Academic literature on the topic 'Automated curation'

In this work we create an image analysis pipeline to segment cells from microscopy image data. A portion of the segmented images are manually curated and this curated data is used to train a Curator network to filter the whole dataset. The curated data is used to train a separate segmentation network to improve the cell segmentation. This technique can be easily applied to different types of microscopy object segmentation.

Mestrado em Engenharia de Computadores e Telemática<br>O aumento acelerado da literatura biomédica levou ao desenvolvimento de vários esforços para extrair e armazenar, de forma estruturada, a informação relativa aos conceitos e relações presentes nesses textos, oferecendo aos investigadores e clínicos um acesso rápido e fácil à informação. No entanto, este processo de "curadoria de conhecimento" é uma tarefa extremamente exaustiva, sendo cada vez mais comum o uso de ferramentas de anotação automática, fazendo uso de técnicas de mineração de texto. Apesar de já existirem sistemas de anotação bastante completos e que apresentam um alto desempenho, estes não são largamente usados pela comunidade biomédica, principalmente por serem complexos e apresentarem limitações ao nível de usabilidade. Por outro lado, o PDF tornou-se nos últimos anos num dos formatos mais populares para publicar e partilhar documentos visto poder ser apresentado exatamente da mesma maneira independentemente do sistema ou plataforma em que é acedido. A maioria das ferramentas de anotação foram principalmente desenhadas para extrair informação de texto livre, contudo hoje em dia uma grande parte da literatura biomédica é publicada e distribuída em PDF, e portanto a extração de informação de documentos PDF deve ser um ponto de foco para a comunidade de mineração de texto biomédico. O objetivo do trabalho descrito nesta dissertação foi a extensão da framework Neji, permitindo o processamento de documentos em formato PDF, e a integração dessas funcionalidades na plataforma Egas, permitindo que um utilizador possa visualizar e anotar, simultaneamente, o artigo original no formato PDF e o texto extraído deste. Os sistemas desenvolvidos apresentam bons resultados de desempenho, tanto em termos de velocidade de processamento como de representação da informação, o que também contribui para uma melhor experiência de utilizador. Além disso, apresentam várias vantagens para a comunidade de mineração de texto e curadores, permitindo a anotação direta de artigos no formato PDF e simplificando o uso e configuração destes sistemas de anotação por parte de investigadores.<br>The accelerated increase of the biomedical literature has led to various efforts to extract and store, in a structured way, the information related with the concepts and relations presented in those texts, providing to investigators and researchers a fast and easy access to knowledge. However, this process of “knowledge curation” is an extremely exhaustive task, being more and more common demanding the application of automatic annotation tools, that make use of text mining techniques. Even thought complete annotation systems already exist and produce high performance results, they are not widely used by the biomedical community, mainly because of their complexity and also due to some limitations in usability. On the other hand, the PDF has become in the last years one of the most popular formats for publishing and sharing documents because of it can be displayed exactly in the same way independently of the system or platform where it is accessed. The majority of annotation tools were mainly designed to extract information from raw text, although a big part of the biomedical literature is published and distributed in PDF, and thus the information extraction from PDF documents should be a focus point for the biomedical text mining community. The objective of the work described in this document is the extension of Neji framework, allowing the processing of documents in PDF format, and the integration of these features in Egas platform, allowing a user to simultaneously visualize the original article in PDF format and its extracted text. The improved and developed systems present good performing results, both in terms of processing speed and representation of the information, contributing also for a better user experience. Besides that, they present several advantages for the biomedical community, allowing the direct annotation of PDF articles and simplifying the use and configuration of these annotation systems by researchers.

