During recent years, there has been exponential growth in biological information. With the emergence of large datasets in biology, life scientists are encountering bottlenecks in handling the biological data. This study presents an integrated geographic information system (GIS)-ontology application for handling microbial genome data. The application uses a linear referencing technique as one of the GIS functionalities to represent genes as linear events on the genome layer, where users can define/change the attributes of genes in an event table and interactively see the gene events on a genome layer. Our application adopted ontology to portray and store genomic data in a semantic framework, which facilitates data-sharing among biology domains, applications, and experts. The application was developed in two steps. In the first step, the genome annotated data were prepared and stored in a MySQL database. The second step involved the connection of the database to both ArcGIS and Protege as the GIS engine and ontology platform, respectively. We have designed this application specifically to manage the genome-annotated data of rumen microbial populations. Such a GIS-ontology application offers powerful capabilities for visualizing, managing, reusing, sharing, and querying genome-related data.

https://profdoc.um.ac.ir/articles/a/1050014.pdf

An Ontology-Based GIS for Genomic Data Management of Rumen Microbes.

The semantic web has been proceeding as a future generation web since it provides the various opportunities to enhance the overall performance of the existing web. Ontology creation is the primary step in order to achieve the intelligent web as well as to extract the information by using distinct websites and also for the growth of subject content. In this paper, we define the concepts of evolutionary computation with the help of evolutionary computation ontology. Our focal point is to develop and implement the knowledge content of the evolutionary computation subject using ontology for the learner. This evolutionary computation ontology helps the learner to enhance their knowledge level in the subject of evolutionary computation and it facilitates the learner to use the visualization feature and query feature.

Evolutionary computation ontology: E-learning system

Automation of laboratory tests, bioinformatic analysis of biological sequences, and professional data management are used routinely in a modern university hospital-based infectious diseases institute. This dates back to at least the 1980s. However, the scientific methods of this 21st century are changing with the increased power and speed of computers, with the "big data" revolution having already happened in genomics and environment, and eventually arriving in medical informatics. The research will be increasingly "data driven," and the powerful machine learning methods whose efficiency is demonstrated in daily life will also revolutionize medical research. A university-based institute of infectious diseases must therefore not only gather excellent computer scientists and statisticians (as in the past, and as in any medical discipline), but also fully integrate the biologists and clinicians with these computer scientists, statisticians, and mathematical modelers having a broad culture in machine learning, knowledge representation, and knowledge discovery.

Data Science Priorities for a University Hospital-Based Institute of Infectious Diseases: A Viewpoint.

Microbes, coherently indispensable for agriculture and crop productivity; apart from the catastrophic damage it results. Proper utilization of the microbe can only be achievable through its explicit knowledge of domains and capability of drawing inference from them for better utilization of that knowledge. It is only feasible through an efficient knowledge representation technique – Ontology. Ontology is used in agriculture in various ways like Gene Ontology (GO): Gene Ontology (GO) was developed by Gene Ontology Consortium (Ashburner et al., 2000). AmiGO is an HTML based browser, which one can use to browse and search Gene Ontology (GO). Gene Ontology covers three domains Molecular Function, Biological Process and Cellular Component. Plant International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 7 Number 04 (2018) Journal homepage: http://www.ijcmas.com

/pdf/microbial-taxonomy-ontology-for-agriculturally-important-191kyfwp27.pdf

Microbial Taxonomy Ontology for Agriculturally Important Microorganisms (AMO) Coupled with Sequence Alignment Reinforcement Options

Artificial intelligence (AI) is the branch of science that deals with the development of machines to mimic human intelligence. Machine learning (ML) is a subdomain of AI where the machine can learn automatically from data without being explicitly programmed. Agriculture is constantly pressed upon to produce more with less resource. AI and ML techniques have the capacity to optimize resource utilization by analysing agricultural data. It has changed the present-day face of farming by predicting various input parameters and forecasting post-harvest life of a crop. This chapter discusses the different AI and ML techniques available and how they have been used in different phases of the agriculture life cycle. This chapter includes vast range areas in agriculture that requires AI and ML. It includes soil, irrigation, and disease managements. Importance of AI in the field of plant phenomics also included in this chapter. The probable use of geographic information system (GIS) and remote sensing coupled with AI are discussed in this chapter. 

Sreekumar Biswas

Papers

Building and Querying Microbial Ontology