Search

Your search keyword '"Park, Sunho"' showing total 595 results
Start Over Author "Park, Sunho"
595 results on '"Park, Sunho"'

157. Radiomics Features of 18F-fluorodeoxyglucose Positron-Emission Tomography as a Novel Prognostic Signature in Colorectal Cancer

Author

Kang, Jeonghyun, primary, Lee, Jae-Hoon, additional, Lee, Hye Sun, additional, Cho, Eun-Suk, additional, Park, Eun Jung, additional, Baik, Seung Hyuk, additional, Lee, Kang Young, additional, Park, Chihyun, additional, Yeu, Yunku, additional, Clemenceau, Jean R., additional, Park, Sunho, additional, Xu, Hongming, additional, Hong, Changjin, additional, and Hwang, Tae Hyun, additional

Published
2019
Full Text
View/download PDF

159. Using transfer learning on whole slide images to predict tumor mutational burden in bladder cancer patients

Author

Xu, Hongming, Park, Sunho, Lee, Sung Hak, and Hwang, Tae Hyun

Abstract

The tumor mutational burden (TMB) is a genomic biomarker, which can help in identifying patients most likely to benefit from immunotherapy across a wide range of tumor types including bladder cancer. DNA sequencing, such as whole exome sequencing (WES) is typically used to determine the number of acquired mutations in the tumor. However, WES is expensive, time consuming and not applicable to all patients, and hence it is difficult to be incorporated into clinical practice. This study investigates the feasibility to predict bladder cancer patients TMB by using histological image features. We design an automated whole slide image analysis pipeline that predicts bladder cancer patient TMB via histological features extracted by using transfer learning on deep convolutional networks. The designed pipeline is evaluated to publicly available large histopathology image dataset for a cohort of 253 patients with bladder cancer obtained from The Cancer Genome Atlas (TCGA) project. Experimental results show that our technique provides over 73% classification accuracy, and an area under the receiver operating characteristic curve of 0.75 in distinguishing low and high TMB patients. In addition, it is found that the predicted low and high TMB patients have statistically different survivals, with the p value of 0.047. Our results suggest that bladder cancer patient TMB is predictable by using histological image features derived from digitized H&E slides. Our method is extensible to histopathology images of other organs for predicting patient clinical outcomes.

Published
2019
Full Text
View/download PDF

177. Radially patterned transplantable biodegradable scaffolds as topographically defined contact guidance platforms for accelerating bone regeneration.

Author

Gwon, Yonghyun, Park, Sunho, Kim, Woochan, Han, Taeseong, Kim, Hyoseong, and Kim, Jangho

Subjects
*TRANSPLANTATION of organs, tissues, etc., *AUTOTRANSPLANTATION, *TISSUE engineering, *BONE regeneration, *CELL migration, *BONE grafting, *TISSUE scaffolds
Abstract

Background: The healing of large critical-sized bone defects remains a clinical challenge in modern orthopedic medicine. The current gold standard for treating critical-sized bone defects is autologous bone graft; however, it has critical limitations. Bone tissue engineering has been proposed as a viable alternative, not only for replacing the current standard treatment, but also for producing complete regeneration of bone tissue without complex surgical treatments or tissue transplantation. In this study, we proposed a transplantable radially patterned scaffold for bone regeneration that was defined by capillary force lithography technology using biodegradable polycaprolactone polymer. Results: The radially patterned transplantable biodegradable scaffolds had a radial structure aligned in a central direction. The radially aligned pattern significantly promoted the recruitment of host cells and migration of osteoblasts into the defect site. Furthermore, the transplantable scaffolds promoted regeneration of critical-sized bone defects by inducing cell migration and differentiation. Conclusions: Our findings demonstrated that topographically defined radially patterned transplantable biodegradable scaffolds may have great potential for clinical application of bone tissue regeneration. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

178. Network-based drug sensitivity prediction.

Author

Ahmed, Khandakar Tanvir, Park, Sunho, Jiang, Qibing, Yeu, Yunku, Hwang, TaeHyun, and Zhang, Wei

Subjects
*PARTIAL least squares regression, *PEARSON correlation (Statistics), *ARTIFICIAL neural networks, *NON-small-cell lung carcinoma, *FEATURE selection, *GENE regulatory networks, *BIOLOGICAL networks
Abstract

