Machine Learning

Introduction Bioinformatics, the field that combines biology, computer science, and statistics, has seen significant advancements with the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. AI and ML are revolutionizing the way we analyze vast amounts of biological data, leading to new discoveries and insights in genomics, proteomics, drug development, and personalized medicine. […]

The rise of Artificial Intelligence (AI) has sparked concerns across various professions, and bioinformatics is no exception. With AI demonstrating impressive capabilities in data analysis, many wonder: will AI replace bioinformaticians altogether? The Short Answer: No, not entirely. While AI is undoubtedly transforming bioinformatics, it’s more likely to act as a powerful collaborator than a

The human brain, with its intricate network of neurons and complex processes, remains one of the greatest mysteries of our time. Unveiling its secrets holds the potential to revolutionize our understanding of neurological disorders, mental health, and even consciousness itself. Enter the BRAIN Initiative, a bold multi-billion dollar effort launched by the US government to

Introduction: In the realm of life sciences, Explainable AI (XAI) stands as a transformative force, revolutionizing AI transparency and decision-making. In this article, we delve into the pivotal role of XAI within life sciences, exploring its applications and the benefits it extends to researchers, physicians, and patients alike. The Significance of XAI in Life Sciences:

In the era of big data, where vast amounts of information are generated daily, one area that has seen significant advancements is genome sequencing. This scientific breakthrough has revolutionized the field of genomics, enabling researchers to explore the intricacies of DNA and unlock its secrets. Deep learning, an advanced branch of artificial intelligence, has emerged

Cancer is a complex and heterogeneous disease that affects millions of people worldwide. One of the biggest challenges in cancer treatment is finding the right drug for each patient, as different patients may respond differently to the same drug. This is where AI/ML methods can help, by using large-scale data sets of molecular features and

Antibody-antigen docking is a special case of protein-protein docking, where one of the proteins is an antibody and the other is an antigen. Antibodies are proteins produced by the immune system that can recognize and bind to specific antigens, such as viruses, bacteria, or toxins. Antigens are molecules that can elicit an immune response by

Protein-protein docking is the process of predicting the 3D structure of a protein complex formed by the interaction of two or more proteins. It is a challenging problem in computational biology, as it requires modelling the molecular interactions, conformational changes, and dynamics of the proteins involved. Protein-protein docking has many applications in drug discovery, biotechnology,

Machine learning (ML) has emerged as a transformative force in computational biology, revolutionizing the way researchers analyze biological data. This powerful tool has facilitated predictive modeling, annotation of genomic sequences, and the identification of crucial biological insights. In this blog post, we will explore the various applications of ML in computational and systems biology, highlighting

The landscape of drug discovery has long been marked by challenges such as high costs, inefficiencies, and a high rate of candidate failures. However, recent years have witnessed a remarkable shift as artificial intelligence (AI) and machine learning technologies have begun to revolutionize the drug development process. Pioneering figures like Alex Zhavoronkov, CEO of Insilico