in terms of clustering quality and in terms of classification accuracy.

Mining Temporal Evolution of Entities in a Stream of Textual Documents

One of the recently addressed research directions focuses on the problem of mining topic evolutions from textual documents. Following this main stream of research, in this paper we face the different, but related, problem of mining the topic evolution of entities (persons, companies, etc.) mentioned in the documents. To this aim, we incrementally analyze streams of time-stamped documents in order to identify clusters of similar entities and represent their evolution over time. The proposed solution is based on the concept of temporal profiles of entities extracted at periodic instants in time. Experiments performed both on synthetic and real world datasets prove that the proposed framework is a valuable tool to discover underlying evolutions of entities and results show significant improvements over the considered baseline methods.

Identifying lncRNA-Disease Relationships via Heterogeneous Clustering

High-throughput sequencing technology led significant advances in functional genomics, giving the opportunity to pay particular attention to the role of specific biological entities. Recently, researchers focused on long non-coding RNAs (lncRNAs), i.e. transcripts that are longer than 200 nucleotides which are not transcribed into proteins. The main motivation comes from their influence on the development of human diseases. However, known relationships between lncRNAs and diseases are still poor and their in-lab validation is still expensive. In this paper, we propose a computational approach, based on heterogeneous clustering, which is able to predict possibly unknown lncRNA-disease relationships by analyzing complex heterogeneous networks consisting of several interacting biological entities of different types. The proposed method exploits overlapping and hierarchically organized heterogeneous clusters, which are able to catch multiple roles of lncRNAs and diseases at different levels of granularity. Our experimental evaluation, performed on a heterogeneous network consisting of microRNAs, lncRNAs, diseases, genes and their known relationships, shows that the proposed method is able to obtain better results with respect to existing methods.

Discovering Novelty Patterns from the Ancient Christian Inscriptions of Rome

Studying Greek and Latin cultural heritage has always been considered essential to the understanding of important aspects of the roots of current European societies. However, only a small fraction of the total production of texts from ancient Greece and Rome has survived up to the present, leaving many gaps in the historiographic records. Epigraphy, which is the study of inscriptions (epigraphs), helps to fill these gaps. In particular, the goal of epigraphy is to clarify the meanings of epigraphs; to classify their uses according to their dating and cultural contexts; and to study aspects of the writing, the writers, and their “consumers.” Although several research projects have recently been promoted for digitally storing and retrieving data and metadata about epigraphs, there has actually been no attempt to apply data mining technologies to discover previously unknown cultural aspects. In this context, we propose to exploit the temporal dimension associated with epigraphs (dating) by applying a data mining method for novelty detection. The main goal is to discover relational novelty patterns—that is, patterns expressed as logical clauses describing significant variations (in frequency) over the different epochs, in terms of relevant features such as language, writing style, and material. As a case study, we considered the set of Inscriptiones Christianae Vrbis Romae stored in Epigraphic Database Bari, an epigraphic repository. Some patterns discovered by the data mining method were easily deciphered by experts since they captured relevant cultural changes, whereas others disclosed unexpected variations, which might be used to formulate new questions, thus expanding the research opportunities in the field of epigraphy.

Network reconstruction for the identification of miRNA: RNA interaction networks

Network reconstruction from data is a data mining task which is receiving a significant attention due to its applicability in several domains. For example, it can be applied in social network analysis, where the goal is to identify connections among users and, thus, sub-communities. Another example can be found in computational biology, where the goal is to identify previously unknown relationships among biological entities and, thus, relevant interaction networks. Such task is usually solved by adopting methods for link prediction and for the identification of relevant sub-networks. Focusing on the biological domain, in [4] and [3] we proposed two methods for learning to combine the output of several link prediction algorithms and for the identification of biological significant interaction networks involving two important types of RNA molecules, i.e. microRNAs (miRNAs) and messenger RNAs (mRNAs). The relevance of this application comes from the importance of identifying (previously unknown) regulatory and cooperation activities for the understanding of the biological roles of miRNAs and mRNAs. In this paper, we review the contribution given by the combination of the proposed methods for network reconstruction and the solutions we adopt in order to meet specific challenges coming from the specific domain we consider.

EDB: Knowledge Technologies for Ancient Greek and Latin Epigraphy

Classical Greek and Latin culture is the very foundation of the identity of modern Europe. Today, a variety of modern subjects and disciplines have their roots in the classical world: from philosophy to architecture, from geometry to law. However, only a small fraction of the total production of texts from ancient Greece and Rome has survived up to the present days, leaving many ample gaps in the historiographic records. Epigraphy, which is the study of inscriptions (epigraphs), aims at plug this gap. In particular, the goal of Epigraphy is to clarify the meanings of epigraphs, classifying their uses according to dates and cultural contexts, and drawing conclusions about the writing and the writers. Indeed, they are a kind of cultural heritage for which several research projects have recently been promoted for the purposes of preservation, storage, indexing and on-line usage. In this paper, we describe the system EDB (Epigraphic Database Bari) which stores about 30,000 Christian inscriptions of Rome, including those published in the Inscriptiones Christianae Vrbis Romae septimo saeculo antiquiores, nova series editions. EDB provides, in addition to the possibility of storing metadata, the possibility of i) supporting information retrieval through a thesaurus-based query engine, ii) supporting time-based analysis of epigraphs in order to detect and represent novelties, and iii) geo-referencing epigraphs by exploiting a spatial database.

LOCANDA: Exploiting Causality in the Reconstruction of Gene Regulatory Networks

The reconstruction of gene regulatory networks via link prediction methods is receiving increasing attention due to the large availability of data, mainly produced by high throughput technologies. However, the reconstructed networks often suffer from a high amount of false positive links, which are actually the result of indirect regulation activities. Such false links are mainly due to the presence of common cause and common effect phenomena, which are typically present in gene regulatory networks. Existing methods for the identification of a transitive reduction of a network or for the removal of (possibly) redundant links suffer from limitations about the structure of the network or the nature/length of the indirect regulation, and often require additional pre-processing steps to handle specific peculiarities of the networks at hand (e.g., cycles).

Hierarchical Multidimensional Classification of Web Documents with MultiWebClass

Most of works on text categorization have focused on classifying documents into a set of categories with no relationships among them (flat classification). However, due to the intrinsic structure that can be found in many domains, recent works are focusing on more complex tasks, such as multi-label classification, hierarchical classification and multidimensional classification. In this paper, we propose the hierarchical multidimensional classification task, where documents can be classified according to different dimensions/viewpoints (e.g., topic, geographic area, time period, etc.), where in each dimension categories can be organized hierarchically. In particular, we propose the system MultiWebClass, a multidimensional variant of the system WebClassIII, which discovers correlations among categories belonging to different dimensions and exploits them, according to two different strategies, to refine the set of features used during the learning process. Experimental evaluation performed on both synthetic and real datasets confirms that the exploitation of correlations among categories can lead to better results in terms of classification accuracy, possibly reducing specialization error or generalization error, depending on the strategy adopted for the refinement of the feature sets.