Background: Drug sensitivity prediction and drug responsive biomarker selection on high-throughput genomic data is a critical step in drug discovery. Many computational methods have been developed to serve this purpose including several deep neural network models. However, the modular relations among genomic features have been largely ignored in these methods. To overcome this limitation, the role of the gene co-expression network on drug sensitivity prediction is investigated in this study. Methods: In this paper, we first introduce a network-based method to identify representative features for drug response prediction by using the gene co-expression network. Then, two graph-based neural network models are proposed and both models integrate gene network information directly into neural network for outcome prediction. Next, we present a large-scale comparative study among the proposed network-based methods, canonical prediction algorithms (i.e., Elastic Net, Random Forest, Partial Least Squares Regression, and Support Vector Regression), and deep neural network models for drug sensitivity prediction. All the source code and processed datasets in this study are available at https://github.com/compbiolabucf/drug-sensitivity-prediction. Results: In the comparison of different feature selection methods and prediction methods on a non-small cell lung cancer (NSCLC) cell line RNA-seq gene expression dataset with 50 different drug treatments, we found that (1) the network-based feature selection method improves the prediction performance compared to Pearson correlation coefficients; (2) Random Forest outperforms all the other canonical prediction algorithms and deep neural network models; (3) the proposed graph-based neural network models show better prediction performance compared to deep neural network model; (4) the prediction performance is drug dependent and it may relate to the drug's mechanism of action. Conclusions: Network-based feature selection method and prediction models improve the performance of the drug response prediction. The relations between the genomic features are more robust and stable compared to the correlation between each individual genomic feature and the drug response in high dimension and low sample size genomic datasets. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

179. Sparse Vector Coding for Ultra-Reliable and Low Latency Communications

Author

Ji, Hyoungju, Park, Sunho, and Shim, Byonghyo

Subjects
FOS: Computer and information sciences, Information Theory (cs.IT), Computer Science - Information Theory
Abstract

Ultra reliable and low latency communication (URLLC) is a newly introduced service category in 5G to support delay-sensitive applications. In order to support this new service category, 3rd Generation Partnership Project (3GPP) sets an aggressive requirement that a packet should be delivered with 10^-5 packet error rate within 1 ms transmission period. Since the current wireless transmission scheme designed to maximize the coding gain by transmitting capacity achieving long codeblock is not relevant for this purpose, a new transmission scheme to support URLLC is required. In this paper, we propose a new approach to support the short packet transmission, called sparse vector coding (SVC). Key idea behind the proposed SVC technique is to transmit the information after the sparse vector transformation. By mapping the information into the position of nonzero elements and then transmitting it after the random spreading, we obtain an underdetermined sparse system for which the principle of compressed sensing can be applied. From the numerical evaluations and performance analysis, we demonstrate that the proposed SVC technique is very effective in URLLC transmission and outperforms the 4G LTE and LTE-Advanced scheme., To appear in IEEE Transactions on Wireless Communications. Copyright 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses

Published
2017

180. Bayesian Semi-nonnegative Tri-matrix Factorization to Identify Pathways Associated with Cancer Types

Author

Park, Sunho and Hwang, Tae Hyun

Subjects
FOS: Computer and information sciences, Statistics - Machine Learning, Machine Learning (stat.ML)
Abstract

Identifying altered pathways that are associated with specific cancer types can potentially bring a significant impact on cancer patient treatment. Accurate identification of such key altered pathways information can be used to develop novel therapeutic agents as well as to understand the molecular mechanisms of various types of cancers better. Tri-matrix factorization is an efficient tool to learn associations between two different entities (e.g., cancer types and pathways in our case) from data. To successfully apply tri-matrix factorization methods to biomedical problems, biological prior knowledge such as pathway databases or protein-protein interaction (PPI) networks, should be taken into account in the factorization model. However, it is not straightforward in the Bayesian setting even though Bayesian methods are more appealing than point estimate methods, such as a maximum likelihood or a maximum posterior method, in the sense that they calculate distributions over variables and are robust against overfitting. We propose a Bayesian (semi-)nonnegative matrix factorization model for human cancer genomic data, where the biological prior knowledge represented by a pathway database and a PPI network is taken into account in the factorization model through a finite dependent Beta-Bernoulli prior. We tested our method on The Cancer Genome Atlas (TCGA) dataset and found that the pathways identified by our method can be used as a prognostic biomarkers for patient subgroup identification., Comment: This work has been accepted to "ML4H: Machine Learning for Health" workshop at NIPS 2017 (https://ml4health.github.io/2017/)

Published
2017
Full Text
View/download PDF

181. Leaf-Inspired Micro- and Nanoengineered Surfaces for Controlled Hydrophilic and Hydrophobic Properties.

Author

Kim, Daun, Kim, Woochan, Park, Sunho, Kim, Sujin, Gwon, Yonghyun, and Kim, Jangho

