Multiple Sclerosis


Drug Discovery AI Can Do in a Day What Currently Takes Months

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To create a new drug, researchers have to test tens of thousands of compounds to determine how they interact. And that's the easy part; after a substance is found to be effective against a disease, it has to perform well in three different phases of clinical trials and be approved by regulatory bodies. It's estimated that, on average, one new drug coming to market can take 1,000 people, 12-15 years, and up to $1.6 billion. There has to be a better way--and now it seems there is. Last week, researchers published a paper detailing an artificial intelligence system made to help discover new drugs, and significantly shorten the amount of time and money it takes to do so.


Artificial intelligence could build new drugs faster than any human team

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Artificial intelligence algorithms are being taught to generate art, human voices, and even fiction stories all on their own--why not give them a shot at building new ways to treat disease? Atomwise, a San Francisco-based startup and Y Combinator alum, has built a system it calls AtomNet (pdf), which attempts to generate potential drugs for diseases like Ebola and multiple sclerosis. The company has invited academic and non-profit researchers from around the country to detail which diseases they're trying to generate treatments for, so AtomNet can take a shot. The academic labs will receive 72 different drugs that the neural network has found to have the highest probability of interacting with the disease, based on the molecular data it's seen. Atomwise's system only generates potential drugs--the compounds created by the neural network aren't guaranteed to be safe, and need to go through the same drug trials and safety checks as anything else on the market.


Flipboard on Flipboard

#artificialintelligence

Artificial intelligence algorithms are being taught to generate art, human voices, and even fiction stories all on their own--why not give them a shot at building new ways to treat disease? Atomwise, a San Francisco-based startup and Y Combinator alum, has built a system it calls AtomNet (pdf), which attempts to generate potential drugs for diseases like Ebola and multiple sclerosis. The company has invited academic and non-profit researchers from around the country to detail which diseases they're trying to generate treatments for, so AtomNet can take a shot. The academic labs will receive 72 different drugs that the neural network has found to have the highest probability of interacting with the disease, based on the molecular data it's seen. Atomwise's system only generates potential drugs--the compounds created by the neural network aren't guaranteed to be safe, and need to go through the same drug trials and safety checks as anything else on the market.


Scientists create smarter mice with 'half-human' brains

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Mice injected with human brain cells grow to have'half human brains' that make them smarter than other rodents, scientists have found. Researchers claim that giving mouse pups a type of immature human brain cell, known as glial cells, caused their brains to grow differently so they became more human-like. These human glial cells, which are the support cells of the brain providing it with structure and nutrients, multiplied and grew to replace a similar type of cell in the brains of the mice. Mice injected with human brain cells grow to have'half human brains' that make them smarter than other rodents, scientists have found. While the mice still had their own neurons - the cells that transmit and store information in the brain - the support cells were almost entirely human, according to the researchers.


Identifying Key Symptoms Differentiating Myalgic Encephalomyelitis and Chronic Fatigue Syndrome from Multiple Sclerosis

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It is unclear what key symptoms differentiate Myalgic Encephalomyelitis (ME) and Chronic Fatigue syndrome (CFS) from Multiple Sclerosis (MS). The current study compared self-report symptom data of patients with ME or CFS with those with MS. The self-report data is from the DePaul Symptom Questionnaire, and participants were recruited to take the questionnaire online. Data were analyzed using a machine learning technique called decision trees. The best discriminating symptoms were from the immune domain (i.e., flu-like symptoms and tender lymph nodes), and the trees correctly categorized MS from ME or CFS 81.2% of the time, with those with ME or CFS having more severe symptoms.