Chromosome segmentation is a crucial analyzing task in karyotyping, a technique used in experiments to discover chromosomal abnormalities. Chromosomes often touch and occlude with each other in images, forming various chromosome clusters. The majority of chromosome segmentation methods only work on a single type of chromosome cluster.
View Article and Find Full Text PDFBackground: Caries are common, especially in economically undeveloped countries with limited access to medical resources. Sometimes patient cannot even realize that they have oral problems until they feel obvious pain. Deep convolutional neural networks (CNNs) have been widely adopted for medical image analysis and management and have yielded some progress in stomatology while the endoscopes are cheap and easily used in daily life for families or other non-medical situations.
View Article and Find Full Text PDFIn common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase.
View Article and Find Full Text PDFIntegrative analysis of multi-omics data can elucidate valuable insights into complex molecular mechanisms for various diseases. However, due to their different modalities and high dimension, utilizing and integrating different types of omics data suffers from great challenges. There is an urgent need to develop a powerful method to improve survival prediction and detect functional gene modules from multi-omics data.
View Article and Find Full Text PDFIEEE Trans Med Imaging
December 2020
Chromosome enumeration is an essential but tedious procedure in karyotyping analysis. To automate the enumeration process, we develop a chromosome enumeration framework, DeepACEv2, based on the region based object detection scheme. The framework is developed following three steps.
View Article and Find Full Text PDFNONCODE is a comprehensive database that aims to present the most complete collection and annotation of non-coding RNAs, especially long non-coding RNAs (lncRNA genes), and thus NONCODE is essential to modern biological and medical research. Scientists are producing a flood of new data from which new lncRNA genes and lncRNA-disease relationships are continually being identified. NONCODE assimilates such information from a wide variety of sources including published articles, RNA-seq data, micro-array data and databases on genetic variation (dbSNP) and genome-wide associations (GWAS).
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