12 papers · ranked by Valyu relevance
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…
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…
JohnMark Taylor, Nikolaus Kriegeskorte
Deep neural network models (DNNs) are essential to modern AI and provide powerful models of information processing in biological neural networks. Researchers in both neuroscience and engineering are pursuing a better understanding of the internal representations and operations that undergird the successes and failures…
Dimitry Tegunov
Fourier-space projection operations are central to electron microscopy single-particle analysis and electron tomography algorithms. Machine learning methods require differentiable implementations for end-to-end model training, but PyTorch’s built-in operations are too slow for practical use. This paper introduces…
Liang Liang, Minliang Liu, John Elefteriades, Wei Sun
Finite-element analysis (FEA) is widely used as a standard tool for stress and deformation analysis of solid structures, including human tissues and organs. For instance, FEA can be applied at a patient-specific level to assist in medical diagnosis and treatment planning, such as risk assessment of thoracic aortic…
Haotian Li
Machine learning and deep learning are novel and trending approaches to solving real-world scientific problems. Graph machine learning is dedicated to performing learning methods, such as graph neural networks, on non-Euclidean data such as graphs. Molecules, with their natural graph structures, could be analyzed by…
Gabriele Orlando, Luis Serrano, Joost Schymkowitz, Frederic Rousseau
Deep learning algorithms applied to structural biology often struggle to converge to meaningful solutions when limited data is available, since they are required to learn complex physical rules from examples. State-of-the-art force-fields, however, cannot interface with deep learning algorithms due to their…
Kyle I. Harrington, Zhuowen Zhao, Jonathan Schwartz, Saugat Kandel + 4 more
We are launching a machine learning (ML) competition focused on particle picking in cryo-electron tomography (cryoET) data, a crucial task in structural biology. To support this, we have created a comprehensive suite of open-source tools to develop resources for our competition, including copick for dataset management…
Alexander T. Honkala, Sanjay V. Malhotra
Samples of closely related cells often contain substantial cell state heterogeneity, which traditional clustering-based analyses struggle to de-convolve. Archetype analysis is an alternative analysis approach that identifies a minimal convex hull enclosing all data points in a dimensionally-reduced space, such as PCA…
Olivier Codol, Jonathan A. Michaels, Mehrdad Kashefi, J. Andrew Pruszynski + 1 more
Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely…
Jonathan Edward King, David Ryan Koes
Protein structure predictions from deep learning models like AlphaFold2, despite their remarkable accuracy, are likely insufficient for direct use in downstream tasks like molecular docking. The functionality of such models could be improved with a combination of increased accuracy and physical intuition. We propose a…
Eliška Chalupová, Ondřej Vaculík, Jakub Poláček, Filip Jozefov + 2 more
The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)…