Gene function finding through cross-organism ensemble learning

Background: Structured biological information about genes and proteins is a valuable resource to improve discovery and understanding of complex biological processes via machine learning algorithms. Gene Ontology (GO) controlled annotations describe, in a structured form, features and functions of genes and proteins of many organisms. However, such valuable annotations are not always reliable and sometimes are incomplete, especially for rarely studied organisms. Here, we present GeFF (Gene Function Finder), a novel cross-organism ensemble learning method able to reliably predict new GO annotations of a target organism from GO annotations of another source organism evolutionarily related and better studied. Results: Using a supervised method, GeFF predicts unknown annotations from random perturbations of existing annotations. The perturbation consists in randomly deleting a fraction of known annotations in order to produce a reduced annotation set. The key idea is to train a supervised machine learning algorithm with the reduced annotation set to predict, namely to rebuild, the original annotations. The resulting prediction model, in addition to accurately rebuilding the original known annotations for an organism from their perturbed version, also effectively predicts new unknown annotations for the organism. Moreover, the prediction model is also able to discover new unknown annotations in different target organisms without retraining.We combined our novel method with different ensemble learning approaches and compared them to each other and to an equivalent single model technique. We tested the method with five different organisms using their GO annotations: Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum. The outcomes demonstrate the effectiveness of the cross-organism ensemble approach, which can be customized with a trade-off between the desired number of predicted new annotations and their precision.A Web application to browse both input annotations used and predicted ones, choosing the ensemble prediction method to use, is publicly available at http://tiny.cc/geff/. Conclusions: Our novel cross-organism ensemble learning method provides reliable predicted novel gene annotations, i.e., functions, ranked according to an associated likelihood value. They are very valuable both to speed the annotation curation, focusing it on the prioritized new annotations predicted, and to complement known annotations available

Knowledge discovery techniques for transactional data model

In this work we give solutions to two key knowledge discovery problems for the Transactional Data model: Cluster analysis and Itemset mining. By knowledge discovery in context of these two problems, we specifically mean novel and useful ways of extracting clusters and itemsets from transactional data. Transactional Data model is widely used in a variety of applications. In cluster analysis the goal is to find clusters of similar transactions in the data with the collective properties of each cluster being unique. We propose the first clustering algorithm for transactional data which uses the latest model definition. All previously proposed algorithms did not use the important utility information in the data. Our novel technique effectively solves this problem. We also propose two new cluster validation metrics based on the criterion of high utility patterns. When comparing our technique with competing algorithms, we miss much fewer high utility patterns of importance than them. Itemset mining is the problem of searching for repeating patterns of high importance in the data. We show that the current model for itemset mining leads to information loss. It ignores the presence of clusters in the data. We propose a new itemset mining model which incorporates the cluster structure information. This allows the model to make predictions for future itemsets. We show that our model makes accurate predictions successfully, by discovering 30-40% future itemsets in most experiments on two benchmark datasets with negligible inaccuracies. There are no other present itemset prediction models, so accurate prediction is an accomplishment of ours. We provide further theoretical improvements in our model by making it capable of giving predictions for specific future windows by using time series forecasting. We also perform a detailed analysis of various clustering algorithms and study the effect of the Big Data phenomenon on them. This inspired us to further refine our model based on a classification problem design. This addition allows the mining of itemsets based on maximizing a customizable objective function made of different prediction metrics. The final framework design proposed by us is the first of its kind to make itemset predictions by using the cluster structure. It is capable of adapting the predictions to a specific future window and customizes the mining process to any specified prediction criterion. We create an implementation of the framework on a Web analytics data set, and notice that it successfully makes optimal prediction configuration choices with a high accuracy of 0.895

Knowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data Streams

The rise of the Internet of Things leads to an unprecedented number of continuous sensor observations that are available as IoT data streams. Harmonization of such observations is a labor-intensive task due to heterogeneity in format, syntax, and semantics. We aim to reduce the effort for such harmonization tasks by employing a knowledge-driven approach. To this end, we pursue the idea of exploiting the large body of formalized public knowledge represented as statements in Linked Open Data

Políticas de Copyright de Publicações Científicas em Repositórios Institucionais: O Caso do INESC TEC

