Intelligence In Bioinformatics Research Articles

No-boundary thinking for artificial intelligence in bioinformatics and education.

No-boundary thinking enables the scientific community to reflect in a thoughtful manner and discover new opportunities, create innovative solutions, and break through barriers that might have otherwise constrained their progress. This concept encourages thinking without being confined by traditional rules, limitations, or established norms, and a mindset that is not limited by previous work, leading to fresh perspectives and innovative outcomes. So, where do we see the field of artificial intelligence (AI) in bioinformatics going in the next 30years? That was the theme of a "No-Boundary Thinking" Session as part of the Mid-South Computational Bioinformatics Society's (MCBIOS) 19th annual meeting in Irving, Texas. This session addressed various areas of AI in an open discussion and raised some perspectives on how popular tools like ChatGPT can be integrated into bioinformatics, communicating with scientists in different fields to properly utilize the potential of these algorithms, and how to continue educational outreach to further interest of data science and informatics to the next-generation of scientists.

Review on the Application of Artificial Intelligence in Bioinformatics

Compared with traditional data, biomedical data has the characteristics of less samples, high dimension, unstructured data, more types of data, and huge amount of data. Bioinformatics processing requires the intersection of statistics, mathematics, and computer science. Artificial intelligence technology has been used in genome annotation, drug design, structure prediction and other research, helping biologists to screen, process, interpret and utilize the huge data collected in biological research. This paper reviews the application of artificial intelligence in biological information, introduces the application and research progress of machine learning in gene analysis, disease diagnosis, brain image processing, etc., and finally summarizes the full text.

Conference Report on 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2022) [Conference Report

The 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2022) was held from August 15th-17th in Ottawa, Canada. This conference has been held annually since 2004. After two years of virtual conferences, IEEE CIBCB 2022 was held primarily in person, with some remote participation. It was great to be back with friends and colleagues!

Investigation of data mining technique and artificial intelligence algorithm in microflora bioinformatics

Bioinformatics has gradually received widespread attention and has shown the characteristics of a large amount of calculation and high complexity. Therefore, it is required to adopt computer algorithms in bioinformatics to improve the efficiency of bioinformatics processing problems. Big data and artificial intelligence technologies have the characteristics of supporting bioinformatics and have achieved certain results in the field of bioinformatics. Introduced the application basis of big data and artificial intelligence in bioinformatics, analyzed data collection, preprocessing, data storage and management, data analysis, and mining technology. Furthermore, typical applications in bioinformatics are discussed in terms of gene expression data analysis, genome sequence information analysis, biological sequence difference and similarity analysis, genetic data analysis, and protein structure and function prediction. Finally, the bottlenecks and challenges in the application of big data and artificial intelligence in bioinformatics are discussed, and the application prospects of related technologies in bioinformatics have prospected.

English

English

Conference Report on 2017 IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2017) [Conference Reports

Presents information on the 2017 IEEE International Conference on Computational Intelligence .

IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology

IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology

Aplicación de la Inteligencia Artificial en la Bioinformática, avances, definiciones y herramientas.

La bioinformática es la ciencia que combina la biología con las tecnologías de la información. Esta unión surge debido a la problemática dada en fenómenos tan complejos como la genética, la simulación del efecto de medicinas, la predicción de enfermedades, etc. Todas estas situaciones manejan gran cantidad de información y variables, de allí surge la necesidad de apoyarse con las nuevas tecnologías. Pero hay circunstancias en las que ni las mejores plataformas tecnológicas pueden encontrar respuestas en un tiempo prudente, es aquí donde se hace fundamental el uso de herramientas, técnicas, <em>frameworks</em> y metodologías propias de la inteligencia artificial para optimizar la mayor cantidad de procesos, reduciendo el tiempo y el gasto computacional que provocan el manejo de esta información. En el siguiente artículo se desarrolla un estado del arte de las mejores formas de la aplicación de la inteligencia artificial en la bioinformática encontrada en la literatura.

IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology

IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology

Swarm intelligence in bioinformatics: methods and implementations for discovering patterns of multiple sequences.

As a promising and innovative research field, bioinformatics has attracted increasing attention recently. Beneath the enormous number of open problems in this field, one fundamental issue is about the accurate and efficient computational methodology that can deal with tremendous amounts of data. In this paper, we survey some applications of swarm intelligence to discover patterns of multiple sequences. To provide a deep insight, ant colony optimization, particle swarm optimization, artificial bee colony and artificial fish swarm algorithm are selected, and their applications to multiple sequence alignment and motif detecting problem are discussed.

