Medical imaging analysis depends on the reproducibility of complex computation. Linux containers enable the abstraction, installation, and configuration of environments so that software can be both distributed in self-contained images and used repeatably by tool consumers. While several initiatives in neuroimaging have adopted approaches for creating and sharing more reliable scientific methods and findings, Linux containers are not yet mainstream in clinical settings. We explore related technologies and their efficacy in this setting, highlight important shortcomings, demonstrate a simple use-case, and endorse the use of Linux containers for medical image analysis.
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http://dx.doi.org/10.1007/s10278-018-0089-4 | DOI Listing |
BMC Bioinformatics
January 2025
Biology Department, University of Massachusetts Amherst, Amherst, MA, USA.
Background: High-throughput behavioral analysis is important for drug discovery, toxicological studies, and the modeling of neurological disorders such as autism and epilepsy. Zebrafish embryos and larvae are ideal for such applications because they are spawned in large clutches, develop rapidly, feature a relatively simple nervous system, and have orthologs to many human disease genes. However, existing software for video-based behavioral analysis can be incompatible with recordings that contain dynamic backgrounds or foreign objects, lack support for multiwell formats, require expensive hardware, and/or demand considerable programming expertise.
View Article and Find Full Text PDFEvolutionary sparse learning (ESL) uses a supervised machine learning approach, Least Absolute Shrinkage and Selection Operator (LASSO), to build models explaining the relationship between a hypothesis and the variation across genomic features (e.g., sites) in sequence alignments.
View Article and Find Full Text PDFBioinformatics
December 2024
Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States.
Summary: Time-lapse 3D imaging is fundamental for studying biological processes but requires software able to handle terabytes of voxel data. Although many multidimensional viewing applications exist, they mostly lack support for heterogeneous voxel counts, datatypes, and modalities in a single timeline. Open Chrono-Morph Viewer provides a straightforward graphical user interface to quickly investigate multi-timescale datasets represented as separate volume files in the common NRRD format for compatibility between toolchains.
View Article and Find Full Text PDFJ Chem Inf Model
December 2024
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm 10044, Sweden.
Although being able to determine whether a host molecule can enclose a guest molecule and form a caging complex could benefit numerous chemical and medical applications, the experimental discovery of molecular caging complexes has not yet been achieved at scale. Here, we propose MoleQCage, a simple tool for the high-throughput screening of host and guest candidates based on an efficient robotics-inspired geometric algorithm for molecular caging prediction, providing theoretical guarantees and robustness assessment. MoleQCage is distributed as Linux-based software with a graphical user interface and is available online at https://hub.
View Article and Find Full Text PDFData Brief
December 2024
Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND, United States.
Deep learning-based weed detection data management involves data acquisition, data labeling, model development, and model evaluation phases. Out of these data management phases, data acquisition and data labeling are labor-intensive and time-consuming steps for building robust models. In addition, low temporal variation of crop and weed in the datasets is one of the limiting factors for effective weed detection model development.
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