A Deep Reinforcement Learning Approach to Droplet Routing for Erroneous Digital Microfluidic Biochips
Digital microfluidic biochips (DMFBs), which are used in various fields like DNA analysis, clinical diagnosis, and PCR testing, have made biochemical experiments more compact, efficient, and user-friendly than the previous methods. However, their reliability is often compromised by their inability t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/21/8924 |
| _version_ | 1827765357607124992 |
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| author | Tomohisa Kawakami Chiharu Shiro Hiroki Nishikawa Xiangbo Kong Hiroyuki Tomiyama Shigeru Yamashita |
| author_facet | Tomohisa Kawakami Chiharu Shiro Hiroki Nishikawa Xiangbo Kong Hiroyuki Tomiyama Shigeru Yamashita |
| author_sort | Tomohisa Kawakami |
| collection | DOAJ |
| description | Digital microfluidic biochips (DMFBs), which are used in various fields like DNA analysis, clinical diagnosis, and PCR testing, have made biochemical experiments more compact, efficient, and user-friendly than the previous methods. However, their reliability is often compromised by their inability to adapt to all kinds of errors. Errors in biochips can be categorized into two types: known errors, and unknown errors. Known errors are detectable before the start of the routing process using sensors or cameras. Unknown errors, in contrast, only become apparent during the routing process and remain undetected by sensors or cameras, which can unexpectedly stop the routing process and diminish the reliability of biochips. This paper introduces a deep reinforcement learning-based routing algorithm, designed to manage not only known errors but also unknown errors. Our experiments demonstrated that our algorithm outperformed the previous ones in terms of the success rate of the routing, in the scenarios including both known errors and unknown errors. Additionally, our algorithm contributed to detecting unknown errors during the routing process, identifying the most efficient routing path with a high probability. |
| first_indexed | 2024-03-11T11:21:47Z |
| format | Article |
| id | doaj.art-d5bd954ec868459ca6a7cfd720f19e09 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:21:47Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d5bd954ec868459ca6a7cfd720f19e092023-11-10T15:12:47ZengMDPI AGSensors1424-82202023-11-012321892410.3390/s23218924A Deep Reinforcement Learning Approach to Droplet Routing for Erroneous Digital Microfluidic BiochipsTomohisa Kawakami0Chiharu Shiro1Hiroki Nishikawa2Xiangbo Kong3Hiroyuki Tomiyama4Shigeru Yamashita5Graduate School of Science and Engineering, Ritsumeikan University, Kusatsu 525-8577, JapanGraduate School of Science and Engineering, Ritsumeikan University, Kusatsu 525-8577, JapanGraduate School of Information Science and Technology, Osaka University, Osaka 565-0871, JapanDepartment of Intelligent Robotics, Faculty of Engineering, Toyama Prefectural University, Imizu 939-0398, JapanGraduate School of Science and Engineering, Ritsumeikan University, Kusatsu 525-8577, JapanCollege of Information Science and Engineering, Ritsumeikan University, Kusatsu 525-8577, JapanDigital microfluidic biochips (DMFBs), which are used in various fields like DNA analysis, clinical diagnosis, and PCR testing, have made biochemical experiments more compact, efficient, and user-friendly than the previous methods. However, their reliability is often compromised by their inability to adapt to all kinds of errors. Errors in biochips can be categorized into two types: known errors, and unknown errors. Known errors are detectable before the start of the routing process using sensors or cameras. Unknown errors, in contrast, only become apparent during the routing process and remain undetected by sensors or cameras, which can unexpectedly stop the routing process and diminish the reliability of biochips. This paper introduces a deep reinforcement learning-based routing algorithm, designed to manage not only known errors but also unknown errors. Our experiments demonstrated that our algorithm outperformed the previous ones in terms of the success rate of the routing, in the scenarios including both known errors and unknown errors. Additionally, our algorithm contributed to detecting unknown errors during the routing process, identifying the most efficient routing path with a high probability.https://www.mdpi.com/1424-8220/23/21/8924biochipsdigital microfluidic biochipsdeep reinforcement learningoptimization |
| spellingShingle | Tomohisa Kawakami Chiharu Shiro Hiroki Nishikawa Xiangbo Kong Hiroyuki Tomiyama Shigeru Yamashita A Deep Reinforcement Learning Approach to Droplet Routing for Erroneous Digital Microfluidic Biochips Sensors biochips digital microfluidic biochips deep reinforcement learning optimization |
| title | A Deep Reinforcement Learning Approach to Droplet Routing for Erroneous Digital Microfluidic Biochips |
| title_full | A Deep Reinforcement Learning Approach to Droplet Routing for Erroneous Digital Microfluidic Biochips |
| title_fullStr | A Deep Reinforcement Learning Approach to Droplet Routing for Erroneous Digital Microfluidic Biochips |
| title_full_unstemmed | A Deep Reinforcement Learning Approach to Droplet Routing for Erroneous Digital Microfluidic Biochips |
| title_short | A Deep Reinforcement Learning Approach to Droplet Routing for Erroneous Digital Microfluidic Biochips |
| title_sort | deep reinforcement learning approach to droplet routing for erroneous digital microfluidic biochips |
| topic | biochips digital microfluidic biochips deep reinforcement learning optimization |
| url | https://www.mdpi.com/1424-8220/23/21/8924 |
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