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Full-Text Articles in Biomedical Engineering and Bioengineering
Artificial Image Objects For Classification Of Breast Cancer Biomarkers With Transcriptome Sequencing Data And Convolutional Neural Network Algorithms, Xiangning Chen, Daniel G. Chen, Zhongming Zhao, Justin M. Balko, Jingchun Chen
Artificial Image Objects For Classification Of Breast Cancer Biomarkers With Transcriptome Sequencing Data And Convolutional Neural Network Algorithms, Xiangning Chen, Daniel G. Chen, Zhongming Zhao, Justin M. Balko, Jingchun Chen
School of Medicine Faculty Publications
Background: Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expression between individual genes. Methods: We proposed a method to transform RNA sequencing data into artificial image objects (AIOs) and applied convolutional neural network (CNN) algorithms to classify these AIOs. With the AIO technique, we considered each gene as a pixel in an image and its expression level as pixel intensity. Using the GSE96058 (n = 2976), GSE81538 (n = 405), and GSE163882 (n = …
Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han
Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han
Public Health Faculty Publications
The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …
Enhancement Of Viable Adipose-Derived Stem Cells In Lipoaspirate By Buffering Tumescent With Sodium Bicarbonate, Ashish Francis Md, Wei Z. Wang Md, Joshua J. Goldman Md, Xin-Hua Fang Mt, Shelley J. Williams Ms, Richard C. Baynosa Md; Facs
Enhancement Of Viable Adipose-Derived Stem Cells In Lipoaspirate By Buffering Tumescent With Sodium Bicarbonate, Ashish Francis Md, Wei Z. Wang Md, Joshua J. Goldman Md, Xin-Hua Fang Mt, Shelley J. Williams Ms, Richard C. Baynosa Md; Facs
School of Medicine Faculty Publications
Background: Fat grafting is a growing field within plastic surgery. Adipose-derived stem cells (ASCs) and stromal vascular fracture (SVF) may have a role in fat graft survival. Our group previously demonstrated a detrimental effect on ASC survival by the lidocaine used in tumescent solution. Sodium bicarbonate (SB) buffers the acidity of lidocaine. The purpose of this study was to determine whether SB buffering is a practical method to reduce ASC and SVF apoptosis and necrosis seen with common lidocaine-containing tumescent solution. Methods: Human patients undergoing bilateral liposuction for any indication were included in this study. An internally controlled, split-body design …
Electrical & Magnetic Stimulation And Health, Yiyan Li
Electrical & Magnetic Stimulation And Health, Yiyan Li
College of Engineering: Graduate Celebration Programs
1, Introduction Most of the patients with neural diseases such as Parkinson’s disease, Stroke and Depression, cannot be cured by taking pills or conducting surgery. In contrast to the conventional therapies, physical therapies such as TMS (transcranial magnetic stimulation) (Fig. 1) and tDCS (transcranial direct current stimulation) (Fig. 2) are the most popular methods in treating neural disorders with a non-invasive way. TMS employs electromagnetic (1-3 Tesla) induction generates an Electric field suitable for neural stimulation; tDCS employs a weak current (1-2 mA) to modulate neuronal excitabilities.