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Open AccessArticle

Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling

1
Anacleto Lab, Computer Science Department, Università degli Studi di Milano, Via Celoria 18, 20133 Milan, Italy
2
Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milan, Italy
3
MIPS Lab, Computer Science Department, Università degli Studi di Milano, Via Celoria 18, 20133 Milan, Italy
4
CINI-AIIS, Italian National Laboratory in Artificial Intelligence and Intelligent Systems, University of Modena and Reggio Emilia, Via Università, 4, 41121 Modena, Italy
*
Authors to whom correspondence should be addressed.
Computers 2020, 9(2), 37; https://doi.org/10.3390/computers9020037
Received: 15 April 2020 / Revised: 30 April 2020 / Accepted: 6 May 2020 / Published: 11 May 2020
Missing data imputation has been a hot topic in the past decade, and many state-of-the-art works have been presented to propose novel, interesting solutions that have been applied in a variety of fields. In the past decade, the successful results achieved by deep learning techniques have opened the way to their application for solving difficult problems where human skill is not able to provide a reliable solution. Not surprisingly, some deep learners, mainly exploiting encoder-decoder architectures, have also been designed and applied to the task of missing data imputation. However, most of the proposed imputation techniques have not been designed to tackle “complex data”, that is high dimensional data belonging to datasets with huge cardinality and describing complex problems. Precisely, they often need critical parameters to be manually set or exploit complex architecture and/or training phases that make their computational load impracticable. In this paper, after clustering the state-of-the-art imputation techniques into three broad categories, we briefly review the most representative methods and then describe our data imputation proposals, which exploit deep learning techniques specifically designed to handle complex data. Comparative tests on genome sequences show that our deep learning imputers outperform the state-of-the-art KNN-imputation method when filling gaps in human genome sequences. View Full-Text
Keywords: data imputation; contractive autoencoders; convolutional neural networks; genome gap filling data imputation; contractive autoencoders; convolutional neural networks; genome gap filling
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MDPI and ACS Style

Cappelletti, L.; Fontana, T.; Di Donato, G.W.; Di Tucci, L.; Casiraghi, E.; Valentini, G. Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling. Computers 2020, 9, 37. https://doi.org/10.3390/computers9020037

AMA Style

Cappelletti L, Fontana T, Di Donato GW, Di Tucci L, Casiraghi E, Valentini G. Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling. Computers. 2020; 9(2):37. https://doi.org/10.3390/computers9020037

Chicago/Turabian Style

Cappelletti, Luca; Fontana, Tommaso; Di Donato, Guido W.; Di Tucci, Lorenzo; Casiraghi, Elena; Valentini, Giorgio. 2020. "Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling" Computers 9, no. 2: 37. https://doi.org/10.3390/computers9020037

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