The rapid development of data mining techniques takes place in 1980. Many newly invented technologies came into the computer field such as satellites, high storage of data medium, etc. This improvement paved the way for a destination in one place for the huge, collected data. There were many methods followed earlier for the analysis of data, but they failed to prove efficiency in higher gathered data and in noisy data compared to data mining. Data mining is defined as useful information from the database also said to be Knowledge Discovery in Databases (KDD). data mining has a very large scope to grow and which in hand can be used for society. Data mining is an interdisciplinary area which includes many domain areas interconnected into it such as prediction, information retrieval, database systems, statistical calculations, and machine learning concepts.

The proposed special issue provides an insight into the structural, functional aspects of biological sequences and the pattern recognition it embeds into the data processing. Due to the diversity of contexts in which biological data analysis is performed, the major problem addresses the selection of machine learning techniques for analysis of biological sequences in a huge environment. The performance of various sequences in data mining algorithms are with variant features to prove its higher efficiency in a real-time environment.
The many areas covered in this special issue include, but are not limited to:
Concepts and Technologies
Machine learning
Deep learning
Pattern Recognition
Protein Prediction
Motif Analysis
Next Generation Sequencing
Biomedical Data Analysis
Genomics
RNA-Seq Analysis
Bioinformatics
Text mining
Image Analysis
Image and video processing
PCR
Geosciences and remote sensing
Medical image processing
Drug Discovery
NGS Tools Design
Genomics Tools Design
Pattern matching
Medical diagnosis
Drug Designing with NGS