How Big Data and AI help the fight against cancer

Diagnosis, personalized treatment, drug discovery… discover how Big Data and artificial intelligence help fight cancer in all its forms.

Cancer can have multiple causes, and there is no single miraculous solution to overcome it. This is why it is very difficult to fight this disease effectively. However, thanks to artificial intelligence, Machine Learning and Big Data, we could finally win the war against cancer. Find out how.

Genome sequencing, a valuable source of data


The che cost of sequencing the human genome has decreased significantly in recent years.. In 2008, its price was $10 million. In 2019, it can be done for only $1,000 and prices will continue to fall in the future. By 2025, it is estimated that one billion individuals will have sequenced their genome.

Gold, sequencing leads to a massive production of data. By 2030, it is estimated that genomic data will account for two to 40 exabytes per year. These data can be of great help in the fight against cancer .

Fighting Cancer with Big Data and Machine Learning

By combining the data obtained from genome sequencing with Machine Learning algorithms, it is possible to triumph over cancer. Indeed, it is, Machine learning can assist in diagnosis, treatment and prognosis of disease.. It is also possible to develop personalized treatments.

Thanks to the large number of medical records and other data held by hospitals, it is possible to diagnose cancer on the basis of labelled data. In the same way, Natural Language Processing allows to give meaning to doctors’ prescriptions.

The Deep Learning neural networks are used to analyze MRI scans.. Machine Learning’s various algorithms review the database of medical records, and discover hidden patterns to aid diagnosis.

Diagnosing Cancer with Big Data and Machine Learning

One example of how Big Data simplifies cancer diagnosis is the invention of 16-year-old Britanny Wenger. When her cousin was diagnosed with breast cancer, she decided to improve the Fine Need Aspiration (FNA) method: a less invasive method, but considered unreliable by the physician.

To do this, Britanny used her programming skills to create a neural network. She nurtured it using public data on the ANF held by the University of Wisconsin. The neural network then processed the data to detect similarities. Within a few hours, the model became capable of detecting cancer from ANF test data with 99.1% accuracy. The method can be applied to other types of cancers, and the accuracy of the diagnosis depends on the quantity and quality of the data.

Curing Cancer with Big Data and Machine Learning

ia cancer treatment

Beyond the diagnosis, Big Data and Machine Learning also have a role to play in cancer treatment.. For example, the IOC at Boston Hospital was able to plan treatment for his wife with stage 3 breast cancer with the help of Big Data tools he created.

In 2008, he created SHRINE (Shared Health Research Information Network): a research tool developed with the help of Harvard-affiliated hospitals. who shared their databases. This allowed doctors to search for suitable treatments for this cancer through 6 million medical records. Doctors were thus able to treat the IOC woman at Boston Hospital with chemotherapy drugs instead of surgery. The Big Data can therefore enable the development of personalised treatments adapted to each specific type of cancer based on previous successes.

Drug Discovery with Big Data and Machine Learning

The latest scope of Big Data and Machine Learning in the fight against cancer is drug discovery. Using open data, researchers are able to discover new uses for existing drugs.

For example, a group of students at the University of California used Algorithms to discover that a drug used to treat pinworm could also destroy carcinoma. a type of liver cancer. Until then, carcinoma was the second most deadly cancer in the world.

In addition, AI and Machine Learning can also enable the discovery of new drugs. By using data related to different drugs, their properties, chemical composition, disease symptoms or side effects, new drugs can be designed to treat different types of cancer. This facilitates drug discovery and saves millions of dollars.

In conclusion, Big Data and Machine Learning are invaluable assets in the fight against cancer.. From diagnosis to treatment to drug discovery, algorithms are a revolution in global health .

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