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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제13권 제2호
- 발행연도
- 2017.1
- 수록면
- 204 - 214 (11page)
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초록· 키워드
오류제보하기
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.
목차
등록된 정보가 없습니다.
참고문헌 (24)
참고문헌 신청
A. Krizhevsky / 2012 / Imagenet classification with deep convolutional neural networks / Advances in Neural Information Processing Systems 25 : 1097 ~ 1105
C. Trapnell / 2009 / TopHat : discovering splice junctions with RNA-Seq / Bioinformatics 25 (9) : 1105 ~ 1111
G. E. Hinton / 2007 / Learning multiple layers of representation / Trends in Cognitive Sciences 11 (10) : 428 ~ 434
G. Hinton / 1995 / The wake-sleep algorithm for unsupervised neural networks / Science 268 (5214) : 1158 ~ 1161
J. Eickholt / 2012 / Predicting protein residue-residue contacts using deep networks and boosting / Bioinformatics 28 (23) : 3066 ~ 3072
C. Trapnell / 2009 / TopHat : discovering splice junctions with RNA-Seq / Bioinformatics 25 (9) : 1105 ~ 1111
G. E. Hinton / 2007 / Learning multiple layers of representation / Trends in Cognitive Sciences 11 (10) : 428 ~ 434
G. Hinton / 1995 / The wake-sleep algorithm for unsupervised neural networks / Science 268 (5214) : 1158 ~ 1161
J. Eickholt / 2012 / Predicting protein residue-residue contacts using deep networks and boosting / Bioinformatics 28 (23) : 3066 ~ 3072
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