A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.

With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

An ontological-based knowledge organization for bioinformatics workflow management system

Motivation and Objectives In the field of Computer Science, ontologies represent formal structures to define and organize knowledge of a specific application domain (Chandrasekaran et al., 1999). An ontology is composed of entities, called classes, and relationships among them. Classes are characterized by features, called attributes, and they can be arranged into a hierarchical organization. Ontologies are a fundamental instrument in Artificial Intelligence for the development of Knowledge-Based Systems (KBS). With its formal and well defined structure, in fact, an ontology provides a machine-understandable language that allows automatic reasoning for problems resolution. Typical KBS are Expert Systems (ES) and Decision Support Systems (DSS). ESs gather and formalize the knowledge of a human expert of a domain in order to produce inferences and recommendations given an initial query. DSSs are more interactive KBS, in the sense they offer support, rather than replacement, for the decision making process during the execution of a task, suggesting one possible strategy or tool given a set of initial conditions. DSSs are mainly adopted in the clinical field, where they are called Clinical DSS (CDSS). Ontology specification, structure and organization are then of fundamental importance for the development of a KBS. In this paper we present an improvement of our ontological approach for knowledge organization in DSS design. In our previous publication (Fiannaca et al., 2012) we defined a paradigm for ontology specification named Data Problem Solver (DPS) and we showed how our approach can be applied to bioinformatics domain, modeling the Protein-Protein Interaction Network extraction scenario. In the proposed approach, we aim at integrating into our ontology the concept of Workflow as a set of processes. Our main objective is to provide a general schema in order to add the functionalities and capability of a DSS to the more recent Workflow Management Systems, that especially in bioinformatics, with the Taverna workbench (Hull et al., 2006), represent a powerful instrument for researchers. We called our extended ontological approach Data Problem Solver Workflow (DPSW).

An ontological-based knowledge organization for bioinformatics workflow management system

An ontological-based knowledge organization for bioinformatics workflow management system

Special Issue on Computational Intelligence in Bioinformatics [Guest Editorial

Presents the guest editorial for this issue of the publication.

Computational Intelligence in Bioinformatics (Fogel, G.B. et al; 2008) [Book review

Computational Intelligence in Bioinformatics is a valuable reference for researchers and educators alike, providing an excellent snapshot of current hot topics as well as a sampling of important emerging areas. With its curation of the data used by its authors it also serves as model for how collections of papers should be managed.

2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2008) [Conference Reports

The 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology was held at the Sun Valley Resort in Sun Valley, Idaho on September 15-17.

Conference report - 2005 IEEE symposium on computational intelligence in bioinformatics and computational biology (IEEE CIBCB 2005)

Conference report - 2005 IEEE symposium on computational intelligence in bioinformatics and computational biology (IEEE CIBCB 2005)

A Special Issue on Computational Intelligence for Bioinformatics

<i>A Special Issue on</i> Computational Intelligence for Bioinformatics

&lt;I&gt;A Special Issue on&lt;/I&gt; Computational Intelligence for Bioinformatics

&lt;I&gt;A Special Issue on&lt;/I&gt; Computational Intelligence for Bioinformatics

Applications of artificial intelligence in bioinformatics: A review

Artificial intelligence (AI) has increasingly gained attention in bioinformatics research and computational molecular biology. With the availability of different types of AI algorithms, it has become common for the researchers to apply the off-shelf systems to classify and mine their databases. At present, with various intelligent methods available in the literature, researchers are facing difficulties in choosing the best method that could be applied to a specific data set. Researchers need tools, which present the data in a comprehensible fashion, annotated with context, estimates of accuracy and explanation. This article aims to review the use of AI in the areas of bioinformatics and computational molecular biology (DNA sequencing). These areas have risen from the needs of biologists to utilize and help interpret the vast amounts of data that are constantly being gathered in genomic research. The underlying motivation for many of the bioinformatics and DNA sequencing approaches is the evolution of organisms and the complexity of working with erroneous data. This article also describes the kind of software programs which were developed by the research community in order to (1) search, classify and mine different available biological databases; (2) simulate biological experiments with and without errors.