10 papers · ranked by Valyu relevance
Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju + 11 more
'Siddha Ganju' 'Dava Newman' 'Sujoy Ganguly' 'Danny Lange' 'Atílím Güneş Baydin' 'Amit Sharma' 'Adam Gibson' 'Stephan Zheng' 'Eric P. Xing' 'Chris Mattmann' 'James Parr' 'Yarin Gal'] The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed…
Adrian H. Zai, Mohammad Adibuzzaman, David D. McManus, Allan Walkey
While the CHAI Blueprint outlines a lifecycle for responsible AI that includes assessment, planning, development, validation, deployment, and monitoring, health systems often require terminology that more directly reflects how interdisciplinary teams conceptualize and execute their work. To support this alignment, we…
Piotr Tynecki, Arkadiusz Guziński, Joanna Kazimierczak, Michał Jadczuk + 2 more
As antibiotic resistance is becoming a major problem nowadays in a treatment of infections, bacteriophages (also known as phages) seem to be an alternative. However, to be used in a therapy, their life cycle should be strictly lytic. With the growing popularity of Next Generation Sequencing (NGS) technology, it is…
Shahadat Uddin, Stephen Ong, Haohui Lu
The analytic procedures incorporated to facilitate the delivery of projects are often referred to as project analytics. Existing techniques focus on retrospective reporting and understanding the underlying relationships to make informed decisions. Although machine learning algorithms have been widely used in addressing…
Zhaoyu Zhai, Zhewei Lin, Qiang Li, Jianbo Pan
The explosive growth of numerical biomedical data poses a challenge in uncovering meaningful insights within from vast omics and clinical data. In recent years, machine learning has emerged as a powerful tool for processing and dissecting numerical biomedical data, making it a popular choice for addressing analytical…
Qiang Gu, Anup Kumar, Simon Bray, Allison Creason + 4 more
Supervised machine learning, where the goal is to predict labels of new instances by training on labeled data, has become an essential tool in biomedical data analysis. To make supervised machine learning more accessible to biomedical scientists, we have developed Galaxy-ML, a platform that enables scientists to…
Paulo Lyra, Junhao Qiu, Khai Dang, Alyssa Pybus + 6 more
Machine learning is increasingly central to biomedical research, but using machine learning well often requires substantial computational expertise and methodological care to produce high-quality results. To make machine learning tools more accessible to biomedical researchers while supporting best-practice approaches…
Azadeh Assadi, Peter C. Laussen, Andrew J. Goodwin, Sebastian Goodfellow + 9 more
'Sebastian Goodfellow' 'William Dixon' 'Robert W. Greer' 'Anusha Jegatheeswaran' 'Devin Singh' 'Melissa McCradden' 'Sara N. Gallant' 'Anna Goldenberg' 'Danny Eytan' 'Mjaye L. Mazwi'] Background and Objectives Machine Learning offers opportunities to improve patient outcomes, team performance, and reduce healthcare…
Sandra Eloranta, Magnus Boman
The deployment of machine learning for tasks relevant to complementing standard of care and advancing tools for precision health has gained much attention in the clinical community, thus meriting further investigations into its broader use. In an introduction to predictive modelling using machine learning, we conducted…
Joshua J. Levy, A. James O’Malley
Machine learning approaches have become increasingly popular modeling techniques, relying on data-driven heuristics to arrive at its solutions. Recent comparisons between these algorithms and traditional statistical modeling techniques have largely ignored the superiority gained by the former approaches due to…