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Inference of directed biological networks is an important but notoriously challenging problem. We introduce , an approach to learning causal networks that leverages large-scale intervention-response data. Applied to 788 genes from the genome-wide perturb-seq dataset, helps elucidate the network architecture of blood traits.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614812 | PMC |
http://dx.doi.org/10.1101/2023.10.13.562293 | DOI Listing |
Environ Manage
September 2025
TEMSUS Research Group, Catholic University of Ávila, Ávila, Spain.
Forests have been increasingly affected by natural disturbances and human activities. These impacts have caused habitat fragmentation and a loss of ecological connectivity. This study examines potential restoration pathways that reconnect the five largest forest cores in the Castilla y León region of Spain.
View Article and Find Full Text PDFAJNR Am J Neuroradiol
September 2025
From the Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America (J.S.S., B.M., S.H., A.H., J.S.), and Department of Aerospace Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India (H.S.).
Background And Purpose: The choroid of the eye is a rare site for metastatic tumor spread, and as small lesions on the periphery of brain MRI studies, these choroidal metastases are often missed. To improve their detection, we aimed to use artificial intelligence to distinguish between brain MRI scans containing normal orbits and choroidal metastases.
Materials And Methods: We present a novel hierarchical deep learning framework for sequential cropping and classification on brain MRI images to detect choroidal metastases.
Cell Chem Biol
September 2025
iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA 92037, USA; Institute of Molecular Biology and Bio
Balanced or biased G protein and arrestin transmembrane signaling by the adenosine 2A receptor (AAR) is related to ligand-induced allosterically triggered variation of structural dynamics in the intracellular half of the transmembrane domain (TMD). F-nuclear magnetic resonance (NMR) of a network of genetically introduced meta-trifluoromethyl-L-phenylalanine (mtfF) probes in the core of the TMD revealed signaling-related structure rearrangements leading from the extracellular orthosteric drug-binding site to the G protein and arrestin contacts on the intracellular surface. The key element in this structural basis of signal transfer is dynamic loss of structural order in the intracellular half of the TMD, as manifested by local polymorphisms and associated rate processes within the molecular architecture determined previously by X-ray crystallography.
View Article and Find Full Text PDFCell Syst
September 2025
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Electronic address:
Spatial transcriptomics allows for the measurement of gene expression within the native tissue context. However, despite technological advancements, computational methods to link cell states with their microenvironment and compare these relationships across samples and conditions remain limited. To address this, we introduce Tissue Motif-Based Spatial Inference across Conditions (TissueMosaic), a self-supervised convolutional neural network designed to discover and represent tissue architectural motifs from multi-sample spatial transcriptomic datasets.
View Article and Find Full Text PDFNeural Netw
September 2025
Dept. of CSE, Konkuk University, Seoul, 05029, Republic of Korea. Electronic address:
Neural network compression problems have been extensively studied to overcome the limitations of compute-intensive deep learning models. Most of the state-of-the-art solutions in this context are based on network pruning that identify and remove unimportant weights, filters or channels. However, existing methods often lack actual speedup or require complex pruning criteria and additional training (fine-tuning) overhead.
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