Data mining is the process of discovering and extracting of interesting patterns and knowledge from large amounts of data. The field of agriculture has to deal with large amounts of data and processing and retrieval of significant data from this abundance of agricultural information is necessary to help the farmers. Therefore, appropriate methods and techniques are required for managing and organizing this data to increase the efficiency and agricultural productivity. The application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel approach in agriculture. Data mining can help to process and convert this raw data into useful information for improving agriculture. In this paper, various data mining techniques used for processing of agricultural information/data such as k-means clustering, k-nearest neighbour, artificial neural networks, support vector machine, naive Bayesian classifier and fuzzy c-means are described. With the advancement of novel and appropriate data mining techniques, different types of agricultural problems will be addressed to improve crop productivity.
Biological sciences pose a unique set of engineering challenges due to incomplete understanding of natural biological systems. Currently, sequencing of macromolecules i.e., DNA (deoxyribose nucleic acid) and proteins obtained from living cells has provided significant information, which is available in different database repositories. These databases comprising of genomic sequences and amino acid sequences (proteins) are utilized in genetic engineering of biological systems to increase the production of chemicals and pharmaceuticals for improving plant and animal health. Recently, synthetic biology approaches are being employed in rational and highthroughput biological engineering to enhance the production of beneficial chemicals. Recent molecular and bioinformatics tools have enabled to redesign the entire biological cycle, including construction of synthetic DNA inside the cell or replacement of entire genome to create synthetic organisms by utilizing gene libraries, computational tools and interfaces. This review describes the genomic, proteomic and phylogenetic databases, which may be utilized for designing and manipulation of synthetic gene circuits to perform novel functions and desired phenotypes in different ecosystems. In addition, synthetic biology approaches were discussed for designing biological systems for production and release of specific metabolic products. The progress and challenges faced in computational methodology and synthetic biology approaches are discussed for their potential applications in synthetic biology.
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