15 papers · ranked by Valyu relevance
Matthew J. K. Vince, Kristin A. Hughes, Anastasiya Buzuk, Deborah L. Perlstein + 2 more
Machine learning (ML) is rapidly gaining traction in many areas of experimental molecular science for elucidating relationships and patterns in large or complex data sets. Historically, ML was largely the preserve of those with specialized training in fields such as statistics or cheminformatics. Increasingly, however…
Najib J. Majaj, Denis G. Pelli
Today most vision-science presentations mention machine learning. Many neuroscientists use machine learning to decode neural responses. Many perception scientists try to understand recognition by living organisms. To them, machine learning offers a reference of attainable performance based on learned stimuli. This…
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…
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…
Lopez Rene, Makita Mario, Ortega Laura, Lal Avantika + 1 more
Machine learning is a complex but essential technology in genomics data analysis and its popularity has increased the rate of new methodological approaches published but this raises the question of how models should be benchmarked and validated. Bench-ML is a generalizable and easy to use web interface for benchmarking…
Chris S Magnano, Fangzhou Mu, Rosemary S Russ, Milica Cvetkovic + 2 more
The increasing prevalence and importance of machine learning in biological research has created a need for machine learning training resources tailored towards biological researchers. However, existing resources are often inaccessible, infeasible, or inappropriate for biologists because they require significant…
Michal Bozděch
Not only in sports is a neural network the most used type of artificial intelligence. With software development, anyone can create a neural network model, but little is known about how to prepare the data and how to set up the model algorithms to their maximum performance. For these reasons, this study aims to…
Joram Soch, Carsten Allefeld
We propose the statistical modelling approach to supervised learning (i.e. predicting labels from features) as an alternative to algorithmic machine learning (ML). The approach is demonstrated by employing a multivariate general linear model (MGLM) describing the effects of labels on features, possibly accounting for…
Lucy Moctezuma, Lorena Benitez Rivera, Florentine van Nouhuijs, Faye Orcales + 4 more
This manuscript describes the development of a module that is part of a learning platform named “NIGMS Sandbox for Cloud-based Learning” https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers…
Sylvain Christin, Éric Hervet, Nicolas Lecomte
A lot of hype has recently been generated around deep learning, a group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning revolutionized several research fields such as bioinformatics or medicine. Yet such a surge of tools and…
Tomasz Konopka
Machine learning models in bioinformatics are often trained and used within the scope of a single project, but some models are also reused across projects and deployed in translational settings. Over time, trained models may turn out to be maladjusted to the properties of new data. This creates the need to improve…
Arni S.R. Srinivasa Rao, Michael P. Diamond
In this technical article, we are proposing ideas those we have been developing of how machine learning and deep learning techniques can potentially assist obstetricians / gynecologists in better clinical decision making using infertile women in their treatment options in combination with mathematical modeling in…
Carlo Dindorf, Eva Bartaguiz, Freya Gassmann, Michael Fröhlich
Artificial intelligence and its subcategories of machine learning and deep learning are gaining increasing importance and attention in the context of sports research. This has also meant that the number of corresponding publications has become complex and unmanageably large in human terms. In the current state of the…
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…
Nastasiya F. Grinberg, Ross D. King
In phenotype prediction the physical character of an organism is predicted from knowledge of its genotype and environment. Such studies are of the highest societal importance as they are now of central importance to medicine, crop-breeding, etc. We investigated two phenotype prediction problems: one simple and clean…