Non-coding RNAs are the most abundant class of RNAs found throughout genomes. These RNAs are key players of gene regulation and thus, the func- tion of whole organisms. Numerous methods have been developed so far for detecting novel classes of ncRNAs or finding homologs to the known ones. Because of their abundance, the sequence availability of these RNAs is rapidly increasing, as is the case for example for microRNAs. However, for classes of them, still only incomplete information is available, invertebrates 7SK snRNA for instance. Consequently, a lot of false positive outputs are produced in the former case, and more accurate annotation methods are needed for the latter cases to improve derivable knowledge. This makes the accuracy of gathering correct homologs a challenging task and it leads directly to a not less important problem, the curation of these data. Finding solutions for the aforementioned problems is more complex than one would expect as these RNAs are characterized not only by sequences informa- tion but also structure information, in addition to distinct biological features. In this work, data curation methods and sensitive homology search are shown as complementary methods to solve these problems. A careful curation and annotation method revealed new structural information in the invertebrates 7SK snRNA, which pushes the investigation in the area forward. This has been reflected by detecting new high potential 7SK RNA genes in different invertebrates groups. Moreover, the gaps between homology search and well- curated data on the one side, and between experimental and computational outputs on the other side, are closed. These gaps were bridged by a curation method applied to the microRNA data, which was then turned into a com- prehensive workflow implemented into an automated pipeline. MIRfix is a microRNA curation pipeline considering the detailed sequence and structure information of the metazoan microRNAs, together with biological features related to the microRNA biogenesis. Moreover, this pipeline can be integrated into existing methods and tools related to microRNA homology search and data curation. The application of this pipeline on the biggest open source microRNA database revealed its high capacity in detecting wrong annotated pre-miRNA, eventually improving alignment quality of the majority of the available data. Additionally, it was tested with artificial datasets highlighting the high accuracy in predicting the pre-miRNA components, miRNA and miRNA*.:Chapter 1: Introduction Chapter 2: Biological and Computational background 2.1 Biology 2.1.1 Non-coding RNAs 2.1.2 RNA secondary structure 2.1.3 Homology versus similarity 2.1.4 Evolution 2.2 The role of computational biology 2.2.1 Alignment 2.2.1.1 Pairwise alignment 2.2.1.2 Multiple sequence alignment (MSA) 2.2.2 Homology search 2.2.2.1 Sequence-based 2.2.2.2 Structure-based 2.2.3 RNA secondary structure prediction Chapter 3: Careful curation for snRNA 3.1 Biological background 3.2 Introduction to the problem 3.3 Methods 3.3.1 Initial seeds and models construction 3.3.2 Models anatomy then merging 3.4 Results 3.4.1 Refined model of arthropod 7SK RNA 3.4.1.1 5’ Stem 3.4.1.2 Extension of Stem A 3.4.1.3 Novel stem B in invertebrates 3.4.1.4 3’ Stem 3.4.2 Invertebrates model conserves the HEXIM1 binding site 3.4.3 Computationally high potential 7SK RNA candidate . 3.4.4 Sensitivity of the final proposed model 3.5 Conclusion Chapter 4: Behind the scenes of microRNA driven regulation 4.1 Biological background 4.2 Databases and problems 4.3 MicroRNA detection and curation approaches Chapter 5: Initial microRNA curation 5.1 Introduction 5.2 Methods 5.2.1 Data pre-processing 5.2.2 Initial seeds creation 5.2.3 Main course 5.3 Results and discussion 5.4 Conclusion Chapter 6: MIRfix pipeline 6.1 Introduction 6.2 Methods 6.2.1 Inputs and Outputs 6.2.2 Prediction of the mature sequences 6.2.3 The original precursor and its alternative 6.2.4 The validation of the precursor 6.2.5 Alignment processing 6.3 Results and statistics 6.4 Applications 6.4.1 Real life examples and artificial data tests 6.4.2 miRNA and miRNA* prediction 6.4.3 Covariance models 6.5 Conclusion Chapter 7: Discussion

碩士<br>國立成功大學<br>資訊管理研究所<br>107<br>The read habit of readers have changed, more and more readers use the computers or mobile devices to browse news, and the news industry is also digitized. Various news broadcaster published online news to pass information. A large number of online news bring readers a variety of information, but at the same time, readers also need to spend more time digesting them. When readers quering a news event, it often returns a large number of search result, which leads readers to spend extra effort to sort out. In order to solve the problem, some platforms apply the concept of Content Curation, which aggregates the news articles based on event, and then organize and present to readers. At present, most of Content Curation is manually organized. Different from the way of the past platform, this study proposes an automated method of news curation. We first extract the topics from the dataset and use the word sequence to find out the topic sequence through the Hidden Markov Model. Then calculate the strength and the variation to detect important time points during the development of the event. Finally, generate a concise summary to every time points. We combine chronology and summary to design the curation, and look forward to help readers to quickly grasp the context of the news event. Experiments has found that the method has a good performance in each modules. The curation result have good practicality for the readers. But in terms of coherence, there is slightly insufficient to improve.

Experimentally determining the biological function of a protein is a process known as protein characterization. Establishing the role a specific protein plays is a vital step toward fully understanding the biochemical processes that drive life in all its forms. In order for researchers to efficiently locate and benefit from the results of protein characterization experiments, the relevant information is compiled into public databases. To populate such databases, curators, who are experts in the biomedical domain, must search the literature to obtain the relevant information, as the experiment results are typically published in scientific journals. The database curators identify relevant journal articles, read them, and then extract the required information into the database. In recent years the rate of biomedical research has greatly increased, and database curators are unable to keep pace with the number of articles being published. Consequently, maintaining an up-to-date database of characterized proteins, let alone populating a new database, has become a daunting task. In this thesis, we report our work to reduce the effort required from database curators in order to create and maintain a database of characterized proteins. We describe a system we have designed for automatically identifying relevant articles that discuss the results of protein characterization experiments. Classifiers are trained and tested using a large dataset of abstracts, which we collected from articles referenced in public databases, as well as small datasets of manually labeled abstracts. We evaluate both a standard and a modified naïve Bayes classifier and examine several different feature sets for representing articles. Our findings indicate that the resulting classifier performs well enough to be considered useful by the curators of a characterized protein database.<br>Thesis (Master, Computing) -- Queen's University, 2010-01-28 18:45:17.249