Latest Articles
Three Ways to Turn Life Science Suppliers into Partners
ArticleA growing number of life science businesses are turning to greater supply chain collaboration for benefits like accelerated time to market, improved quality, reduced risk and more rapid and widespread innovation. But while 68% of executives in this industry say active and meaningful engagement with suppliers is essential to success, far too many, over a third, struggle to implement it.
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How Can Machine Learning Improve Surgery?
ArticleMachine learning modeling is one of the most eagerly adopted technologies across healthcare. An important technology in this area is robot-assisted surgery, where the hope is that AI’s rapid evolution will soon allow machine learning models to enhance current surgical practice. This article reviews the current and close future applications of machine learning in burn surgery and microsurgery.
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Advanced Data Analytics in Pharma: Going Beyond Patterns to Understand the “Why”
ArticleDevelopments in pharmaceuticals have made new treatments available, enhancing quality of life for patients. Advanced data analytics solutions mean treatments are more effective and affordable, and less intrusive. However, these evolutions will mean major changes in how companies function requiring new capabilities for operations and supply chain.
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Helping Analytical Chemistry Embrace Big Data
ArticleMany recent advances in research have aimed to maximize the amount of data we can produce. But handling all that data is a challenge, and in analytical chemistry, data has more complexity and value than everyday spreadsheets, and tools matching that complexity will be needed to get data back into shape. We discussed how the field should approach these challenges with Andrew Anderson and , Graham McGibbon of Toronto-based analytical software supplier ACD/Labs.
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Why Pharma Is Rethinking Its Data Integration Strategy – the Criticality of Data Integration
ArticleThe pharma industry is being disrupted in multiple ways. Data has never been more accessible and the speed at which it is flowing has left the industry reeling. Fundamental to the success of these advances is the need to integrate data from diverse sources and leverage predictive analytics to drive informed, real-time decisions. This article explores how pharma can maximize the value of different data types to advance research.
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Is It Possible to Have a Google for EHR?
ArticleScanning through medical records at the speed of light is currently just a dream. In the future we can hope for a Google-like tool for EHR.
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AI Is a New Must for Medical Software
ArticleFrom decluttering waiting rooms to assisting diagnostics, AI is changing the face of healthcare. In this article, Yaroslav Kuflinski looks over some of the software that is moving medicine forward.
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Team Work Helps Overcome Environmental Challenges
ArticleWe spoke to Andrew Howley from Adventure Scientists,a pioneering not-for-profit organization that seeks to unite skilled adventurers with scientists keen to receive valuable data from remote areas, to learn more about the initiative and the impact their projects are having in the scientific community and beyond.
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Data Mining Techniques: From Preprocessing to Prediction
Article
If you work in science, chances are you spend upwards of 50% of your time analyzing data in one form or another.However, it's easy to get lost when it comes to the question of what techniques to apply to what data. This is where data mining comes in - put broadly, data mining is the utilization of statistical techniques to discover patterns or associations in the datasets you have. Here we provide an overview of the critical steps you'll need to get the most out of your data analysis pipeline.
One-Way vs Two-Way ANOVA: Differences, Assumptions and Hypotheses
ArticleA key statistical test in research fields including biology, economics and psychology, Analysis of Variance (ANOVA) is very useful for analyzing datasets. It allows comparisons to be made between three or more groups of data. Here, we summarize the key differences between these two tests, including the assumptions and hypotheses that must be made about each type of test.
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