14 papers · ranked by Valyu relevance
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…
Matteo De Matola, Giorgio Arcara
Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come…
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…
Vlastimil Martinek, Andrea Gariboldi, Dimosthenis Tzimotoudis, Mark Galea + 7 more
Extracting knowledge from biomedical data is crucial for advancing our understanding of biological systems and developing novel therapeutics. The quantity, quality, and resolution of biomedical data constantly evolves, requiring the automation of biomedical machine learning (ML). Existing Automated ML tools lack…
Huifang Ma, Zhicheng Ji
Large language models have shown remarkable capabilities in algorithm design, but their effectiveness in solving data science challenges remains poorly understood. We conducted a classroom experiment in which graduate students used large language models (LLMs) to solve biomedical data science challenges on Kaggle.…
Akshay Akshay, Mitali Katoch, Navid Shekarchizadeh, Masoud Abedi + 5 more
Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this…
Jorge Guerra Pires
Introduction: deep learning emerged in 2012 as one of the most important machine learning technologies, reducing image identification error from 25% to 5%. This article has two goals: 1) to demonstrate to the general public the ease of building state-of-the-art machine learning models without coding expertise; 2) to…
Hung Q. Vo, Huy Q. Vo, Son T. Ly, Zhihao Wan + 5 more
Conventional tissue image analysis software provides foundational capabilities for cellular analysis, including segmentation, basic morphological feature extraction, and spatial organization analysis. However, these tools often require manual intervention and are not well integrated with code-driven automation…
Aws Ismail Abu Eid, Salameh A. Mjlae, Suzie Yaseen Rababa’h, Ahmed Hammad + 1 more
This comprehensive benchmarking study explores the performance of three prominent machine learning libraries: PyTorch, Keras with TensorFlow backend, and Scikit-learn with the same criteria, software, and hardware. The evaluation encompasses two diverse datasets, “student performance” and “College Attending Plan…
Zhe Liu, Yihang Bao, Wenhao Li, Weihao Li + 1 more
Non-coding single nucleotide polymorphisms (SNPs) are critical drivers of gene regulation and disease susceptibility, yet predicting their functional impact remains a challenging task. A variety of methods exist for encoding non-coding SNPs, such as direct base encoding or using pre-trained models to obtain embeddings.…
Byron T. Belcher, Eliana H. Bower, Benjamin Burford, Maria Rosa Celis + 13 more
Image-based machine learning methods are quickly becoming among the most widely-used forms of data analysis across science, technology, and engineering. These methods are powerful because they can rapidly and automatically extract rich contextual and spatial information from images, a process that has historically…
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…
Scott Spillias, Jacob Rogers, Fabio Boschetti, Beth Fulton + 3 more
Ecosystem models are essential for ecosystem management, but their development traditionally requires significant time and expertise, creating bottlenecks in addressing urgent environmental challenges. We present LEMMA (LLM Enabled Mechanistic Modelling for ecosystem Assessment), a framework that programmatically…
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…