Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, <it>ab initio </it>approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM) are extensively adopted in these <it>ab initio </it>approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention.</p> <p>Results</p> <p>This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G²DE) based classifier. The G²DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set.</p> <p>Conclusion</p> <p>Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G²DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G²DE based predictor.</p

Integrating high dimensional bi-directional parsing models for gene mention tagging

Motivation: Tagging gene and gene product mentions in scientific text is an important initial step of literature mining. In this article, we describe in detail our gene mention tagger participated in BioCreative 2 challenge and analyze what contributes to its good performance. Our tagger is based on the conditional random fields model (CRF), the most prevailing method for the gene mention tagging task in BioCreative 2. Our tagger is interesting because it accomplished the highest F-scores among CRF-based methods and second over all. Moreover, we obtained our results by mostly applying open source packages, making it easy to duplicate our results

Deep Test to Transformers Architecture in Named Entity Recognition

Named Entity Recognition is a task of Natural Language Processing, which aims to extract and classify named entities such as ”Queen of England”. Depending on the objective of the extraction, the entities can be classified with different labels. These labels usually are Person, Organization, and Location but can be extended and include sub-entities like cars, countries, etc., or very different such as when the scope of the classification is biological, and the entities are Genes or Virus. These entities are extracted from raw text, which may be a well-structured scientific document or an internet post, and written in any language. These constraints create a considerable challenge to create an independent domain model. So, most of the authors have focused on English documents, which is the most explored language and contain more labeled data, which requires a significant amount of human resources. More recently, approaches are focused on Transformers architecture models, which may take up to days to train and consume millions of labeled entities. My approach is a statistical one, which means it will be language-independent while still requiring much computation power. This model will combine multiple techniques such as Bag of Words, Steeming, and Word2Vec to compute his features. Then, it will be compared with two transformer-based models, that although they have similar architecture, they have respectful differences. The three models will be tested in multiple datasets, each with its challenges, to conduct deep research on each model’s strengths and weaknesses. After a tough evaluation process the three models achieved performances of over 90% in datasets with high number of samples. The biggest challenge were the datasets with lower data, where the Pipeline achieved better performances than the transformer-based models.Named Entity Recognition é uma tarefa no Processamento de Língua Natural, que tem como objectivo extrair e classificar entidades como ”Rainha da Inglaterra”. Dependendo do objectivo da extração, as entidades podem ser classificadas em diferentes categorias. As categorias mais comuns são: Pessoa, Organização e Local, mas podem ser estendidas e incluir sub-entidades como carros, países, entre outros. Existem ainda categorias muito diferentes, por exemplo, quando o texto é do domínio da Biologia e as categorias são Genes ou Vírus. Essas entidades são extraídas de diferentes tipos de texto como documentos científicos estruturados corretamente ou um post da internet, podendo ser escritos em qualquer idioma. Estes constrangimentos criam um enorme desafio, sendo muito ambicioso criar um modelo independente do idioma. Acontece que a maioria dos autores está focado em documentos em inglês, uma vez que este é o idioma mais explorado e aquele que contém mais dados rotulados. Para obter estes dados são necessários recursos humanos capazes de os classificar à mão. Mais recentemente, as abordagens estão focadas em modelos de Deep Learning que podem levar dias para treinar e consomem milhões de entidades rotuladas. A minha abordagem é uma abordagem estatística, o que significa que será independente da língua, embora ainda necessite de muito poder de computação. Este modelo combinará múltiplas técnicas tais como Bag of Words, Steeming, e Word2Vec para caracterizar os dados. De seguida, será comparado com dois modelos baseados em transformers, que embora tenham uma arquitectura semelhante, têm diferenças significativas. Os três modelos serão testados em múltiplos conjuntos de dados, cada um com os seus desafios, para conduzir uma pesquisa profunda sobre os pontos fortes e fracos de cada modelo. Após uma extenso processo de avaliação os três modelos obtiveram métricas superiores a 90% em datasets com grandes quantidades de dados. O maior desafio foram os datasets com menos dados onde o Pipeline obteve métricas superiores aos modelos baseados em transformers

Ontology Enrichment from Free-text Clinical Documents: A Comparison of Alternative Approaches

While the biomedical informatics community widely acknowledges the utility of domain ontologies, there remain many barriers to their effective use. One important requirement of domain ontologies is that they achieve a high degree of coverage of the domain concepts and concept relationships. However, the development of these ontologies is typically a manual, time-consuming, and often error-prone process. Limited resources result in missing concepts and relationships, as well as difficulty in updating the ontology as domain knowledge changes. Methodologies developed in the fields of Natural Language Processing (NLP), Information Extraction (IE), Information Retrieval (IR), and Machine Learning (ML) provide techniques for automating the enrichment of ontology from free-text documents. In this dissertation, I extended these methodologies into biomedical ontology development. First, I reviewed existing methodologies and systems developed in the fields of NLP, IR, and IE, and discussed how existing methods can benefit the development of biomedical ontologies. This previously unconducted review was published in the Journal of Biomedical Informatics. Second, I compared the effectiveness of three methods from two different approaches, the symbolic (the Hearst method) and the statistical (the Church and Lin methods), using clinical free-text documents. Third, I developed a methodological framework for Ontology Learning (OL) evaluation and comparison. This framework permits evaluation of the two types of OL approaches that include three OL methods. The significance of this work is as follows: 1) The results from the comparative study showed the potential of these methods for biomedical ontology enrichment. For the two targeted domains (NCIT and RadLex), the Hearst method revealed an average of 21% and 11% new concept acceptance rates, respectively. The Lin method produced a 74% acceptance rate for NCIT; the Church method, 53%. As a result of this study (published in the Journal of Methods of Information in Medicine), many suggested candidates have been incorporated into the NCIT; 2) The evaluation framework is flexible and general enough that it can analyze the performance of ontology enrichment methods for many domains, thus expediting the process of automation and minimizing the likelihood that key concepts and relationships would be missed as domain knowledge evolves

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author