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An ecological-evolutionary perspective on the genomic diversity and habitat preferences of the .
Microb Genom
January 2025
Department of Microbiology and Molecular Genetics, Oklahoma State University, Stillwater, OK, USA.
Members of the phylum inhabit a wide range of ecosystems including soils. We analysed the global patterns of distribution and habitat preferences of various lineages across major ecosystems (soil, engineered, host-associated, marine, non-marine saline and alkaline and terrestrial non-soil ecosystems) in 248 559 publicly available metagenomic datasets. Classes , , and were highly ubiquitous and showed a clear preference to soil over non-soil habitats, while classes and showed preferences to non-soil habitats.
View Article and Find Full Text PDFDiagnostic Decision-Making Variability Between Novice and Expert Optometrists for Glaucoma: Comparative Analysis to Inform AI System Design.
JMIR Med Inform
January 2025
Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.
Background: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experience may depend more on extensive data, comparisons, and external guidance. Understanding these variations is important for developing artificial intelligence (AI) systems that can effectively support optometrists with varying degrees of experience and minimize decision inconsistencies.
Objective: The main objective of this study is to identify and analyze the variations in diagnostic decision-making approaches between novice and expert optometrists.
Tensor Network Computations That Capture Strict Variationality, Volume Law Behavior, and the Efficient Representation of Neural Network States.
Phys Rev Lett
December 2024
California Institute of Technology, Division of Chemistry and Chemical Engineering, Pasadena, California 91125, USA.
We introduce a change of perspective on tensor network states that is defined by the computational graph of the contraction of an amplitude. The resulting class of states, which we refer to as tensor network functions, inherit the conceptual advantages of tensor network states while removing computational restrictions arising from the need to converge approximate contractions. We use tensor network functions to compute strict variational estimates of the energy on loopy graphs, analyze their expressive power for ground states, show that we can capture aspects of volume law time evolution, and provide a mapping of general feed-forward neural nets onto efficient tensor network functions.
View Article and Find Full Text PDFPrediction of gene expression using histone modification patterns extracted by Particle Swarm Optimization.
Bioinformatics
January 2025
Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, 37099, Germany.
Motivation: Histone modifications play an important role in transcription regulation. Although the general importance of some histone modifications for transcription regulation has been previously established, the relevance of others and their interaction is subject to ongoing research. By training Machine Learning models to predict a gene's expression and explaining their decision making process, we can get hints on how histone modifications affect transcription.
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