Researchers have used advanced AI and large sets of genomic data to unveil how humans have adapted to recent diseases.

The method could also be applied to new pathogens such as the coronavirus that causes COVID-19, helping identify which gene mutations may be associated with more severe cases of the disease.

The study, by researchers from Imperial College London, the Middle East Technical University, Turkey, and the Universita degli Studi di Bari Aldo Moro, Italy, is published today in a Special Issue of Molecular Ecology Resources on ‘Machine Learning techniques in Evolution and Ecology’.

Natural selection is the process by which beneficial gene mutations are preserved from generation to generation, until they become dominant in our genomes – the catalogue of all our genes. One thing that can drive natural selection is protection against pathogens.

However, if a population of people moves from one environment to another, or changes its way of life, gene mutations that are protective against one pathogen could make people susceptible to new diseases.

One example of such a new disease is Familial Mediterranean Fever (FMF), an inherited autoimmune disease that has emerged over the past 20,000 years. FMF is prevalent in southern Europe, the Middle East and northern Africa, where around 50 percent of the people in the region today carry a gene mutation that makes them more susceptible to the disease.

Spotting selection

This prevalence of a seemingly detrimental gene mutation could be the result of two different types of natural selection. One option is ‘incomplete sweep’, where the gene mutation for susceptibility is in the process of being removed from the population, but has not yet been completely eradicated. In this case, natural selection is ongoing.

The other option is ‘balancing selection’, where some potentially detrimental gene mutations for one condition are preserved in the population because they confer some protection against a different disease. In this case, the gene for FMF susceptibility has been associated with protection against the bacteria Yersinia pestis, which causes the plague.

To determine which version of natural selection is at play in FMF, the researchers turned to advanced AI, which is particularly good at spotting patterns or recognising images. They trained their algorithm on datasets that have known values to test its ability to spot patterns.

The team then ran their algorithm on the database for the 1000 genomes project, which holds genomic data for 2,504 individuals from 26 populations, including the relevant ones around the Mediterranean. They discovered that the FMF gene mutations are still prevalent as a result of ongoing selection; they haven’t reached an equilibrium yet and natural selection is still acting.

Old and new diseases

Lead researcher Dr Matteo Fumagalli, from the Department of Life Sciences at Imperial, said: “This is the first tool to test difference between different types of natural selection, finding signals in the genome that have previously been inaccessible.

“Now we have proven that AI can be used to search genomes for subtle patterns of selection, we can use it to further investigate how humans have both adapted to old diseases, like the plague, and relatively new diseases, like FMF.”

One disease area the team are now investigating is the human relationship with coronaviruses. Humans have been living with coronaviruses for at least 50,000 years, and the greater susceptibility some people have to more severe COVID-19 could be a signal of another balancing selection mechanism.

This study was funded by The Leverhulme Trust, Erasmus+, and Imperial College FoNS European Partners award.

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Commonwealth Bank of Australia is harnessing machine learning technology and Big Data science to offer customers same-day, pro-active emergency assistance in the event of weather-induced natural disasters.The new technology platform uses custom-built algorithms to monitor, in real-time, a mix of data points from official emergency sources and weather alert systems to offer one-to-one, personalised support for customers impacted by natural disasters.

CBA’s chief analytics officer, Andrew McMullan, says: “CBA’s Customer Engagement Engine runs around 400 machine learning models across 157 billion data points in real time so we can add value to our customers in terms of relevance and personal experience – whether that’s through messages and live in-app chats using the CommBank app, or having relevant conversations in-branch or over the phone.”

Most recently, the bank was able to offer same-day personalised support to 80,000 customers who were impacted by the Perth bushfires.

Says McMullen: “Being able to anticipate our customers’ needs and contact them on the same day that a postcode is identified as being at risk from a substantial weather event is a game-changer, and something customers in Perth told us they appreciated during the recent bushfires.”

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An expert in machine learning and natural language processing discusses how these technologies are enhancing care and enabling the use of SDOH data and personalized analytics.

Some healthcare provider organizations are using machine learning and other forms of artificial intelligence to provide clinicians with the best evidence-based care pathways.

A group’s aim could be to improve a patient’s care plan based on personalized analytics. Another goal could be the further merging of evidence-based care paths with historical utilization and outcomes in order to offer optimal patient care. Provider organizations might be using social determinants of health combined with machine learning to offer clinically meaningful services.

Healthcare IT News talked over these ideas with Niall O’Connor, chief technology officer at Cohere Health, a vendor of artificial intelligence technology and services designed to improve the provider, patient and payer experiences.

Q: How is machine learning being used to comprehensively enhance a patient’s entire care plan based on personalized analytics? And how is machine learning being used to combine evidence-based care paths – with real-world historical utilization, outcomes and the latest literature – to provide first-rate patient care?

A: Evidence-based guidelines are an important component of an intelligent care path solution. In fact, they are the starting point for our models. We would never want to relearn the complexity that has been elucidated in clinical guidelines.

At the same time, guidelines were written for the average patient and can’t possibly accommodate all the comorbidity permutations that exist for patients of high acuity. This is where machine learning can help. For patients that don’t perfectly fit existing evidence-based care paths, we can employ machine learning models to infer what has been the most efficacious path for diagnostically identical patients from real world historical data.

Q: How is machine learning being used to use social determinants of health and patient lifestyle to provide precise and clinically meaningful care?

A: Data regarding social determinants of health (SDOH) and patient lifestyle are not typically captured in standard electronic health records, but diligent physicians typically refer to this type of data in their clinical notes.

We can also supplement models with SDOH data – such as the U.S. Census – that can point to access or other patient challenges and incorporate patient-reported data, whether on lifestyle or health state.

This presents a challenge for typical analysis, so we employ natural language processing to help isolate and interpret references to things like lifestyle impacts or resumption of employment following surgery. Although detection of these phrases isn’t comprehensive, when present they can help provide valuable outcome endpoints for us.

Q: How did machine learning first come to be seen as useful in these areas?

A: Clinical data analysis isn’t a big data problem; it’s a messy data problem and is plagued by the fact that much of the valuable information isn’t readily available in structured form.

Volume also plays a big part in why we use machine learning; when we end up with thousands of attributes, we need machine learning to identify the variables that are driving the model. For SDOH in particular, machine learning will be crucial for variable selection and essential to refining some signal from the noise of operational clinical data.

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1| Natural Language Processing

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3| Natural Language Processing With Deep Learning

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5| Deep Natural Language Processing

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8| Speech And Language Processing

About: This is an ebook by authors Dan Jurafsky and James H. Martin where you will learn from the basics to advance of language processing. The topics included here are text normalisation, edit distance, regular expressions, language modelling, logistic regression, vector semantics, neural networks, neural language models, and other such related topics.

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