The post Navigating Networks of Oncology Biomarkers appeared first on Digital Science.

IDENTIFYING GLOBAL EXPERTISE IN CAR-T

In this snapshot you will find global trends in Car-T research investment, research and innovation across a 10-year time span. We also use our NLP search technology to surface experts at the topic intersection of Car-T and solid tumor research using our unique database of awarded grants some running up to 2023 for our top expert Carl June – so what is funded now could indicate what becomes effective research in the near future.

Download Now

The post Identifying Global Expertise in CAR-T appeared first on Digital Science.

Scientific portfolio analysis

The breadth of Dimensions data and the extensive networks of linkages facilitates robust portfolio analysis. Beyond determining relative strengths and identifying gaps, our analysts use our data to provide a view across your portfolio to improve efficiency and effectiveness including:

Situational awareness

Digital Science’s Dimensions platform is an ideal foundation for helping you to maintain situational awareness about rapidly evolving activities in research.  The COVID-19 pandemic is just the latest in examples where agencies need to have real-time updates about scientific innovations.

Program evaluation

Not only is Dimensions the largest collection of research data, our unique reach expands beyond publications to include grants, patents, clinical trials, policy documents, and data sets, providing a more complete picture of activity and impact. Leveraging the power of our data, Digital Science’s expert analysts work with you and your stakeholders on comprehensive, collaborative program analysis to provide a thorough, custom evaluation of current programs and actionable recommendations to improve effectiveness of future endeavors. 

Horizon scanning

Digital Science’s Dimensions platform is an ideal foundation for Horizon Scanning, supporting governments and funders around the world in identifying the ‘unknown unknowns’. With hundreds of millions documents and billions of relationships, Dimensions is the largest corpus of data relevant to Horizon Scanning of science and technology. Dimensions includes data that captures scientific ideas at their inception as they’re proposed by researchers for funding, as well as data sources that can indicate trends and innovation in the commercial sector.

Various features of the data, such as document type, dates, and categories, provide easy-to-use facets to define a corpus of interest for a particular study. Dimensions data can be accessed in multiple complementary ways to support the identification of weak and early signals, trends, and oddities that may be indicative of opportunities or threats. Our data supports standard and validated indicators for horizon scanning, as well as the experimentation and validation of novel indicators. 

Landscape assessment

Research is continually evolving and dynamics within and between fields change constantly. Landscape assessments ensure you understand the lay of the land and are prepared to adapt to change. Our analysts use Dimensions to help you:

• Identify major global research areas.
• Understand how scientific fields are changing. 
• Assess emerging trends within a specific field of study. 
• Recognize the experts in these fields.
• Who are the top funders? Top research organizations? Who is collaborating?

Technology watch

Dimensions is also the ideal foundation for Technology Watch, supporting governments and funders around the world carrying out strategic assessments of current, emerging, and future technologies pertinent to their mission. With a focus on the ‘known unknown’, technology watch frequently complements horizon scanning activity which is focused on the ‘unknown unknown’. Dimensions includes data that extends across traditional data silos, including:

• Grants: pre-research ideas
• Preprints: research-in-progress
• Publications: published research
• Patents, Policy Documents, Clinical Trials: research applications

Various features of the data provide easy-to-use facets to define a corpus of interest for a particular study. Dimensions data can be accessed in multiple complementary ways to support analysts in observing, tracking, and assessing potential technologies without the traditional confines of narrow types of research activity.

Impact assessment

Impact is the cornerstone of many institutional missions. Impact is no longer seen as simply the volume of outputs or interventions; it includes long-term effects on populations, communities, the environment, research fields, and researchers themselves. Our analysts help you understand whether your impact is delivering on your mission, including: 

• Understanding your impact within a field of study. 
• Identifying publications from your research that became prominent, widely cited, additions to the literature corpus of your field 
• Highlighting work that has been picked up by the media. 
• Linking research to policy development
• Determining whether your funding kept researchers in the field and whether it has  impacted the careers of your researchers. 
• Comparing impact of our funding to that of others.

Topical analysis

Increasing volumes of research outputs and data are a treasure trove of information if you have the time, tools, and expertise to make sense of it. Topical analysis leverages NLP, text mining, machine learning, coding, classification, and other techniques to perform top-down and bottom-up views of what is happening in research, from broad topics areas like cancer or engineering, to granular emerging areas first mentioned a week ago. Our data, tools, and analysts can help you separate the signals from the noise, and get reliable precision/recall balanced data sets and views of any topic in global research.

Emerging Technology

Digital Science’s Dimensions platform is an ideal foundation to support your team in understanding the opportunities and threats posed by emerging technologies. With hundreds of millions documents and billions of relationships, Dimensions offers you a one-stop-shop to uncover emerging technologies wherever they occur — in the lab, in the commercial sector, in your own city, or on the other side of the world. Dimensions can help you conquer challenges of technical jargon (e.g. blockchain vs distributed ledger) and identify (even predict!) novel applications of rapidly emerging technologies. 

The post Advanced Analysis appeared first on Digital Science.

