Other than this, there are also other image processing techniques like image recognition using Support Vector Machines<\/strong><\/a>, image enhancement and reconstruction, edge detection etc.<\/p>\n There are several other methods that are applied to enhance the images and improve the accuracy of the outcome. Big Data platforms like Hadoop apply MapReduce to find parameters that can be used in various tasks.<\/p>\n For data science enthusiasts beginning with image analysis, there are several open datasets of brain imaging that you can utilize for gaining a practical experience –<\/p>\n Genomics is the study of sequencing and analysis of genomes. A genome consists of the DNA and all the genes of the organisms. Ever since the compilation of the Human Genome Project, the research has been advancing rapidly and has inculcated itself in the realms of big data and data science<\/strong><\/a>.<\/p>\n Before the availability of powerful computation, the organizations spent a lot of time and money on analyzing the sequence of genes. This was an expensive and tedious process.<\/p>\n However, with the advanced data science tools, it is now possible to analyze and derive insights from the human gene in a much shorter period of time and in a much lower cost.<\/p>\n The goal of research scientists is to analyze the genomic strands and search for irregularities and defects in it. Then, they find connections between genetics and health of the person.<\/p>\n In general, researchers use data science to analyze the genetic sequences and try to find a correlation between the parameters contained within it and the disease.<\/p>\n Furthermore, research in genomics also involves finding the right drug which provides a deeper insight in the way a drug reacts to a particular genetic issue. There is in fact, a recent discipline that combines data science and genetics called Bioinformatics.<\/p>\n There are several data science tools<\/strong><\/a> like MapReduce, SQL, Galaxy, Bioconductor etc. MapReduce processes the genetic data and reduces the time it takes to process genetic sequences.<\/p>\n SQL is a relational database language that we use to perform querying and retrieve data from genomic databases. Galaxy is an open source, GUI based biomedical research application that allows you to perform various operations on genomes.<\/p>\n And finally, Bioconductor is an open-source software developed for the analysis and comprehension of genomic data.<\/p>\n The research that has been conducted in the field of computational biology and bioinformatics, there is still a lot of ocean that still remains uncharted. There are advanced fields that are still being researched such as genetic risk prediction, gene expression prediction etc.<\/p>\n Drug Discovery is a highly complicated discipline. Pharmaceutical industries are heavily relying on data science to solve their problems and create better drugs for the people. Drug Discovery is a time-consuming process that also involves heavy financial expenditure and heavy testing.<\/p>\n Data Science and Machine Learning algorithms<\/strong> <\/a>are revolutionizing this process and providing extensive insights into optimizing and increasing the success rate of predictions.<\/p>\n Pharmaceutical companies use the insights from the patient information such as mutation profiles and patient metadata. This information helps the researchers to develop models and find statistical relationships between the attributes.<\/p>\n This way, companies can design drugs that address the key mutations in the genetic sequences. Also, deep learning algorithms can find the probability of the development of disease in the human system.<\/p>\n The data science algorithms can also help to simulate how the drugs will act in the human body that takes away the long laboratory experimentations.<\/p>\n With the advancements in the data-science facilitated drug discovery, it is now possible to improve the collection of historical data to assist in the drug development process. With a combination of genetics and drug-protein binding databases, it is possible to develop new innovations in this field.<\/p>\n Furthermore, using data science, researchers can analyze and test the chemical compounds against a combination of different cells, genetic mutations etc. Usage of machine learning algorithms, researchers can develop models that compute the prediction from the given variables.<\/p>\n Healthcare is an important domain for predictive analytics. It is one of the most popular topics in health analytics. A predictive model uses historical data, learns from it, finds patterns and generates accurate predictions from it.<\/p>\n It finds various correlations and association of symptoms, finds habits, diseases and then makes meaningful predictions.<\/p>\n Predictive Analytics is playing an important role in improving patient care, chronic disease management and increasing the efficiency of supply chains and pharmaceutical logistics.<\/p>\n Population health management is becoming an increasingly popular topic in predictive analytics. It is a data-driven approach focusing on prevention of diseases that are commonly prevalent in society.<\/p>\n With data science, hospitals can predict the deterioration in patient\u2019s health and provide preventive measures and start an early treatment that will assist in reducing the risk of the further aggravation of patient health.<\/p>\n Furthermore, predictive analytics plays an important role in monitoring the logistic supply of hospitals and pharmaceutical departments.<\/p>\n Data Science plays a vital role in IoT (Internet of Things)<\/strong><\/a>. These IoT devices, that are present as wearable devices that track heartbeat, temperature and other medical parameters of the users. The data that is collected is analyzed with the help of data science.<\/p>\n With the help of analytical tools, doctors are able to keep track of patient\u2019s circadian cycle, their blood pressure as well as their calorie intake.<\/p>\n Other than wearable monitoring sensors, doctor can monitor a patient\u2019s health through home devices. For patients that are chronically ill, there are several systems that track patient\u2019s movements, monitor their physical parameters and analyze the patterns that are present in the data.<\/p>\n It makes use of real-time analytics to predict if the patient will face any problem based on the present condition. Furthermore, it helps the doctors to take the necessary decisions to help the patients in distress.<\/p>\n Data Science plays a pivotal role in monitoring patient\u2019s health and notifying necessary steps to be taken in order to prevent potential diseases from taking place. Data Scientists are using powerful predictive analytical tools to detect chronic diseases at an early level.<\/p>\n In many extreme cases, there are instances where due to negligibility, diseases are not caught at an early stage.<\/p>\n This proves to be highly detrimental to not only the patient\u2019s health but also the economic costs. As the disease grows, the cost of curing it also increases. Therefore, data science plays a huge role in optimizing the economic spending on healthcare.<\/p>\n There are several instances where AI has played a huge role in detecting diseases at an early stage. Researchers at the University of Campinas<\/strong> in Brazil have developed an AI platform that can diagnose Zika virus<\/strong> using metabolic markers.<\/p>\n Several other companies like IQuity are using machine learning to detect autoimmune diseases.<\/p>\n![\"Data](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/data-science-in-medical-imaging.jpeg\")
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2. Data Science for Genomics<\/h4>\n
3. Drug Discovery with Data Science<\/h4>\n
4. Predictive Analytics in Healthcare<\/h4>\n
5. Monitoring Patient Health<\/h4>\n
6. Tracking & Preventing Diseases<\/h4>\n