Bioinformatics

  Building End-to-End ML Pipelines: Models, Tools & Software for Scalable Success In the era of data-driven decision making, machine learning (ML) has evolved from experimental prototypes to robust, production-ready systems. But deploying an ML model isn't as simple as training and testing it once. Real-world applications require a well-structured ML pipeline—a reproducible, scalable, and automated workflow that handles everything from raw data to actionable predictions.   In this blog, we’ll walk you through the essentials of ML pipeline development, the tools and software that power it, and best practices to bridge the gap between experimentation and deployment.   What Is an ML Pipeline? An ML pipeline is a structured sequence of steps that automate the workflow of a machine learning project. It includes: ·        Data Retrieval   ·        Processing of data sets   ·   ...

  Machine Learning and Drug Discovery Using AI: A New Era in Medicine The traditional drug discovery process has been long and expensive when compare to targeted drug discovery process. On average of developing a new drugs has taken over 10 years with this conventional process. However, due to the recent technologies and rapid growth of model world, the drug discovery much simpler and it has open wide variety of opportunities. Machine learning (ML) and artificial intelligence (AI) have made the conventional approach far easier than it was previously. Based on the existing data deposited these many years, researchers can easily predict the future consequences within less time frame. AI, particularly machine learning, offers a way to develop pipe-line for disease prediction, drug-target interaction prediction, lead compound optimization, reducing the cost and time of clinical testing. Designing of novel compounds also can be possible through these days. Key Applications of Mach...