Machine learning approaches and their current application in plant molecular biology: A systematic review

Silva, José Cleydson Ferreira da; Teixeira, Ruan M.; Silva, Fabyano Fonseca e; Brommonschenkel, Sérgio Hermínio; Fontes, Elizabeth P. B.

doi:10.1016/j.plantsci.2019.03.020

Cited by 75 publications

(72 citation statements)

References 130 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These factors lead to categorize this process as a complex and non-linear biological process. Thus, traditional statistical and computational methods such as simple regression cannot be considered as an appropriate approach for studying the effect of melatonin on plant biological responses to drought stress [23]. Hence, there is a serious need to use nonlinear statistical methodology such as artificial neural networks (ANNs).…”

Section: Introductionmentioning

confidence: 99%

“…The examples should be carefully chosen otherwise time is wasted or even worse the model might be working inaccurately [23]. However, difficulty in achieving an optimized solution can be considered as one of the demerit points of most machine learning algorithms [23,30,31]. To overcome this bottleneck, Zhang et al [32] employed the genetic algorithm (GA) as one of the common optimization algorithms for optimizing relative humidity, light duration, agar concentration, and culture temperature in order to maximize indirect shoot organogenesis in Cucumis melo.…”

Section: Introductionmentioning

confidence: 99%

“…In another study, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was employed to optimize different types and concentrations of disinfectants as well as immersion time for maximizing explant viability and minimizing in vitro contamination in chrysanthemum [33]. However, most studies have found the optimized solution by trials and error [23,34,35]. NSGA-II is the search algorithms inspired by natural selection and genetics concepts.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

Jafari

Shahsavar

2020

PLoS ONE

View full text Add to dashboard Cite

Drought stress as one of the most devastating abiotic stresses affects agricultural and horticultural productivity in many parts of the world. The application of melatonin can be considered as a promising approach for alleviating the negative impact of drought stress. Modeling of morphological responses to drought stress can be helpful to predict the optimal condition for improving plant productivity. The objective of the current study is modeling and predicting morphological responses (leaf length, number of leaves/plants, crown diameter, plant height, and internode length) of citrus to drought stress, based on four input variables including melatonin concentrations, days after applying treatments, citrus species, and level of drought stress, using different Artificial Neural Networks (ANNs) including Generalized Regression Neural Network (GRNN), Radial basis function (RBF), and Multilayer Perceptron (MLP). The results indicated a higher accuracy of GRNN as compared to RBF and MLP. The great accordance between the experimental and predicted data of morphological responses for both training and testing processes support the excellent efficiency of developed GRNN models. Also, GRNN was connected to Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize input variables for obtaining the best morphological responses. Generally, the validation experiment showed that ANN-NSGA-II can be considered as a promising and reliable computational tool for studying and predicting plant morphological and physiological responses to drought stress.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

Jafari

Shahsavar

2020

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…It is becoming difficult to find a field that hasn't yet adopted ML approaches in one way or another, with applications ranging all the way from the humanities (see [2][3][4] for some examples) to paleontology [5] or health care [6]. The biological sciences are indeed no exception [7][8][9][10], having entered into the era of Big Data thanks to technological advances such as next generation sequencing and multi-omics approaches [11]. Machine learning, and deep learning in particular, have been used to predict sequence specifities of both DNA-and RNA-binding proteins, as well as enhancers and other types of regulatory regions [12,13].…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for staging embryonic tissue isolates with small data

Pond

Hwang

Verd

et al. 2020

Preprint

View full text Add to dashboard Cite

Machine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these models. The requirement for large training sets is also responsible for limiting further potential applications of machine learning, particularly in fields where data tend to be scarce such as developmental biology. However, recent research seems to indicate that machine learning and Big Data can sometimes be decoupled to train models with modest amounts of data. In this work we set out to train a CNN-based classifier to stage zebrafish tail buds at four different stages of development using small information-rich data sets. Our results show that two and three dimensional convolutional neural networks can be trained to stage developing zebrafish tail buds based on both morphological and gene expression confocal microscopy images, achieving in each case up to 100% test accuracy scores. Importantly, we show that high accuracy can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a convolutional neural net. Furthermore, our classifier shows that it is possible to stage isolated embryonic structures without the need to refer to classic developmental landmarks in the whole embryo, which will be particularly useful to stage 3D culture in vitro systems such as organoids. We hope that this work will provide a proof of principle that will help dispel the myth that large data set sizes are always required to train CNNs, and encourage researchers in fields where data are scarce to also apply ML approaches.Author summaryThe application of machine learning approaches currently hinges on the availability of large data sets to train the models with. However, recent research has shown that large data sets might not always be required. In this work we set out to see whether we could use small confocal microscopy image data sets to train a convolutional neural network (CNN) to stage zebrafish tail buds at four different stages in their development. We found that high test accuracies can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a CNN. This work also shows that we can robustly stage the embryonic development of isolated structures, without the need to refer back to landmarks in the tail bud. This constitutes an important methodological advance for staging organoids and other 3D culture in vitro systems. This work proves that prohibitively large data sets are not always required to train CNNs, and we hope will encourage others to apply the power of machine learning to their areas of study even if data are scarce.

show abstract

“…This algorithm consists of numerous highly interconnected processing neurons that work in parallel to find a solution for a particular problem. MLP is learned by example, which should be carefully chosen otherwise time is wasted or even in worse scenarios, the model might be working inaccurately [52].…”

mentioning

confidence: 99%

Development of support vector machine-based model and comparative analysis with artificial neural network for modeling the plant tissue culture procedures: effect of plant growth regulators on somatic embryogenesis of chrysanthemum, as a case study

et al. 2020

View full text Add to dashboard Cite

Background: Optimizing the somatic embryogenesis protocol can be considered as the first and foremost step in successful gene transformation studies. However, it is usually difficult to achieve an optimized embryogenesis protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach, such as machine learning algorithms for this aim. In the present study, two machine learning algorithms, including Multilayer Perceptron (MLP) as an artificial neural network (ANN) and support vector regression (SVR), were employed to model somatic embryogenesis of chrysanthemum, as a case study, and compare their prediction accuracy. Results: The results showed that SVR (R 2 > 0.92) had better performance accuracy than MLP (R 2 > 0.82). Moreover, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was also applied for the optimization of the somatic embryogenesis and the results showed that the highest embryogenesis rate (99.09%) and the maximum number of somatic embryos per explant (56.24) can be obtained from a medium containing 9.10 μM 2,4-dichlorophenoxyacetic acid (2,4-D), 4.70 μM kinetin (KIN), and 18.73 μM sodium nitroprusside (SNP). According to our results, SVR-NSGA-II was able to optimize the chrysanthemum's somatic embryogenesis accurately. Conclusions: SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.

show abstract

Machine learning approaches and their current application in plant molecular biology: A systematic review

Cited by 75 publications

References 130 publications

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

A deep learning approach for staging embryonic tissue isolates with small data

Development of support vector machine-based model and comparative analysis with artificial neural network for modeling the plant tissue culture procedures: effect of plant growth regulators on somatic embryogenesis of chrysanthemum, as a case study

Contact Info

Product

Resources

About