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Mining new applications from current algorithms
Computer Methods and Programs in Biomedicine 152 (2017) A1
Contents lists available at ScienceDirect
Computer Methods and Programs in Biomedicine journal homepage: www.elsevier.com/locate/cmpb
Editorial
Mining new applications from current algorithms
New technologies and the accessibility of big data have led to enormously increased number of new algorithms with 10-fold to 100-fold improvement in performance. This does not mean the traditional algorithms are out-of-date. This month’s Editor’s Choice articles demonstrates how to use the current algorithms in new ways. Bayesian personalized ranking (BPR) was designed to solve a ranking problem with positive-only information. It is a novel use to apply this kind of algorithm in drug-target interaction prediction, which was previously done with probabilistic algorithms. Peska et al. [1] proposed Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI) to perform in silico prediction of drug-target interactions. They demonstrated how to overcome the challenges with three main techniques. Also, a new “per-drug ranking” approach make BRDTI more suitable for drug-centric repositioning scenarios. Artificial neural networks (ANNs) have been created for more than seventy years. And also have been used in a wide range of disciplines with good performance, especially for non-linear condition. One of the major criticisms of ANNs is their “black-box” character, which block clinical users to understand the relationship between the input and output variables. Lins et al. [2] demonstrated how to dig the relationship between input and output variables using a feature selection in ANNs. With their work, they not only developed a good diagnostic support tool for mild cognitive impairment and dementia, but also found social information may not be decisive in this situation. They opened the “black-box”. Diabetes mellitus is a common and important disease worldwide. Although several classification techniques are available, it is still very difficult to classify diabetes. Gaussian process classification is also a well-known non-parametric machine learning technique. Maniruzzaman et al. [3] used Gaussian process classification with three kernel functions and Laplace approximation framework to classify diabetes mellitus data and got results better than previous studies. This indicated that with proper optimization, it is possible to get a good result without new kinds of algorithm. The aforementioned Editor’s Choice articles demonstrated how to use the current algorithms in new ways. The researches’ brain
https://doi.org/10.1016/S0169-2607(17)31259-2 0169-2607
is still the most important part in the era of machine learning explosion. Chung-Ho Hsieh Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City, Taiwan Department of General Surgery, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan Usman Iqbal Master Program in Global Health and Development, College of Public Health, Taipei Medical University, Taipei, Taiwan International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan Yu-Chuan (Jack) Li∗ International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan Chair, Dept. of Dermatology, Wan Fang Hospital, Taipei, Taiwan ∗ Corresponding
author. 250-Wuxing Street, Xinyi District, Taipei 11031, Taiwan E-mail addresses: [email protected], [email protected] (Y.-C. (Jack) Li)
References [1] L. Peska, K. Buza, J. Koller, Drug-target interaction prediction: A Bayesian ranking approach, Comput. Methods Programs Biomed. 152 (2017) 15–21. [2] A.J.C.C. Lins, M.T.C. Muniz, A.N.M. Garcia, A.V. Gomes, R.M. Cabral, C.J.A. Bastos– Filho, Using artificial neural networks to select the parameters for the prognostic of mild cognitive impairment and dementia in elderly individuals, Comput. Methods Programs Biomed. 152 (2017) 93–104. [3] Maniruzzaman, N. Kumar, M. Abedin, S. Islam, H.S. Suri, A.S. El-Baz, J.S. Suri, Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm, Comput. Methods Programs Biomed. 152 (2017) 23–34.
Contents lists available at ScienceDirect
Computer Methods and Programs in Biomedicine journal homepage: www.elsevier.com/locate/cmpb
Editorial
Mining new applications from current algorithms
New technologies and the accessibility of big data have led to enormously increased number of new algorithms with 10-fold to 100-fold improvement in performance. This does not mean the traditional algorithms are out-of-date. This month’s Editor’s Choice articles demonstrates how to use the current algorithms in new ways. Bayesian personalized ranking (BPR) was designed to solve a ranking problem with positive-only information. It is a novel use to apply this kind of algorithm in drug-target interaction prediction, which was previously done with probabilistic algorithms. Peska et al. [1] proposed Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI) to perform in silico prediction of drug-target interactions. They demonstrated how to overcome the challenges with three main techniques. Also, a new “per-drug ranking” approach make BRDTI more suitable for drug-centric repositioning scenarios. Artificial neural networks (ANNs) have been created for more than seventy years. And also have been used in a wide range of disciplines with good performance, especially for non-linear condition. One of the major criticisms of ANNs is their “black-box” character, which block clinical users to understand the relationship between the input and output variables. Lins et al. [2] demonstrated how to dig the relationship between input and output variables using a feature selection in ANNs. With their work, they not only developed a good diagnostic support tool for mild cognitive impairment and dementia, but also found social information may not be decisive in this situation. They opened the “black-box”. Diabetes mellitus is a common and important disease worldwide. Although several classification techniques are available, it is still very difficult to classify diabetes. Gaussian process classification is also a well-known non-parametric machine learning technique. Maniruzzaman et al. [3] used Gaussian process classification with three kernel functions and Laplace approximation framework to classify diabetes mellitus data and got results better than previous studies. This indicated that with proper optimization, it is possible to get a good result without new kinds of algorithm. The aforementioned Editor’s Choice articles demonstrated how to use the current algorithms in new ways. The researches’ brain
https://doi.org/10.1016/S0169-2607(17)31259-2 0169-2607
is still the most important part in the era of machine learning explosion. Chung-Ho Hsieh Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City, Taiwan Department of General Surgery, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan Usman Iqbal Master Program in Global Health and Development, College of Public Health, Taipei Medical University, Taipei, Taiwan International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan Yu-Chuan (Jack) Li∗ International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan Chair, Dept. of Dermatology, Wan Fang Hospital, Taipei, Taiwan ∗ Corresponding
author. 250-Wuxing Street, Xinyi District, Taipei 11031, Taiwan E-mail addresses: [email protected], [email protected] (Y.-C. (Jack) Li)
References [1] L. Peska, K. Buza, J. Koller, Drug-target interaction prediction: A Bayesian ranking approach, Comput. Methods Programs Biomed. 152 (2017) 15–21. [2] A.J.C.C. Lins, M.T.C. Muniz, A.N.M. Garcia, A.V. Gomes, R.M. Cabral, C.J.A. Bastos– Filho, Using artificial neural networks to select the parameters for the prognostic of mild cognitive impairment and dementia in elderly individuals, Comput. Methods Programs Biomed. 152 (2017) 93–104. [3] Maniruzzaman, N. Kumar, M. Abedin, S. Islam, H.S. Suri, A.S. El-Baz, J.S. Suri, Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm, Comput. Methods Programs Biomed. 152 (2017) 23–34.