New manufacturing and rapid prototyping technologies have fueled the creation of affordable and easy to replicate upper-limb prostheses. In this matter, many types and designs of 3D-printed upper-limb prostheses have been created over the last years. However, there is no consensus in the testing methodology for these devices regarding their mechanical capabilities and the comparisons authors can make are limited to their own metrics, which could be considered as a subjective approach. In order to tackle this issue, this work revises the existing methods for testing both the mechanical resistance and the mechanical performance or efficiency of upper-limb prostheses; specifically, the ones that are relevant for 3D-printed body-powered prostheses. Then, the adaptations needed to apply these methods to 3D-printed prostheses are discussed. Finally, recommendations are given for prosthetists and researchers in order to execute reliable tests that can be compared across different hand prosthesis designs.
{"title":"[Regular Paper] Mechanical Testing Methods for Body-Powered Upper-Limb Prostheses: A Review","authors":"Renato Mio, Midori Sanchez, Q. Valverde","doi":"10.1109/BIBE.2018.00040","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00040","url":null,"abstract":"New manufacturing and rapid prototyping technologies have fueled the creation of affordable and easy to replicate upper-limb prostheses. In this matter, many types and designs of 3D-printed upper-limb prostheses have been created over the last years. However, there is no consensus in the testing methodology for these devices regarding their mechanical capabilities and the comparisons authors can make are limited to their own metrics, which could be considered as a subjective approach. In order to tackle this issue, this work revises the existing methods for testing both the mechanical resistance and the mechanical performance or efficiency of upper-limb prostheses; specifically, the ones that are relevant for 3D-printed body-powered prostheses. Then, the adaptations needed to apply these methods to 3D-printed prostheses are discussed. Finally, recommendations are given for prosthetists and researchers in order to execute reliable tests that can be compared across different hand prosthesis designs.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122779212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The ECI2 gene encodes a isomerase of mammalian peroxisomes, and one of ECI2 isoforms was found as an important cancer antigen called as hepatocellular carcinoma-associated antigen 64 (HCA64). Recently, it was also found as a gene signature for the prognosis of other cancers such as breast cancer and prostate cancer. High-throughput RNA sequencing has become the state-of-the-art method for measuring the levels of gene expression. This paper studies the expression analysis of breast cancer gene signatures with both Hisat2 and RSEM programs from breast cancer RNA-Seq datasets. The results showed that the transcript of HCA64 was only expressed in only part of breast cancer samples.
{"title":"Quantitative Analysis of ECI2 Expression from RNA-Seq for Breast Cancer Gene Signatures","authors":"Ming-Yi Yen, H. Chu, Yu-Ching Chen, J. Tsai","doi":"10.1109/BIBE.2018.00055","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00055","url":null,"abstract":"The ECI2 gene encodes a isomerase of mammalian peroxisomes, and one of ECI2 isoforms was found as an important cancer antigen called as hepatocellular carcinoma-associated antigen 64 (HCA64). Recently, it was also found as a gene signature for the prognosis of other cancers such as breast cancer and prostate cancer. High-throughput RNA sequencing has become the state-of-the-art method for measuring the levels of gene expression. This paper studies the expression analysis of breast cancer gene signatures with both Hisat2 and RSEM programs from breast cancer RNA-Seq datasets. The results showed that the transcript of HCA64 was only expressed in only part of breast cancer samples.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132192289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Dessie, Ezra B. Wijaya, Chien-Hung Huang, D. Agustriawan, J. Tsai, K. Ng
MicroRNAs as biomarkers play an important role in the oncogenesis process, including ovarian cancer. The objective of this study is to evaluate the miRNAs overexpression association with survival of ovarian cancer patients. MiRNA expression levels between tumor and normal samples were compared using t-test. Differentially expressed miRNAs were selected (p-value ≤ 0.001) and only 195 up-regulated miRNAs for 565 ovarian cancer samples were further analyzed using multivariate Cox regression and survival random forest. The median survival time for ovarian cancer patient was 33.64 months. The result of survival random forest and multivariate Cox regression showed that high level expression of nine miRNAs were associated with shorten survival of ovarian cancer patients; whereas high level expression of hsa-miR-154* was significantly correlated with a prolonged overall survival ovarian cancer patients. These nine aberrantly overexpressed miRNAs that resulted shorter survival time may play important roles in oncogenesis, growth, and metastasis of ovarian cancer. Hence, these findings may be used as novel prognostic biomarkers and therapeutic targets for ovarian cancer patients.
