Data Mining Over Biological Datasets: An Integrated Approach Based on Computational Intelligence

Stegmayer, Georgina; Gérard, M.; Milone, Diego H.

doi:10.1109/mci.2012.2215122

Cited by 17 publications

(8 citation statements)

References 65 publications

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“…First, as each conversation might have multiple topics, the importance ranking algorithm in our method can be replaced with clustering-based techniques like fuzzy C-means [42] and self-organizing maps [43] to capture the topical diversity among the collection of participating user PWIs. Given a conversation, this approach makes it possible to extract the most relevant PWIs for a specific user from the most context-relevant clusters.…”

Section: Discussionmentioning

confidence: 99%

Context-Aware Personal Information Retrieval From Multiple Social Networks

Han

Wei

Miao

et al. 2014

IEEE Comput. Intell. Mag.

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People use a variety of social networking services to collect and organize web information for future reuse. When such contents are actually needed as reference to reply a post in an online conversation, however, the user may not be able to retrieve them with proper cues or may even forget their existence at all. In this paper, we study this problem in the online conversation context and investigate how to automatically retrieve the most context-relevant previously-seen web information without user intervention. We propose a Context-aware Personal Information Retrieval (CPIR) algorithm, which considers both the participatory and implicit-topical properties of the context to improve the retrieval performance. Since both the context and the user's web information are usually short and ambiguous, the participatory context is utilized to formulate and expand the query. Moreover, the implicit-topical context is exploited to implicitly determine the importance of each web information of the targeting user in the given context. The experimental results using real-world dataset demonstrate that CPIR can achieve significant improvements over several baselines.

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Section: Discussionmentioning

confidence: 99%

Context-Aware Personal Information Retrieval From Multiple Social Networks

Han

Wei

Miao

et al. 2014

IEEE Comput. Intell. Mag.

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“…That is why SOM can be appropriate for visualization and data analysis when looking for underlying hidden patterns in data. A SOM structures the neurons in a way that those in closer proximity are more similar to each other than to others that are farther apart [37].…”

Section: Deepsom For High Class-imbalanced Biological Datamentioning

confidence: 99%

High Class-Imbalance in pre-miRNA Prediction: A Novel Approach Based on deepSOM

Stegmayer

Yones

Kamenetzky

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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The computational prediction of novel microRNA within a full genome involves identifying sequences having the highest chance of being a miRNA precursor (pre-miRNA). These sequences are usually named candidates to miRNA. The well-known pre-miRNAs are usually only a few in comparison to the hundreds of thousands of potential candidates to miRNA that have to be analyzed, which makes this task a high class-imbalance classification problem. The classical way of approaching it has been training a binary classifier in a supervised manner, using well-known pre-miRNAs as positive class and artificially defining the negative class. However, although the selection of positive labeled examples is straightforward, it is very difficult to build a set of negative examples in order to obtain a good set of training samples for a supervised method. In this work, we propose a novel and effective way of approaching this problem using machine learning, without the definition of negative examples. The proposal is based on clustering unlabeled sequences of a genome together with well-known miRNA precursors for the organism under study, which allows for the quick identification of the best candidates to miRNA as those sequences clustered with known precursors. Furthermore, we propose a deep model to overcome the problem of having very few positive class labels. They are always maintained in the deep levels as positive class while less likely pre-miRNA sequences are filtered level after level. Our approach has been compared with other methods for pre-miRNAs prediction in several species, showing effective predictivity of novel miRNAs. Additionally, we will show that our approach has a lower training time and allows for a better graphical navegability and interpretation of the results. A web-demo interface to try deepSOM is available at http://fich.unl.edu.ar/sinc/web-demo/deepsom/.

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“…Let t denote the data generation time of biological engineering. The non-linear calculation of mobile crowd data was shown in equation (7).…”

Section: Mobile Crowd Biological Engineering Opportunistic Optimizatimentioning

confidence: 99%

“…Regarding biological big data, Nounou et al used Wavelet-based multiscale filtering to mine the important features in measured biological data [6]. Stegmayer presented a novel integrated computational intelligence approach for biological data mining that involves neural networks and evolutionary computation [7]. Chatziioannou et al presented the web-based grid application that could exploit grid infrastructures for distributed data processing and management through a generic, consistent, computational analysis framework [8].…”

Section: Introductionmentioning

confidence: 99%

Big data services drive mobile crowd embedded opportunistic control mechanism for biological engineering

Wang

Zhang

2016

J Embedded Systems

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Big data of biological engineering and mobile control increase the complexity of system control. In order to resolve the above problems and improve biological engineering system performance, this paper proposes a large data-driven and mobile crowd embedded opportunistic control mechanism. First of all, the measurement model of established big data-driven biological embedded engineering was proposed based on the research of biological engineering with non-linear and unpredictability. Based on the characteristics of the mobile crowd terminal perception outside interference, we proposed the mobile crowd biological engineering optimization opportunities control mechanism. The experimental results show that the established control mechanism in a long-term, large-scale performance still has high performance and high temperature. In addition, under different pressure, the rate of convergence of the scheme established is superior to the biological engineering control scheme based on coordination control.

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Data Mining Over Biological Datasets: An Integrated Approach Based on Computational Intelligence

Cited by 17 publications

References 65 publications

Context-Aware Personal Information Retrieval From Multiple Social Networks

Context-Aware Personal Information Retrieval From Multiple Social Networks

High Class-Imbalance in pre-miRNA Prediction: A Novel Approach Based on deepSOM

Big data services drive mobile crowd embedded opportunistic control mechanism for biological engineering

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