Mouse sitting among pills
Article
Johnson & Johnson Innovative Medicine

Data science: empowering animal-free progress in preclinical development

Many drugs fail clinical trials, often because preclinical animal models fall short of replicating human physiology. To improve animal welfare, speed up drug development, and reduce costs, we need to rely less on animal models, while also minimizing the number of failures early in the drug development process. Artificial intelligence and machine learning are powerful tools that can help us achieve these goals by predicting a drug’s efficacy, safety, and uptake in preclinical studies. These technologies can help researchers to make informed decisions and optimize testing strategies, improving drug development for both animals and people.

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Janssen logo and picture of people helping each other up a cliff
Article
Johnson & Johnson Innovative Medicine

Data science – taking clinical drug development to the next level

Clinical drug development is a challenging endeavor, but help is at hand! From trial site selection, to patient recruitment, to endpoint characterization – data science integration can help to overcome bottlenecks and improve efficiency in clinical development by generating unique insights to help guide study design and operations. For this strategy to be used to its full potential in Belgium, local partners should optimize data governance and quality, and improve collaboration. This would help the country maintain its status as a leading location for clinical trials.

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Woman looking at red pill with scientific symbols
Opinion
Johnson & Johnson Innovative Medicine

Data science in early drug discovery – getting it right from the start

The application of data science in the early stages of drug development is not new – progress in algorithms and computing power has been ongoing for years. We have reached the point where we have to reflect on the road travelled and look forward to upcoming opportunities and challenges. To further pave the way and reach the top in health data science, stakeholders will have to find each other and work together. Once everyone is on board, data science knows no bounds!

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Business poeple and doctors working together
Press release
BioVox

Belgian stakeholders unite to strive for data science integration in healthcare

Data science is booming, including in the healthcare sector. However, in order to extract insights and benefits from our health data, we first have to build a solid system for structural data processing and management. To frame the needs for healthcare data reuse in Belgium, Inovigate has united stakeholders and summarized their recommendations in two white papers.

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Christine Durinx shaking hands with Minister Hilde Crevits
Opinion
BioVox

VIB: a new focus on data science with Christine Durinx at the helm

Christine Durinx has been appointed as the new co-Managing Director of the world-class life sciences research institute VIB. Taking over from Jo Bury, Durinx joins VIB’s other co-Managing Director Jérôme Van Biervliet in ushering in a new era of discoveries increasingly driven by data science.

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Woman holding heart with binary code
Opinion
BioVox

Why keeping up with data science matters for our health

Data without science is nothing; just 1s and 0s, floating around a cloud waiting for someone to make sense of them. Data science is the process of extracting value from data, using advanced analytics tools. Enormous amounts of health information are being gathered every second, and we are rapidly getting better at decoding it: turning bytes into insights that can be used to improve the lives of patients. But the pace, methods and ethics of data science adoption varies dramatically between countries and regions. Why should we care about keeping up?

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Article
Johnson & Johnson Innovative Medicine

A Guide to the OMOP Common Data Model

The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is a standardized framework designed by the Observational Health Data Sciences and Informatics (OHDSI) community. This open-science community aims to improve the quality of healthcare by providing guidelines for a more harmonized approach to data science.

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Article
Johnson & Johnson Innovative Medicine

Using patient data to enable personalized healthcare, ATHENA shows us how!

Personalized healthcare is characterized by tailoring treatments to each individual patient’s needs. Despite evident benefits, implementing such a system is not straightforward. The ATHENA project consortium has successfully devised a set of building blocks to address the challenges associated. Both technological and governance tools were developed that can now be used on a larger scale to catalyze the transition towards personalized medicine and care.

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Opinion
V-Bio Ventures

Fraud in scientific institutes: a crack in the fabric of science

The pursuit of scientific knowledge is at the heart of human progress – it leads to ground-breaking discoveries that have transformed our understanding of the world and our place within it. However, this noble pursuit is not without its blemishes. Scientific fraud – the deliberate misrepresentation of data or results to deceive the scientific community – poses a serious threat to the integrity of the scientific enterprise in both academia and industry. So, what can we do about it?

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Article
Johnson & Johnson Innovative Medicine

Optimizing the patient care pathway through data-driven insights

The patient’s journey in a healthcare setting is influenced by factors beyond the mere medical aspects of the case. Local variations in care procedures among hospitals and caregivers enable benchmarking and adaptation of practices to optimize outcomes. For effective process modeling, it’s essential to collect data and extract insights. To optimize this process efficiently, collaborative efforts should explore alternative data sources and implement novel tools.

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Article
BioVox

Smart cities as living laboratories: a data-driven strategy for creating a sustainable future

Similar to the growing use of large-scale scientific data to guide laboratory research, urban data presents significant potential for informed decision-making in city governance. Extensive data on waste collection, traffic, pollution, and various other facets of city management can be collected and analyzed to help policymakers identify challenges and develop prediction-based solutions. Thomas Van Oppens, Deputy Mayor of Leuven, underscores the importance of local collaboration in establishing such a data-driven growth model for city governance.

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