1
|
Tomizawa Y, Minamino N, Shimokawa E, Kawamura S, Komatsu A, Hiwatashi T, Nishihama R, Ueda T, Kohchi T, Kondo Y. Harnessing Deep Learning to Analyze Cryptic Morphological Variability of Marchantia polymorpha. Plant Cell Physiol 2023; 64:1343-1355. [PMID: 37797211 DOI: 10.1093/pcp/pcad117] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 09/20/2023] [Accepted: 09/29/2023] [Indexed: 10/07/2023]
Abstract
Characterizing phenotypes is a fundamental aspect of biological sciences, although it can be challenging due to various factors. For instance, the liverwort Marchantia polymorpha is a model system for plant biology and exhibits morphological variability, making it difficult to identify and quantify distinct phenotypic features using objective measures. To address this issue, we utilized a deep-learning-based image classifier that can handle plant images directly without manual extraction of phenotypic features and analyzed pictures of M. polymorpha. This dioicous plant species exhibits morphological differences between male and female wild accessions at an early stage of gemmaling growth, although it remains elusive whether the differences are attributable to sex chromosomes. To isolate the effects of sex chromosomes from autosomal polymorphisms, we established a male and female set of recombinant inbred lines (RILs) from a set of male and female wild accessions. We then trained deep learning models to classify the sexes of the RILs and the wild accessions. Our results showed that the trained classifiers accurately classified male and female gemmalings of wild accessions in the first week of growth, confirming the intuition of researchers in a reproducible and objective manner. In contrast, the RILs were less distinguishable, indicating that the differences between the parental wild accessions arose from autosomal variations. Furthermore, we validated our trained models by an 'eXplainable AI' technique that highlights image regions relevant to the classification. Our findings demonstrate that the classifier-based approach provides a powerful tool for analyzing plant species that lack standardized phenotyping metrics.
Collapse
Affiliation(s)
- Yoko Tomizawa
- Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazak, Aichii, 444-8787 Japan
| | - Naoki Minamino
- Division of Cellular Dynamics, National Institute for Basic Biology, Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585 Japan
| | - Eita Shimokawa
- Graduate School of Biostudies, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo, Kyoto, 606-8502 Japan
| | - Shogo Kawamura
- Graduate School of Biostudies, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo, Kyoto, 606-8502 Japan
| | - Aino Komatsu
- Graduate School of Biostudies, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo, Kyoto, 606-8502 Japan
| | - Takuma Hiwatashi
- Division of Cellular Dynamics, National Institute for Basic Biology, Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585 Japan
| | - Ryuichi Nishihama
- Graduate School of Biostudies, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo, Kyoto, 606-8502 Japan
- Department of Applied Biological Science, Faculty of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba, 278-8510 Japan
| | - Takashi Ueda
- Division of Cellular Dynamics, National Institute for Basic Biology, Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585 Japan
- Department of Basic Biology, SOKENDAI (The Graduate University for Advanced Studies), Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585 Japan
| | - Takayuki Kohchi
- Graduate School of Biostudies, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo, Kyoto, 606-8502 Japan
| | - Yohei Kondo
- Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazak, Aichii, 444-8787 Japan
- Division of Quantitative Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi, 444-8787 Japan
- Department of Basic Biology, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies), 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi, 444-8787 Japan
| |
Collapse
|
2
|
Yudistira N, Sumitro SB, Nahas A, Riama NF. Learning where to look for COVID-19 growth: Multivariate analysis of COVID-19 cases over time using explainable convolution-LSTM. Appl Soft Comput 2021; 109:107469. [PMID: 33994895 PMCID: PMC8103767 DOI: 10.1016/j.asoc.2021.107469] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 04/20/2021] [Accepted: 04/28/2021] [Indexed: 12/28/2022]
Abstract
Determinant factors which contribute to the prediction should take into account multivariate analysis for capturing coarse-to-fine contextual information. From the preliminary descriptive analysis, it shows that environmental factor such as UV (ultraviolet) is one of the essential factors that should be considered to observe the COVID-19 epidemic drivers. Moreover, there are education, government, morphological, health, economic, and behavioral factors contributing to the growth of COVID-19. Besides descriptive analysis, in this research, multivariate analysis is considered to provide comprehensive explanations about factors contributing to pandemic dynamics. To achieve rich explanations, visual attribution of explainable Convolution-LSTM is utilized to see high contributing factors responsible for the growth of daily COVID-19 cases. Our model consists of 1 D CNN in the first layer to capture local relationships among variables followed by LSTM layers to capture local dependencies over time. It produces the lowest prediction errors compared to the other existing models. This permits us to employ gradient-based visual attribution for generating saliency maps for each time dimension and variable. These are then used for explaining which variables throughout which period of the interval is contributing for a given time-series prediction, likewise as explaining that during that time intervals were the joint contribution of most vital variables for that prediction. The explanations are useful for stakeholders to make decisions during and post pandemics. The explainable Convolution-LSTMcode is available here: https://github.com/cbasemaster/time-series-attribution.
Collapse
Affiliation(s)
| | | | - Alberth Nahas
- Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University Raleigh, United States of America.,Indonesian Agency for Meteorology, Climatology and Geophysics, Center for Research and Development, Indonesia
| | - Nelly Florida Riama
- Indonesian Agency for Meteorology, Climatology and Geophysics, Center for Research and Development, Indonesia
| |
Collapse
|