Integration between Bioinformatics Algorithms and Neutrosophic Theory

Abstract

This paper presents a neutrosophic inference model for bioinformatics. The model is 
used to develop a system for accurate comparisons of human nucleic acids, where the new nucleic 
acid is compared to a database of old nucleic acids. The comparisons are analyzed in terms of 
accuracy, certainty, uncertainty, neutrality, and bias. The proposed system achieves good results 
and provides a reliable standard for future comparisons. It highlights the potential of neutrosophic 
inference models in bioinformatics applications. Data mining and bioinformatics play a crucial role 
in computational biology, with applications in scientific research and industrial development. 
Biological analysts rely on specialized tools and algorithms to collect, store, categorize, and analyze 
large volumes of unstructured data. Data mining techniques are used to extract valuable 
information from this data, aiding in the development of new therapies and understanding genetic 
relationships between organisms. Recent advancements in bioinformatics include gene expression 
tools, Bio sequencing, and Bio databases, which facilitate the extraction and analysis of vital 
biological information. These technologies contribute to the analysis of big data, identification of 
key bioinformatics insights, and generation of new biological knowledge. Data collection, analysis, 
and interpretation in this field involves the use of modern technologies such as cloud computing, 
machine learning, and artificial intelligence, enabling more efficient and accurate results. 
Ultimately, data mining and bioinformatics enhance our understanding of genetic relationships, aid 
in developing new therapies, and improve healthcare outcomes.

DOI: 10.5281/zenodo.10905872