Abstract
The integration of artificial intelligence (AI) techniques with bioinformatics and computational biology has enabled unmatched insights into complex biological systems and processes. This has paved the way for groundbreaking innovations in biomedicine and biotechnology, with the potential to revolutionize drug discovery, personalized medicine, and therapeutic strategies. AI algorithms, including machine learning, deep learning, natural language processing, and data mining, have proven to be powerful tools for analyzing large biological datasets and extracting meaningful insights. Collaborations between computer scientists, biologists, and clinicians are essential in harnessing the potential of AI in biology and medicine. Ongoing research and interdisciplinary collaboration are crucial to address ethical challenges such as data privacy, patent laws, and the bioethics of AI algorithms. Future advancements in AI algorithms tailored for bioinformatics applications hold immense promise in enhancing data quality and interpretability and driving transformative innovations in healthcare.
TopIntroduction
Human Genome Project (Olson, 1993) introduced copious amounts of genomic data, growing the volume and complexity of biological data in bioinformatics. For the future of this field, it had a high demand for complex computational tools and techniques to extract meaningful insights from these datasets. Understandably, large datasets were required to extract relevant patterns, correlations and hidden relationships between the biological data. This new information is combined or integrated to create a deeper level of understanding or a visual representation of the biological systems and their underlying mechanisms (Nagarajan et al., 2019; Cohen, 2004).
Computational tools developed and applied in the field of bioinformatics primarily facilitate data analysis, visualization and interpretation in biological research. On the other hand, computational biology is a broader field that makes use of computational and mathematical models that study complex biological phenomena, integrating different principles from biology, computer science, mathematics and statistics (Kasabov, 2005).
In the scope of Artificial Intelligence, Machine Learning (ML) and Deep Learning (DL) algorithms serve as strategies for solving complex biological problems and speeding up scientific advancements in different fields such as genomics and systems biology. Applying these removes the major hurdles in specific places like genome assembly, sequence alignment predicting protein structures and drug discovery. In the case of predicting protein structures, AI is used to predict protein interactions and pinpoint biomarkers to detect genetic diseases and create appropriate medicines by enhancing the drug discovery processes. Combining computational methods with biology emphasizes the need for computer specialists, biologists and medical professionals in research to collaborate on endeavors that are focused on leveraging AI’s potential to address pain points in biology and medicine (Duch et al., 2007; Falchi et al., 2014; Dara et al., 2021).
The purpose of this chapter is to deliver an understanding of the use of AI in the existing research, its utilization of those algorithms in specific use cases and its applications, and to identify safety limitations & ethical gaps with the emerging trends, and to establish connections within Bioinformatics and Computational Biology.
Key Terms in this Chapter
Probabilistic Graphical Models: Statistical models representing variable relationships via graphs to encode uncertainty efficiently.
Human Genome Project (HGP): Landmark scientific endeavour mapping the entire human genome, unlocking crucial insights into genetic variation and disease susceptibility.
Protein Remote Homology: Identifying evolutionary relationships between proteins that share a distant common ancestor but exhibit low sequence similarity.
Gene Regulatory Network: Interacting system of genes and regulatory elements controlling gene expression and cellular functions.
Intellectual Property: Legal rights protecting creations of the mind, such as inventions and artistic works, from unauthorized use.
Transcriptomic Data: RNA transcripts present in cells, revealing gene expression patterns and regulatory mechanisms.
Latent Semantic Analysis: A technique used to uncover latent patterns in large datasets by analyzing the relationships between terms and documents based on their semantic meanings.
Natural Language Processing: A field of artificial intelligence focused on enabling computers to understand, interpret, and generate human language.
Genomic Homology Search: Computational technique identifying similarities between nucleotide or protein sequences across different species, aiding in evolutionary studies and gene function prediction.
Drug Discovery: Process of identifying and developing new medications to treat diseases, involving target identification, compound screening, and preclinical and clinical testing to bring effective therapies to market.
Protein Spectral Analysis: The study of the spectral properties of proteins, typically using techniques such as mass spectrometry, to characterize their structure, composition, and function.
Text Mining: The process of extracting useful information and insights from large collections of text data.
Grammatical Analysis: The examination and understanding of the structure and rules governing the grammar of a language or text.
Sequence Databases: Repositories storing biological sequences such as DNA, RNA, and protein sequences, facilitating research and analysis in bioinformatics.
Protein-Protein Interactions: The physical contact between two or more proteins that result in a biological function or process.
Data Privacy: Ensuring the protection and confidentiality of personal information from unauthorized access or use.
Bioethics: The study of ethical issues in biology and medicine, addressing moral dilemmas in healthcare, research, and biotechnology.
Genome: The complete set of genetic material present in an organism, including its genes and non-coding DNA sequences, providing instructions for the development, functioning, and inheritance of traits.