Search Word(s) in Title, Keywords, Authors, and Abstract: Bioinformatics

Deep Learning in Genomic and Medical Image Data Analysis: Challenges and Approaches
Ning Yu, Zeng Yu, Feng Gu, Tianrui Li, Xinmin Tian and Yi Pan
Page: 204~214, Vol. 13, No.2, 2017
10.3745/JIPS.04.0029

Keywords: Bioinformatics, Deep Learning, Deep Neural Networks, DNA Genome Analysis, Image Data Analysis, Machine Learning, lincRNA
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Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.


A Maximum Entropy-Based Bio-Molecular Event Extraction Model that Considers Event Generation
Hyoung-Gyu Lee, So-Young Park, Hae-Chang Rim, Do-Gil Lee and Hong-Woo Chun
Page: 248~265, Vol. 11, No.2, 2015
10.3745/JIPS.04.0008

Keywords: Bioinformatics, Event Extraction, Maximum Entropy, Text-Mining
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In this paper, we propose a maximum entropy-based model, which can mathematically explain the bio- molecular event extraction problem. The proposed model generates an event table, which can represent the relationship between an event trigger and its arguments. The complex sentences with distinctive event structures can be also represented by the event table. Previous approaches intuitively designed a pipeline system, which sequentially performs trigger detection and arguments recognition, and thus, did not clearly explain the relationship between identified triggers and arguments. On the other hand, the proposed model generates an event table that can represent triggers, their arguments, and their relationships. The desired events can be easily extracted from the event table. Experimental results show that the proposed model can cover 91.36% of events in the training dataset and that it can achieve a 50.44% recall in the test dataset by using the event table.


An Adaptive Workflow Scheduling Scheme Based on an Estimated Data Processing Rate for Next Generation Sequencing in Cloud Computing
Byungsang Kim, Chan-Hyun Youn, Yong-Sung Park, Yonggyu Lee and Wan Choi
Page: 555~566, Vol. 8, No.4, 2012
10.3745/JIPS.2012.8.4.555
Keywords: Resource-Provisioning, Bio-Workflow Broker, Next-Generation Sequencing
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The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batchprocessing scheme in a local computing farm and data storage. In the case of a largescale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resourceprovisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.