Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data.

In order to mitigate the effects of a changing climate, agriculture requires more effective evaluation, selection, and production of crop cultivars in order to accelerate genotype-to-phenotype connections and the selection of beneficial traits. Critically, plant growth and development are highly dependent on sunlight, with light energy providing plants with the energy required to photosynthesize as well as a means to directly intersect with the environment in order to develop. In plant analyses, machine learning and deep learning techniques have a proven ability to learn plant growth patterns, including detection of disease, plant stress, and growth using a variety of image data. To date, however, studies have not assessed machine learning and deep learning algorithms for their ability to differentiate a large cohort of genotypes grown under several growth conditions using time-series data automatically acquired across multiple scales (daily and developmentally). Here, we extensively evaluate a wide range of machine learning and deep learning algorithms for their ability to differentiate 17 well-characterized photoreceptor deficient genotypes differing in their light detection capabilities grown under several different light conditions. Using algorithm performance measurements of precision, recall, F1-Score, and accuracy, we find that Suport Vector Machine (SVM) maintains the greatest classification accuracy, while a combined ConvLSTM2D deep learning model produces the best genotype classification results across the different growth conditions. Our successful integration of time-series growth data across multiple scales, genotypes and growth conditions sets a new foundational baseline from which more complex plant science traits can be assessed for genotype-to-phenotype connections.

RNA Secondary Structure Prediction with Pseudoknots Using Chemical Reaction Optimization Algorithm.

RNA molecules play a significant role in cell function especially including pseudoknots. In past decades, several methods have been developed to predict RNA secondary structure with pseudoknots and the most popular one uses minimum free energy. It is a nondeterministic polynomial-time hard (NP-hard) problem. We have proposed an approach based on a metaheuristic algorithm named Chemical Reaction Optimization (CRO) to solve the RNA pseudoknotted structure prediction problem. The reaction operators of CRO algorithm have been redesigned and used on the generated population to find the structure with the minimum free energy. Besides, we have developed an additional operator called Repair operator which has a great influence on our algorithm in increasing accuracy. It helps to increase the true positive base pairs while decreasing the false positive and false negative base pairs. Four energy models have been applied to calculate the energy. To evaluate the performance, we have used four datasets containing RNA pseudoknotted sequences taken from the RNA STRAND and Pseudobase++ database. We have compared the proposed approach with some existing algorithms and shown that our CRO based model is a better prediction method in terms of accuracy and speed.