Machine Learning Applications in Bioinformatics: Real World Application

Importance of Machine Learning Applications in Bioinformatics

Machine learning is oriented toward constructing computational systems that learn from data. With the onset of the information age and increasing demand of bioinformatics and AI, data are being produced from various sources and not just people and servers (such as sensors embedded in phones and wearables, video surveillance cameras, MRI machines set-top boxes). Artificial intelligence AI, specifically machine learning ML has advanced quickly in the last few years in reference to data analysis and computational applications that allow applications to act in intelligent ways.

With the emergence of these information intensive data streams and advances in high performance computing technologies, big data analytics have emerged to conduct real time descriptive and predictive analysis on massive amounts of data, for the purposes of making informed intelligent decisions which helps in all types of healthcare decision making and innovations.,

Real World Application: Machine Learning Applications in Bioinformatics

Machine Learning Applications in Bioinformatics and Biological Research

Machine learning has become an essential component to many tasks in biological research. For instance we can take examples of how deep learning in genomics, and AI for drug discovery is helping scientists to predict and analyze.. If you are wondering how deep learning in genomics, and AI for drug discovery is helping our scientists! Machine Learning has helped increase precision, accuracy, and efficiency for predictive modeling, addressing biological questions across several scales from predicting molecular structures, to omics analysis, to pest identification and ecological prediction. For example explore the application of bioinformatics and AI in microarray data analysis and evolutionary research, mentioned bellow:

Microarray data analysis

Microarray data sets are increasing quickly, th costs of microarray experiment continue to drop and the experimentation with microarrays has become common in biology today. Microarray experiments are being conducted not only for gene sample analysis but to capture how gene expression values change over time, or the stages of a disease. Bioinformatics with Big data technologies are needed to rapidly construct both co-expression and regulatory networks from large microarray data sets.

Evolutionary research

According to new developments in molecular biological technologies, machine learning has become one of the primary drivers of data production. For example, huge amount of data has been produced in different projects that developed or generated data at multiple scales of a microbial organism through a variety of technologies such as whole genome sequencing, microarrays, and metabolomics and much of that is passed on as bioinformatics, which has been developing as an analytical and archival platform, to extract useful information from this vast amount of data.

A study published in BMC Biology by Md Alam, and Kiran Basava showed that an areas of big data exploration in bioinformatics has been in efforts using the evolutionary stories of adaptation and change using microbials, or applying biology to study primitive organisms.

Intelligent decision-making

One of the main application areas of ML is intelligent data-driven predictive analytics for decision making. Predictive analytics is based on the observation that we can determine and utilize relationships between explanatory variables and predicted variables based on past occurrences to predict the unknown outcome of occurrence. For example identifying potential suspects or criminals once a crime has been committed. Or, gauging the factual evidence of credit card fraud in real time as it occurs.

Another example where MLA can assist retailers to better gauge consumer preferences and behavior, manage stock levels and minimize out of stock situations, and improve logistics and warehousing in the context of e-commerce. Accurate predictions provide insights into the unknown that can improve the decision-making of industries, businesses, and other organization types including government agencies, e-commerce, telecommunications, banking and financial services, healthcare, sales and marketing, transportation, social networking and more

COVID-19 pandemic

Machine learning can assist in resolving diagnostic and prognostic concerns in many healthcare sector, including but not limited to, the diagnosis of disease, extraction of medical knowledge, mining for regularities in data, patient management etc. Coronavirus disease COVID-19 is a disease caused by a new coronavirus, per the World Health Organization (WHO). In the fight against COVID-19 in particular over the last few years, learning techniques have become popular. The learning techniques apply to COVID-19 pandemic patients, including classification of those patients at high risk, and their mortality risk, and the disease's other features.

The learning techniques also apply to understanding the origins of the infection, prediction of new COVID-19 cases, and for diagnosing the disease and treatment. Machine learning primarily predicts specific locations where COVID-19 is most likely to spread, the timeframe for the spread, and would also help notify those locations to make necessary plans.

Sustainable agriculture

Agriculture is crucial for all human activities to survive and ustainable agriculture practices promote agricultural productivity without causing the same negative impacts to the environment. Sustainable agriculture supply chains are knowledge-intensive and rely on information, skills, technologies, etc., where knowledge transfer allows farmers to increase their decision making of sustainable agriculture practices with the surplus of data that can be obtained through emerging technologies, e.g. The Internet of Things (IoT), mobile technologies and devices, etc.

Machine learning can be used in different phases of sustainable agriculture as mentioned bellow:
• Phase I - pre-production phase (predicting yields, soil properties, irrigation needs, etc.)
• Phase II - production phase (weather prediction, disease detection, weed detection, soil nutrient management, livestock management, etc.)
• Phase III - processing phase (demand forecasting, production planning, etc.)
• Phase IV - distribution phase (inventory management, consumer analysis, etc.)