We present an overview of image-processing methods for Affymetrix GeneChips. All GeneChips are affected to some extent by spatially coherent defects and image processing has a number of potential impacts on the downstream analysis of GeneChip data. Fortunately, there are now a number of robust and accurate algorithms, which identify the most disabling defects. One group of algorithms concentrate on the transformation from the original hybridisation DAT image to the representative CEL file. Another set uses dedicated pattern recognition routines to detect different types of hybridisation defect in replicates. A third type exploits the information provided by public repositories of GeneChips (such as GEO). The use of these algorithms improves the sensitivity of GeneChips, and should be a prerequisite for studies in which there are only few probes per relevant biological signal, such as exon arrays and SNP chips.
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http://dx.doi.org/10.1093/bib/bbm055 | DOI Listing |
Biol Cell
January 2025
CNRS, Univ Rennes, IGDR [(Institut de Génétique et Développement de Rennes)]-UMR 6290, Rennes, France.
Understanding the spatiotemporal organization of components within living systems requires the highest resolution possible. Microscopy approaches that allow for a resolution below 250 nm include electron and super-resolution microscopy (SRM). The latter combines advanced imaging techniques and the optimization of image processing methods.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Department of Ophthalmology, Key Lab of Ocular Fundus Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Age-related macular degeneration (AMD) represents a significant clinical concern, particularly in aging populations, and recent advancements in artificial intelligence (AI) have catalyzed substantial research interest in this domain. Despite the growing body of literature, there remains a need for a comprehensive, quantitative analysis to delineate key trends and emerging areas in the field of AI applications in AMD. This bibliometric analysis sought to systematically evaluate the landscape of AI-focused research on AMD to illuminate publication patterns, influential contributors, and focal research trends.
View Article and Find Full Text PDFCancer Med
January 2025
Department of Pediatric Surgery, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Background: Medical images play an important role in diagnosis and treatment of pediatric solid tumors. The field of radiology, pathology, and other image-based diagnostics are getting increasingly important and advanced. This indicates a need for advanced image processing technology such as Deep Learning (DL).
View Article and Find Full Text PDFCancers (Basel)
January 2025
Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Glioblastoma is the most common primary brain tumor in adult patients, and despite standard-of-care treatment, median survival has remained less than two years. Advances in our understanding of molecular mutations have led to changes in the diagnostic criteria of glioblastoma, with the WHO classification integrating important mutations into the grading system in 2021. We sought to review the basics of the important genetic mutations associated with glioblastoma, including known mechanisms and roles in disease pathogenesis/treatment.
View Article and Find Full Text PDFNeuroinformatics
January 2025
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
This paper introduces the Automated Lesion and Feature Extraction (ALFE) pipeline, an open-source, Python-based pipeline that consumes MR images of the brain and produces anatomical segmentations, lesion segmentations, and human-interpretable imaging features describing the lesions in the brain. ALFE pipeline is modeled after the neuroradiology workflow and generates features that can be used by physicians for quantitative analysis of clinical brain MRIs and for machine learning applications. The pipeline uses a decoupled design which allows the user to customize the image processing, image registrations, and AI segmentation tools without the need to change the business logic of the pipeline.
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