{"id":272,"date":"2022-01-01T12:00:37","date_gmt":"2022-01-01T12:00:37","guid":{"rendered":"http:\/\/blogs.kent.ac.uk\/kei-case-studies\/?p=272"},"modified":"2023-06-30T09:56:17","modified_gmt":"2023-06-30T08:56:17","slug":"272","status":"publish","type":"post","link":"https:\/\/blogs.kent.ac.uk\/kei-case-studies\/2022\/01\/01\/272\/","title":{"rendered":"TRN: Investigating the feasibility of extracting academic profile information from web sources"},"content":{"rendered":"

The Research Network (TRN)<\/a>\u00a0is based in Kent and provides pharmaceutical R&D consultancy, project management, outsourcing, training and due diligence services. Their expertise in drug discovery and development disciplines covers both small molecule and biotherapeutics. They regularly collaborate with universities and research organisations to progress new drug discovery ventures from lead identification through to early clinical development.<\/p>\n

The project between TRN and\u00a0University of Kent<\/a>\u00a0academics was\u00a0funded through EIRA\u2019s Innovation Voucher scheme<\/a>. It explored whether it was possible to use data mining techniques to extract accurate profile information of potential collaborators. This study was conducted to investigate the feasibility of mining academic information, which would then be used to find similarities between academics who could potentially collaborate in the future.<\/p>\n

The Challenge<\/span><\/h2>\n

Finding the right kind of scientific expertise needed to contribute to cutting edge drug discovery research presents a huge challenge. Information on expertise which could be utilised is often dispersed and comes from disparate sources, such as a company website or university profile pages. This project explored the possibility of using data mining techniques to extract academic profile information, providing a simple and effective way of shortlisting potential collaborators.<\/p>\n

The Approach<\/h2>\n

University of Kent academics\u00a0Dr James Bentham<\/a>\u00a0and\u00a0Dr Jennifer Hiscock<\/a>\u00a0are experts in the fields of machine learning, data extraction and bioscience.<\/p>\n

Their approach to the project was:<\/p>\n

    \n
  1. To identify potential sources of information on academics and their collaborations<\/li>\n
  2. To carry out pilot web scraping (data extraction)<\/li>\n
  3. To consider data storage and access requirements<\/li>\n
  4. To carry out pilot pre-processing and data mining<\/li>\n<\/ol>\n

    Various websites were investigated to identify salient attributes which could then be extracted, stored and used as the basis of data mining potential scientific investigators.<\/p>\n

    One way of identifying individual academics and areas of research was to generate lists of university departments, which could then be used for further data gathering. Webscraping was carried out for five universities:<\/p>\n