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Adding context to natural language programming helps unearth unexpected connections buried in existing drug discovery literature.
Produced by Nature Research Custom Media Researchers at FRONTEO are using natural language processing, a form of artificial intelligence, to search for new drugs from information in the published research literature. Facebook
Researchers at FRONTEO are using natural language processing, a form of artificial intelligence, to search for new drugs from information in the published research literature. Artificial intelligence (AI) offers the tantalizing promise of revealing new drugs by unveiling patterns lurking in the existing research literature. But efforts to unleash AI’s potential in this area are being hindered by inherent biases in the publications used for training AI models.
By adopting an approach that mimics the strategies children use to understand unfamiliar words when encountering 1 , a Japanese company is seeking to bypass this limitation. FRONTEO Inc., an AI-solutions company, with its headquarters in Tokyo, has developed a natural language processing (NLP) model that adds a critical parameter — context — to the AI-powered analysis of research literature.
“When children encounter an unfamiliar word, they grasp its meaning by looking at the surrounding context,” says Hiroyoshi Toyoshiba, chief technology officer at FRONTEO. “Similarly, our engine automatically determines meanings based on context, without relying on pre-existing definitions.”
Promising results obtained by applying this approach hint that it could lead to ground-breaking health discoveries.
Context is king FRONTEO’s flagship AI engine, KIBIT, uses the distributional hypothesis to analyse word relationships in written texts. Formalized in the 1950s, the distributional hypothesis states that words derive their significance from their context. For example, “king” and “monarch” both appear in sentences about ruling, whereas seeing “bank” in sentences about financial institutions and rivers reveals some words have multiple interpretations.“KIBIT focuses on the ‘company’ a word keeps, the surrounding words and their distribution,” says Toyoshiba. “This allows us to identify true connections.”Refined over nearly two decades, the KIBIT engine excels at discovering relevant information from large datasets, such as legal documents, medical records and financial data. […]
AI can hunt for hidden clues to new drugs in published papers