{"title":"[Regular Paper] Identification of Several Core Overexpressed MicroRNAs that Could Predict Survival in Patients with Ovarian Cancer","authors":"E. Dessie, Ezra B. Wijaya, Chien-Hung Huang, D. Agustriawan, J. Tsai, K. Ng","doi":"10.1109/BIBE.2018.00058","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00058","url":null,"abstract":"MicroRNAs as biomarkers play an important role in the oncogenesis process, including ovarian cancer. The objective of this study is to evaluate the miRNAs overexpression association with survival of ovarian cancer patients. MiRNA expression levels between tumor and normal samples were compared using t-test. Differentially expressed miRNAs were selected (p-value ≤ 0.001) and only 195 up-regulated miRNAs for 565 ovarian cancer samples were further analyzed using multivariate Cox regression and survival random forest. The median survival time for ovarian cancer patient was 33.64 months. The result of survival random forest and multivariate Cox regression showed that high level expression of nine miRNAs were associated with shorten survival of ovarian cancer patients; whereas high level expression of hsa-miR-154* was significantly correlated with a prolonged overall survival ovarian cancer patients. These nine aberrantly overexpressed miRNAs that resulted shorter survival time may play important roles in oncogenesis, growth, and metastasis of ovarian cancer. Hence, these findings may be used as novel prognostic biomarkers and therapeutic targets for ovarian cancer patients.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125623160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chun-Yu Lin, Peiying Ruan, Ruiming Li, Jinn-Moon Yang, S. See, T. Akutsu
Cancer subtype identification is an unmet need in precision diagnosis. Recently, evolutionary conservation has been indicated containing understandable signatures for functional significance in cancers. However, the importance of evolutionary conservation in distinguishing cancer subtypes remains unclear. Here, we identified the evolutionarily conserved genes (i.e., core gene) and observed that they are mainly involved in the pathways relevant to cell growth and metabolisms. By using these core genes, we integrated their evolutionary and genomic profiles with deep learning to develop a feature-based strategy (FES) and an image-based strategy (IMS). In comparison with FES using the random set and the strategy using the PAM50 classifier, core gene set-based FES has higher accuracy for identifying breast cancer subtypes. Moreover, the IMS with data augmentation yields better performance than the other strategies. Comprehensive analysis of eight TCGA cancer data demonstrates that our evolutionary conservation-based models provide a valid and helpful approach to identify cancer subtypes and the core gene set offers distinguishable clues of cancer subtypes.
{"title":"Deep Learning with Evolutionary and Genomic Profiles for Identifying Cancer Subtypes","authors":"Chun-Yu Lin, Peiying Ruan, Ruiming Li, Jinn-Moon Yang, S. See, T. Akutsu","doi":"10.1109/BIBE.2018.00035","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00035","url":null,"abstract":"Cancer subtype identification is an unmet need in precision diagnosis. Recently, evolutionary conservation has been indicated containing understandable signatures for functional significance in cancers. However, the importance of evolutionary conservation in distinguishing cancer subtypes remains unclear. Here, we identified the evolutionarily conserved genes (i.e., core gene) and observed that they are mainly involved in the pathways relevant to cell growth and metabolisms. By using these core genes, we integrated their evolutionary and genomic profiles with deep learning to develop a feature-based strategy (FES) and an image-based strategy (IMS). In comparison with FES using the random set and the strategy using the PAM50 classifier, core gene set-based FES has higher accuracy for identifying breast cancer subtypes. Moreover, the IMS with data augmentation yields better performance than the other strategies. Comprehensive analysis of eight TCGA cancer data demonstrates that our evolutionary conservation-based models provide a valid and helpful approach to identify cancer subtypes and the core gene set offers distinguishable clues of cancer subtypes.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126274960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：