Computers in biology and medicine
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Deep learning has emerged as a leading machine learning tool in object detection and has attracted attention with its achievements in progressing medical image analysis. Convolutional Neural Networks (CNNs) are the most preferred method of deep learning algorithms for this purpose and they have an essential role in the detection and potential early diagnosis of colon cancer. In this article, we hope to bring a perspective to progress in this area by reviewing deep learning practices for colon cancer analysis. ⋯ We conclude our work with a summary of recent deep learning practices for colon cancer analysis, a critical discussion of the challenges faced, and suggestions for future research. This study differs from other studies by including 135 recent academic papers, separating colon cancer into five different classes, and providing a comprehensive structure. We hope that this study is beneficial to researchers interested in using deep learning techniques for the diagnosis of colon cancer.
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This paper continues a recent study of the spike protein sequence of the COVID-19 virus (SARS-CoV-2). It is also in part an introductory review to relevant computational techniques for tackling viral threats, using COVID-19 as an example. Q-UEL tools for facilitating access to knowledge and bioinformatics tools were again used for efficiency, but the focus in this paper is even more on the virus. ⋯ However compounds like emodin that inhibit SARS entry, apparently by binding ACE2, might also have functions at several different human protein binding sites. The enzyme 11β-hydroxysteroid dehydrogenase type 1 is again argued to be a convenient model pharmacophore perhaps representing an ensemble of targets, and it is noted that it occurs both in lung and alimentary tract. Perhaps it benefits the virus to block an inflammatory response by inhibiting the dehydrogenase, but a fairly complex web involves several possible targets.
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The Arden Syntax for Medical Logic Modules is a language for encoding medical knowledge bases that consist of independent modules. The Arden Syntax has been used to generate clinical alerts, diagnostic interpretations, management messages, and screening for research studies and quality assurance. ⋯ Most MLMs are triggered by clinical events, evaluate medical criteria, and, if appropriate, perform an action such as sending a message to a health care provider. This paper provides a detailed tutorial on how to write MLMs.
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In implementing a clinical event monitor (CEM), a decision-support system, we worked with an existing repository of clinical data (Keystone), fed from ancillary systems using HL7. The rules are written in the Arden Syntax, an ASTM standard for expressing medical knowledge as medical logic modules (MLMs). ⋯ Overall, less than a quarter of the development effort has gone into the Arden compiler and interpreter; the rest has focused on accessing the data and integrating with other systems. We feel that the Arden Syntax has proved its worth in writing rules; effort should now be focused on medical vocabularies and data models.