In this paper published in Frontiers Research Metrics and Analytics, Daniel W Hook, Simon J Porter, Hélène Draux and Christian T Herzog propose the concept of “real-time” bibliometrics as a new capability for researchers, policymakers and analysts across the sector.

The COVID-19 crisis has highlighted a different set of needs to analyze trends in scholarship as they occur: Real-time bibliometrics. The combination of full-text search, daily data updates, a broad set of scholarly objects including pre-prints and a wider set of data fields for analysis, broadens opportunities for a different style of analysis. A subset of these emerging capabilities is discussed and three basic analyses are presented as illustrations of the potential for real-time bibliometrics.

Read more

The post Dimensions as a Resource for Analyzing COVID-19 appeared first on Digital Science.

Today’s chapter of our NLP blog series is written by Andrii Buvailo. Andrii is a co-founder and director at BPT Analytics, and also Editor at BiopharmaTrend.com, responsible for all content, analytics, and product development in the project. He has been writing about research and business trends in the pharmaceutical industry for over four years, mainly focusing on the digital transformation of drug discovery. Before moving to the pharma space, Andrii held a number of executive positions in various hi-tech companies. Prior to his industrial career, he spent years as a practising scientist, having participated in numerous research projects in Belgium, Germany, the United States, and Ukraine. Andrii holds a BSc and an MSc in Inorganic Chemistry, and a PhD in Physical Chemistry from Kyiv National Taras Shevchenko University. Outside his professional career, Andrii is a big fan of travel, chess, and digital drawing.

See more posts in the NLP blog series

Building an initial knowledge base about the pharmaceutical industry

BPT Analytics project started back in 2016 with a simple drug discovery market research blog at BiopharmaTrend.com, where we posted our own regular observations about innovations and technology trends in the pharmaceutical industry, focusing on what companies did to advance the field. At that time we started a systematic effort of collecting data about as many drug discovery and biotech companies and startups as we possibly could. The idea was to create a large enough database of properly labelled companies to see if we would be able to later train machine learning models on it. In 2019 we were awarded a Catalyst Grant to advance our efforts.

Today, we already have a database of more than 7,000 pharma/biotech companies and over 3,000 investors active in the area. The list of companies is matched with numerous other databases and information resources, including clinical trials, marketed drugs, research papers and patents, funding rounds, R&D partnerships, and other aspects important to understand each company’s role and position in the pharmaceutical landscape. We gather data from numerous sources, including our web-parsing engine, collections with external APIs, data supplied by users, and data collected manually.

Importantly, we have built the infrastructure to manually curate the inflowing data by our freelancers. This process allowed us to accelerate and scale up our manual data curation effort. In order for this data to become useful for the pharmaceutical professional and other decision-makers, we are building a subscription-based web-interface BPT Analytics where users can conduct their own market research using our data, with advanced filters and powerful visualization tools. The interface is currently in private beta testing mode for basic functionality.

On the horizon: using NLP to automate ontology construction 

While well-organised manual data curation is one way to build a useful market intelligence service for the pharma industry, it is certainly a limited value proposition. For example, our search is limited to exact keyword-based indexing, without any semantic search options. It means that we can only find information using exact terms and parameters. If a document contains a slight variation of the same term, our search will not be able to find that document.

Another limitation is that all labelling has to be done manually for each entity, and all entities have to be manually associated in the database, which is extremely resource-demanding and inefficient.  In order to provide a new level of data mining capabilities for our future customers, we are now exploring ways to apply natural language processing (NLP) technologies in our project. Some of the key tasks that we are hoping to solve by implementing NLP models is to be able to automate domain-specific entity recognition – identifying biotech companies, drugs, diseases, therapeutic modalities etc. – out of vast amounts of mostly unstructured data, and grouping them by a number of requirements.

In time this means we will be able to extract relations between the entities and build knowledge graphs; a key component in being able to understand the pharmaceutical R&D market and derive macro- and micro-trends and business insights for the user.

Challenges to overcome

Integrating NLP models into the existing project is a tricky endeavour, and we will need to expand our expertise substantially to achieve this goal. We have unique domain-specific expertise in the life sciences industry and biotech market, a large corpus of quality data to train models. We are now exploring our potential customers’ needs to formulate use cases, and pipeline requirements for the NLP system and its output.

See more posts in the NLP blog series

The post Using NLP to Build a Market Intelligence Platform for the Biotech Industry appeared first on Digital Science.

Disease types associated with NAFLD present a tremendous research and development challenge for life sciences organizations and also a great opportunity for them to create value and innovation. The NASH market alone was valued at $1,179 million in 2017, and is expected to reach $21,478 million by 2025, growing at a CAGR of 58.4% from 2021 to 2025.

The graphic shows some examples of insights in non-alcoholic fatty liver disease (NAFLD) and its common disease type NASH (non-alcoholic steatohepatitis) extracted from our Dimensions platform. Digital Science offers the data capabilities to help information services leaders, discovery scientists, CI analysts, knowledge management, partnering and business development professionals uncover insights and expertise in disease-related topics and key indication areas.

To find out more, or to arrange a demo, get in touch.

Download Now

The post Pinpointing Global Expertise in Non-Alcoholic Fatty Liver Disease (NAFLD) appeared first on Digital Science.