Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene e...
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Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.
Ubiquitous applications use context information to provide services and relevant information for their users. On the other hand, in Software Product Line approaches, commonality and variability of a system family shou...
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Ubiquitous applications use context information to provide services and relevant information for their users. On the other hand, in Software Product Line approaches, commonality and variability of a system family should be identified and documented through variability modeling. Thus, one of the challenges to build Context-Aware Product Lines, called Dynamic Software Product Lines, is the consistent representation of context information that influences the variability model. This work proposes the use of UML profiles and OCL to formalize and represent variability and context concepts in a consistent manner.
作者：
Dr. Katherine Camacho Carr CNM, phdCynthia L. Farley CNM, phdKatherine Camacho Carr
CNM PhD FACNM is an Associate Professor at Seattle University School of Nursing and a certified nurse-midwife practicing in Seattle WA at Highline Midwifery & Women's Health. She has been a faculty member of several midwifery education programs over the past 12 years focusing on Web-based and distance learning. Cynthia L. Farley
CNM PhD is a certified nurse-midwife and Director of Greene Midwifery Care Fairborn OH. She is also faculty in Philadelphia University's Master's of Science in Midwifery program an asynchronous distributive computer-based curriculum.
The use of computer-based instructional technologies has become almost ubiquitous in education, including health professions education. In addition, clinical practice requires the use of digital information, networkin...
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The use of computer-based instructional technologies has become almost ubiquitous in education, including health professions education. In addition, clinical practice requires the use of digital information, networking, and continued learning. Faculty, students, and clinicians must be prepared to use computer-based technologies and telecommunications. Many faculty members in the health professions need assistance with the transfer of traditional (campus-based) courses or course material to the World Wide Web. This article presents the instructional design and pedagogical aspects of redesigning a traditional campus-based course for the World Wide Web. Specific steps and a template are identified. The development of a virtual community of learners is also discussed as a critical element of on-line teaching and learning.
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