AI Research
How AI is transforming research into rare dementias
For most research studies and clinical trials, securing a large sample size is a perennial challenge. A huge amount of time and resource goes into recruiting willing participants to ensure an adequate data set.
While a typical trial may use hundreds of thousands of samples, reaching these numbers is often unfeasible for rare diseases, where eligible participants are few and far between.
“We don’t have a good way of identifying genes associated with rarer disorders because they are so rare and the sample numbers small,” says Dr Maryam Shoai, a Senior Research Fellow in Professor Sir John Hardy’s lab at the UCL Queen Square Institute of Neurology.
Dr Shoai specialises in statistical modelling with a focus on genetic and clinical data in neurodegenerative disorders. For the past 18 months, Dr Shoai’s team, in collaboration with University of Surrey’s Nature Inspired Computing and Engineering Research Group has been using machine learning to determine whether it’s possible to reduce sample numbers while still achieving results comparable to traditional methods of genome-wide association studies (GWAS).
GWAS analysis scans the genome of many individuals to identify genetic variations associated with specific diseases or traits. These studies typically require thousands of samples, over a million participants were used in the latest GWAS of Alzheimer’s disease.
Big discoveries with smaller samples using AI
To explore whether it is possible to reduce sample sizes and still retain power to detect effects, the team use logic programming, a subset of artificial intelligence. Unlike traditional AI approaches that rely on vast datasets and statistical models, logic programming uses known rules and relationships to identify genetic patterns.
Primary analysis on pilot data suggests that even with as few as 250 samples, the genetic regions identified for Alzheimer’s disease are similar to a standard GWAS, which typically use thousands of cases and controls.
“This is huge, because it means that it could be used for rarer dementias or disorders. At the moment we struggle to do GWAS for rarer diseases as the sample numbers are often lacking.”
Recently, Dr Shoai and her team investigated Pick’s disease. Pick’s disease is a rare form of Frontotemporal dementia, which can only be definitively diagnosed post-mortem by examining brain tissue. Despite using almost all the brains donated to research with confirmed Pick’s disease, the numbers only reached 300-400, which is classed as a small sample size for GWAS.
This highlighted the need to explore other methods to investigate the genetics of Pick’s disease.
“Using this logic programming method seems to show that standard statistical tests like genome-wide association studies could be replaced with more complex methodologies and get similar answers for smaller data sets.”
Methods like logic programming could also be revolutionary for countries where genotyping thousands of people and obtaining good quality phenotyping data, or tracking them for a long period of time for longitudinal studies can be difficult, be it due to economic issues or geographical hinderance. Even for a disease as common as Alzheimer’s disease, data sets can still be small.
“It’s something that’s always on our minds” says Dr Maryam Shoai. “We don’t have the background knowledge of what most neurodegenerative diseases look like in most non-Caucasian populations, and we need to bridge this gap to enable globally suitable therapies.”
Collaboration is key to developing our understanding of neurogenetics
Dr Shoai believes that the possibilities to expand our knowledge of genetics, using artificial intelligence are vast, but collaboration and an interdisciplinary approach is crucial: “The real test is marrying the knowledge between artificial intelligence models with the expert information we have from genetic, clinical, and disease biology.”
“If we get enough people trained in both fields simultaneously, it could propel this area exponentially over the next few years.”
This collaborative ethos is at the heart of UCL’s new neuroscience centre. Due to open in 2027, the building makes it easier for researchers working in different areas and in different ways to cross paths.
The impact of this research for people who are living with neurodegenerative diseases could be transformative.
The future of AI and neurodegenerative diseases
By harnessing machine-learning, researchers will be able to quickly recognise patterns in data. This opens doors to improved and more personalised neurodegenerative diagnoses, allowing researchers to understand how someone’s genetic background can affect the cause and course of the disease.
Predictive models could also help enable earlier and alternative treatments for people with Alzheimer’s disease.
“Pharmaceutical clinical trials in Alzheimer’s disease predominantly focus on the removal of amyloid or tau proteins in the brain, the hallmarks of Alzheimer’s disease. While some of these have demonstrated significant promise, questions arise regarding cohorts where the disease presentation deviates from typical Alzheimer’s. This often pertains to rarer forms of Alzheimer’s disease or populations with genetic profiles markedly distinct from the Western and Northern European cohorts studied to date. This is an opportunity for unconventional methods to influence the future of therapeutic target discovery for globally relevant and efficient treatment.”
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“Databricks is in a tricky spot with Naveen Rao stepping back. He was not just a figurehead, but deeply involved in shaping their AI vision, particularly after MosaicML,” said Robert Kramer, principal analyst at Moor Insights & Strategy.
“Rao’s absence may slow the pace of new innovation slightly, at least until leadership stabilizes. Internal teams can keep projects on track, but vision-driven leaps, like identifying the ‘next MosaicML’, may be harder without someone like Rao at the helm,” Kramer added.
Rao became a part of Databricks in 2023 after the data lakehouse provider acquired MosaicML, a company Rao co-founded, for $1.3 billion. During his tenure, Rao was instrumental in leading research for many Databricks products, including Dolly, DBRX, and Agent Bricks.
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NFL player props, odds: Week 2, 2025 NFL picks, SportsLine Machine Learning Model AI predictions, SGP
The Under went 12-4 in Week 1, indicating that not only were there fewer points scored than expected, but there were also fewer yards gained. Backing the Under with NFL prop bets was likely profitable for the opening slate of games, but will that maintain with Week 2 NFL props? Interestingly though, four of the five highest-scoring games last week were the primetime games, so if that holds, then the Overs for this week’s night games could be attractive with Week 2 NFL player props.
There’s a Monday Night Football doubleheader featuring star pass catchers like Nico Collins, Mike Evans and Brock Bowers. The games also feature promising rookies such as Ashton Jeanty, Omarion Hampton and Emeka Egbuka. Prop lines are usually all over the place early in the season as sportsbooks attempt to establish a player’s potential, and you could take advantage of this with the right NFL picks. If you are looking for NFL prop bets or NFL parlays for Week 2, SportsLine has you covered with the top Week 2 player props from its Machine Learning Model AI.
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Week 2 NFL props for Sunday’s main slate
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Chicago allowed 12 receivers to go Over 63.5 receiving yards last season as the Bears’ pass defense is adept at keeping opponents out of the endzone but not as good at preventing yardage. Chicago allowed the highest yards per attempt and second-highest yards per completion in 2024. While St. Brown had just 45 yards in the opener, the last time he was held under 50 receiving yards, he then had 193 yards the following week. The SportsLine Machine Learning Model projects 82.5 yards for St. Brown in a 4.5-star pick. See more Week 2 NFL props here.
Week 2 NFL props for Vikings vs. Falcons on Sunday Night Football
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Week 2 NFL props for Buccaneers vs. Texans on Monday Night Football
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Week 2 NFL props for Chargers vs. Raiders on Monday Night Football
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How to make Week 2 NFL prop picks
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“I had an interesting experience over the summer teaching an AI ethics class. You know plagiarism would be an interesting question in an AI ethics class … They had permission to use AI for the first written assignment. And it was clear that many of them had just fed in the prompt, gotten back the paper and uploaded that. But rather than initiate a sort of disciplinary oppositional setting, I tried to show them, look, what you what you’ve produced is kind of generic … and this gave the students a chance to recognize that they weren’t there in their own work. This opened the floodgates,” Feeney said.
“I think the focus should be less on learning how to work with the interfaces we have right now and more on just graduate with a story about how you did something with AI that you couldn’t have done without it. And then, crucially, how you shared it with someone else,” he continued.
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