Abstract

In various industries, it is important to manipulate engineering platforms by controlling hydrophilic and hydrophobic properties. Here, we report on a simple, tunable replication method that mimics the unique surface of micro- and nanotopographies of common camellia, fragrant plantain, and lotus leaf, with properties that range from hydrophilic to hydrophobic. Using lithographic technology in combination with thermal- or ultraviolet (UV)-sensitive materials such as polycaprolactone (PCL) and polyurethane acrylate (PUA), we designed and fabricated hydrophilic and hydrophobic surfaces using a single material (i.e., PCL and PUA with controlled surface properties). The morphologies of the mimetic leaves were analyzed, which showed that flat, hexagonal, and protuberance topographies were successfully generated using the same material. There was no change in the chemical bending or crystalline structure of the mimetic platform compared with the flat platforms. This demonstrates that our leaf-inspired micro- and nanostructures could exhibit hydrophilic and hydrophobic properties, which is an important consideration in designing engineering platforms for various industries. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

185. Abstract 2572:HSD3B1genotype and abiraterone metabolism in patients with prostate cancer

Author

Alyamani, Mohammad, primary, Emamekhoo, Hamid, additional, Park, Sunho, additional, Taylor, Jennifer, additional, Almassi, Nima, additional, Upadhyay, Sunil, additional, Tyler, Allison, additional, Berk, Michael P., additional, Hwang, Tae Hyun, additional, Grivas, Petros, additional, Rini, Brian, additional, Garcia, Jorge, additional, Auchus, Richard J., additional, and Sharifi, Nima, additional

Published
2018
Full Text
View/download PDF

186. HSD3B1(1245A>C) variant regulates dueling abiraterone metabolite effects in prostate cancer

Author

Alyamani, Mohammad, primary, Emamekhoo, Hamid, additional, Park, Sunho, additional, Taylor, Jennifer, additional, Almassi, Nima, additional, Upadhyay, Sunil, additional, Tyler, Allison, additional, Berk, Michael P., additional, Hu, Bo, additional, Hwang, Tae Hyun, additional, Figg, William Douglas, additional, Peer, Cody J., additional, Chien, Caly, additional, Koshkin, Vadim S., additional, Mendiratta, Prateek, additional, Grivas, Petros, additional, Rini, Brian, additional, Garcia, Jorge, additional, Auchus, Richard J., additional, and Sharifi, Nima, additional

Published
2018
Full Text
View/download PDF

191. Chemistry-First Approach for Nomination of Personalized Treatment in Lung Cancer

Author

McMillan, Elizabeth A., primary, Ryu, Myung-Jeom, additional, Diep, Caroline H., additional, Mendiratta, Saurabh, additional, Clemenceau, Jean R., additional, Vaden, Rachel M., additional, Kim, Ju-Hwa, additional, Motoyaji, Takashi, additional, Covington, Kyle R., additional, Peyton, Michael, additional, Huffman, Kenneth, additional, Wu, Xiaofeng, additional, Girard, Luc, additional, Sung, Yeojin, additional, Chen, Pei-Hsaun, additional, Mallipeddi, Prema L., additional, Lee, Joo Young, additional, Hanson, Jordan, additional, Voruganti, Sukesh, additional, Yu, Yunku, additional, Park, Sunho, additional, Sudderth, Jessica, additional, DeSevo, Christopher, additional, Muzny, Donna M., additional, Doddapaneni, HarshaVardhan, additional, Gazdar, Adi, additional, Gibbs, Richard A., additional, Hwang, Tae-Hyun, additional, Heymach, John V., additional, Wistuba, Ignacio, additional, Coombes, Kevin R., additional, Williams, Noelle S., additional, Wheeler, David A., additional, MacMillan, John B., additional, Deberardinis, Ralph J., additional, Roth, Michael G., additional, Posner, Bruce A., additional, Minna, John D., additional, Kim, Hyun Seok, additional, and White, Michael A., additional

Published
2018
Full Text
View/download PDF

196. HSD3B1 genotype and abiraterone (Abi) metabolites in patients (pts) with prostate cancer (PCa).

Author

Emamekhoo, Hamid, primary, Alyamani, Mohammad, additional, Park, Sunho, additional, Taylor, Jennifer, additional, Almassi, Nima, additional, Upadhyay, Sunil, additional, Tyler, Allison Janine, additional, Berk, Mike, additional, Hwang, Tae Hyun, additional, Grivas, Petros, additional, Rini, Brian I., additional, Garcia, Jorge A., additional, Auchus, Richard J., additional, and Sharifi, Nima, additional

Published
2018
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results