ArticleJournal of food science and technology2015
Computational approaches for the classification of seed storage proteins.
Article in Journal of food science and technology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Evaluation and prediction of the physical properties and quality of Jatobá-do-Cerrado seeds processed and stored in different conditions using machine learning models.Scientific reports · 2024Article
- Toward Diverse Plant Proteins for Food Innovation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Sulfate transport and metabolism: strategies to improve the seed protein quality.Molecular biology reports · 2024Review
- Improvements in Visual Aspects and Chemical, Techno-Functional and Rheological Characteristics of Cricket Powder (Foods (Basel, Switzerland) · 2023Article
- Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders.Journal of food science and technology · 2023Review
- In order to lower the antinutritional activity of serine protease inhibitors, we need to understand their role in seed development.Frontiers in plant science · 2023Article
- Predicting the quality of soybean seeds stored in different environments and packaging using machine learning.Scientific reports · 2022Article
- Crosstalk during the Carbon-Nitrogen Cycle That Interlinks the Biosynthesis, Mobilization and Accumulation of Seed Storage Reserves.International journal of molecular sciences · 2021Review
- Modeling to Understand Plant Protein Structure-Function Relationships-Implications for Seed Storage Proteins.Molecules (Basel, Switzerland) · 2020Review
- Predicting ATP-Binding Cassette Transporters Using the Random Forest Method.Frontiers in genetics · 2020Article
- Advances in the Biology of Seed and Vegetative Storage Proteins Based on Two-Dimensional Electrophoresis Coupled to Mass Spectrometry.Molecules (Basel, Switzerland) · 2018Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Seed storage proteins comprise a major part of the protein content of the seed and have an important role on the quality of the seed. These storage proteins are important because they determine the total protein content and have an effect on the nutritional quality and functional properties for food processing. Transgenic plants are being used to develop improved lines for incorporation into plant breeding programs and the nutrient composition of seeds is a major target of molecular breeding programs. Hence, classification of these proteins is crucial for the development of superior varieties with improved nutritional quality. In this study we have applied machine learning algorithms for classification of seed storage proteins. We have presented an algorithm based on nearest neighbor approach for classification of seed storage proteins and compared its performance with decision tree J48, multilayer perceptron neural (MLP) network and support vector machine (SVM) libSVM. The model based on our algorithm has been able to give higher classification accuracy in comparison to the other methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.