Amid COVID-19, Artificial Intelligence (AI) became an essential service to reopen the economy enterprises, and the government realized the importance of data assets and integrated systems. There was an unexpected requirement to solve the AI-led decision-making process during this time. The AI algorithms become man’s friend with their ability in data annotation. A particular data is labeled so that the Machine can understand the inputted material and come up with an accurate output. Companies can build and improve AI implementations with high-quality, human-powered data annotation. The result is a better customer experience solution such as product recommendations, authentic search engine results, speech recognition, computer vision, chatbots, etc.
Human Genomic Annotations
AI & Machine learning (MI) models need both human and machine intelligence. This is known as human-in-the-loop model, where human judgment is used to continuously enhance the performance of machine learning models. Similarly, the process of data annotation needs humans and human-annotated data powers machine learning always. When it comes to annotation, human judgment provides subjectivity, right intent, and clarification. In some ambiguous cases, such as determining whether a search result is relevant, more than one human is needed to reach a consensus. High-quality training data is known as the lifeblood of computer vision applications and machine learning is dependent on the quality as well as quantity of its training data.
Genomics creates more extensive databases for the discovery, study and production of new therapeutics worldwide. It would be impossible to imagine that billions of base pairs comprising the humanoid genetic makeup may now be studied to find genetic variations within the population by Artificial Intelligence. Healthcare firms are increasingly using AI according to HEOR (Health Economics Outcome Research), i.e., help categorize possible clinically significant genes; Artificial Intelligence is used to merge data produced from genomic studies with analysis from the scientific literature. Machine learning also plays an integral role in developing the genomics industry today. Current applications of machine learning in gene technology boost up future applications of genomics machine learning.
Also Read: Everything You Need to Know About Self-Sustained AI and Existing Models
How Human Annotations Help AI?
The recent COVID-19 pandemic was caused by acute respiratory syndrome with the remarkable speed that has infected a large group of people and continues a mortifying influence on the world population’s health and well-being. In this situation, genome sequence analysis and advanced artificial intelligence techniques helped researchers and medical experts to understand the genetic variants of COVID-19 or SARS-CoV-2. Genome sequence analysis of COVID-19 is crucial to understand the virus’s origin, behavior, and structure, which helped produce/develop vaccines, antiviral drugs, and efficient preventive strategies as well.
Humans are better at coping with ambiguity, regulating subjectivity and recognizing purpose than machines. They can annotate a wide range of shapes and sizes of things in various formats. They can complete this task with full customization for a cost-effective and versatile annotation service. One of the critical reasons regarding the increasing acceptance of Artificial Intelligence & Machine Intelligence methods is their ability to identify patterns in data automatically. It is crucial when the expert knowledge is inaccurate or incomplete, when the amount of available information is too large to be manually handled, or when there are exceptions to the general cases. Developing an Artificial Intelligence or Machine Learning model that acts like a human requires huge volumes of training data, testing data, and validation data set needed at various stages of development. For a model to make proper decisions and take action, it must be trained to understand specific information. Human annotation helps categorize and label data for AI applications, and training data must be categorized appropriately and annotated for a particular use case. To develop the right and fully functional model, you need to have the right amount and quality of data sets that should be structured in the correct formats covering all types of variations learned to your ML model. This helps provide accurate predictions in a real-world scenario.
Large databases are developed by genomics for the discovery, study, and development of various treatments all around the world. It’s not difficult to conceive that artificial intelligence (AI) might currently study the 3 billion base pairs that make up humanoid genetic makeup to uncover genetic disparities among the population. By 2026, large pharmaceutical companies are expected to have researched up to 2 million genomes and analyzed vast amounts of patient data from clinical drug studies. To aid in classifying potentially clinically significant genes, AI is used to merge data from genomic research with literature analysis, and machine learning is now a critical component of the genomics industry’s growth.
AI to be successfully used in medical applications, especially those utilizing human genetics and genomics data. Machine Learning (ML) methods for protein function prediction are urgently needed, especially now that a substantial fraction of known sequences remain unannotated despite the extensive use of functional assignments based on sequence similarity. AI and Machine Learning in genomics are already impacting several areas, including medical care delivery, genetic testing, and genomics accessibility for people interested in learning more about how their genes influence their health. Human annotations help explore AI and Machine learning applications in gene technology and their importance in paving the way for upcoming genomics Machine Learning applications. Human annotations boost AI’s role for risk prediction in common complicated diseases and biases that must be addressed appropriately for individualized medicine to be successfully deployed.
Conclusion
The recent explosion of AI follows the excellent achievements made possible by ‘deep learning’ and a burst of ‘big data’ which can meet its hunger. This has been made possible by considerable advancements in high throughput technologies applied to determine how individual components of a biological system work together to accomplish different processes. It helped paradigmatic representatives of big data producers in medical sciences.
By leveraging human intelligence, Opporture can create and train effective AI models according to your business values and goals. We are committed to providing businesses with the best human annotation services available. Contact us today to learn more about our AI model training services for human annotation.
