10 Ways Predictive Analytics Improves Innovation - InformationWeek

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3/12/2016
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Lisa Morgan
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10 Ways Predictive Analytics Improves Innovation

From drug discovery to price optimization, across virtually every industry, more companies are using predictive analytics to increase revenue, reduce costs, and modernize the way they do business. Here are some examples.
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Disrupt An Industry 

Drug discovery has been done the same way for decades, if not centuries. Researchers have a hypothesis-driven target, screen that target against chemical compounds, and then iteratively take them through clinical trials. As history has shown, a lot of trial and error is involved, perhaps more than is necessary, particularly in this day and age. According to industry association PhRMA, it takes an average of more than 10 years and $2.6 billion to develop a drug. Pharmaceutical company BERG Health aims to change that. It is using predictive analytics and artificial intelligence (AI) to discover and develop lifesaving treatments. 
'There's no way a human can process the amount of data necessary to dissect the complexity of biology and disease into form-based discovery,' said Niven Narain, founder and CEO of BERG. 'We use human tissue samples to learn about as many biological components as we can and we include that patient's clinical and demographic data.' 
Its platform builds a model of healthy individuals and then compares that to individuals with a disease. The AI then builds a model of the genes and proteins that pinpoints the core differences between health and disease. The model helps BERG target its drug discovery process. The company also uses the same process to identify which patients are the best candidates for a certain drug. 
Using a single tissue sample, its platform can create more than 14 trillion data points that collectively become a 'patient signature.' The patient signature indicates whether or not the individual will likely respond well to a treatment that, for example, is far more precise than first-line pancreatic cancer treatment. First-line pancreatic treatments fail 90% of the time, Narain said. 
(Image: bykst via Pixabay)

Disrupt An Industry

Drug discovery has been done the same way for decades, if not centuries. Researchers have a hypothesis-driven target, screen that target against chemical compounds, and then iteratively take them through clinical trials. As history has shown, a lot of trial and error is involved, perhaps more than is necessary, particularly in this day and age. According to industry association PhRMA, it takes an average of more than 10 years and $2.6 billion to develop a drug. Pharmaceutical company BERG Health aims to change that. It is using predictive analytics and artificial intelligence (AI) to discover and develop lifesaving treatments.

"There's no way a human can process the amount of data necessary to dissect the complexity of biology and disease into form-based discovery," said Niven Narain, founder and CEO of BERG. "We use human tissue samples to learn about as many biological components as we can and we include that patient's clinical and demographic data."

Its platform builds a model of healthy individuals and then compares that to individuals with a disease. The AI then builds a model of the genes and proteins that pinpoints the core differences between health and disease. The model helps BERG target its drug discovery process. The company also uses the same process to identify which patients are the best candidates for a certain drug.

Using a single tissue sample, its platform can create more than 14 trillion data points that collectively become a "patient signature." The patient signature indicates whether or not the individual will likely respond well to a treatment that, for example, is far more precise than first-line pancreatic cancer treatment. First-line pancreatic treatments fail 90% of the time, Narain said.

(Image: bykst via Pixabay)

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