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Scholarly Publications by Integral Academia

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    Data-Driven Approach of Species DNA Sequencing Based on Similar Patterns Using Machine Learning
    (Bentham Science, 2025) Manoj Kumar, Anil Kumar Yadav, Vivek Kumar, Bably Dolly
    Bioinformatics is majorly used to infer hidden information from biological datasets using advanced techniques. Genomics uses machine learning and deep learning extensively to understand hidden facts and dependencies in new biological hypotheses. Recent advancements in dataset development have increased hope in understanding biologically inherited diseases, and a lot of computational models have shown promising results in deoxyribonucleic acid (DNA) sequencing to detect similar and dissimilar kinds of species. Two sequences are called homogeneous if similarities exceed, and we can conclude that two sequences are likely to have an evolutionary relationship. It has already been proven that chimpanzees are ancestors of humans, but can we prove it based on DNA similarity? This work is an effort to understand DNA structure and species of similar kinds based on genome sequencing. We have used the human, chimpanzee, and dog datasets to interpret their DNA sequence. The result shows that humans and chimpanzees share closeness in terms of DNA sequence, which confirms that both organisms are related to each other. Naïve Bays algorithm has shown similarity between chimpanzees and humans by scoring 99.30% and 98.40%, which are the closest figures compared to the dog with a score of 92.60%. This work has a lot of future scope to testify different species and their ancestor, which will help us understand life sciences more closely.
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    Bioinformatics Approaches in Noncoding RNAs Research
    (Springer, Cham, 2026) Fariya Khan, Ajay Kumar, Salman Akhtar
    In recent years, noncoding RNAs have sparked significant interest in understanding the diverse roles of ncRNAs in cellular regulation and disease processes. Noncoding RNAs (ncRNAs), encompassing small noncoding RNAs (sncRNAs) and long noncoding RNAs (lncRNAs), are critical regulators of gene expression, epigenetic modifications, and various cellular processes within the human genome. The diverse nature ncRNAs, along with certain complex features, has made them difficult to study through traditional experimental methods. As a result, bioinformatics tools have expanded the possibilities for offering new insights through advanced computational strategies. This chapter explores the recent advancements in ncRNA databases, emphasizing their importance and the innovative in silico strategies that enable the prediction and analysis of biological interactions, particularly for miRNAs and lncRNAs. It offers an in-depth overview of the structural properties, classification, and functions of various types of noncoding RNAs, highlighting their crucial roles in cellular processes. Additionally, the chapter discusses the significant therapeutic potential of ncRNAs, focusing on their applications in treating cancer and other severe diseases. These insights are pivotal in advancing the development of targeted therapies and precision medicine.