A progressiva transformação das práticas científicas, impulsionada pelo desenvolvimento das novas Tecnologias de Informação e Comunicação (TIC), têm possibilitado aumentar o acesso à informação, caminhando gradualmente para uma abertura do ciclo de pesquisa. Isto permitirá resolver a longo prazo uma adversidade que se tem colocado aos investigadores, que passa pela existência de barreiras que limitam as condições de acesso, sejam estas geográficas ou financeiras. Apesar da produção científica ser dominada, maioritariamente, por grandes editoras comerciais, estando sujeita às regras por estas impostas, o Movimento do Acesso Aberto cuja primeira declaração pública, a Declaração de Budapeste (BOAI), é de 2002, vem propor alterações significativas que beneficiam os autores e os leitores. Este Movimento vem a ganhar importância em Portugal desde 2003, com a constituição do primeiro repositório institucional a nível nacional. Os repositórios institucionais surgiram como uma ferramenta de divulgação da produção científica de uma instituição, com o intuito de permitir abrir aos resultados da investigação, quer antes da publicação e do próprio processo de arbitragem (preprint), quer depois (postprint), e, consequentemente, aumentar a visibilidade do trabalho desenvolvido por um investigador e a respetiva instituição. O estudo apresentado, que passou por uma análise das políticas de copyright das publicações científicas mais relevantes do INESC TEC, permitiu não só perceber que as editoras adotam cada vez mais políticas que possibilitam o auto-arquivo das publicações em repositórios institucionais, como também que existe todo um trabalho de sensibilização a percorrer, não só para os investigadores, como para a instituição e toda a sociedade. A produção de um conjunto de recomendações, que passam pela implementação de uma política institucional que incentive o auto-arquivo das publicações desenvolvidas no âmbito institucional no repositório, serve como mote para uma maior valorização da produção científica do INESC TEC.The progressive transformation of scientific practices, driven by the development of new Information and Communication Technologies (ICT), which made it possible to increase access to information, gradually moving towards an opening of the research cycle. This opening makes it possible to resolve, in the long term, the adversity that has been placed on researchers, which involves the existence of barriers that limit access conditions, whether geographical or financial. Although large commercial publishers predominantly dominate scientific production and subject it to the rules imposed by them, the Open Access movement whose first public declaration, the Budapest Declaration (BOAI), was in 2002, proposes significant changes that benefit the authors and the readers. This Movement has gained importance in Portugal since 2003, with the constitution of the first institutional repository at the national level. Institutional repositories have emerged as a tool for disseminating the scientific production of an institution to open the results of the research, both before publication and the preprint process and postprint, increase the visibility of work done by an investigator and his or her institution. The present study, which underwent an analysis of the copyright policies of INESC TEC most relevant scientific publications, allowed not only to realize that publishers are increasingly adopting policies that make it possible to self-archive publications in institutional repositories, all the work of raising awareness, not only for researchers but also for the institution and the whole society. The production of a set of recommendations, which go through the implementation of an institutional policy that encourages the self-archiving of the publications developed in the institutional scope in the repository, serves as a motto for a greater appreciation of the scientific production of INESC TEC

The evaluation of ontologies: quality, reuse and social factors

Finding a “good” or the “right” ontology is a growing challenge in the ontology domain, where one of the main aims is to share and reuse existing semantics and knowledge. Before reusing an ontology, knowledge engineers not only have to find a set of appropriate ontologies for their search query, but they should also be able to evaluate those ontologies according to different internal and external criteria. Therefore, ontology evaluation is at the heart of ontology selection and has received a considerable amount of attention in the literature.Despite the importance of ontology evaluation and selection and the widespread research on these topics, there are still many unanswered questions and challenges when it comes to evaluating and selecting ontologies for reuse. Most of the evaluation metrics and frameworks in the literature are mainly based on a limited set of internal characteristics, e.g., content and structure of ontologies and ignore how they are used and evaluated by communities. This thesis aimed to investigate the notion of quality and reusability in the ontology domain and to explore and identify the set of metrics that can affect the process of ontology evaluation and selection for reuse. [Continues.