Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology最新文献_第2页

The Pharmacy Automatically Machine 药房自动售货机

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 2019-05-29 DOI: 10.1145/3340074.3340092

Boonyarat Phimmasorn, S. Visitsattapongse

引用次数: 1

Fetus Heart Beat Extraction from Mother's PCG Using Blind Source Separation 利用盲源分离技术提取母体心电图中的胎儿心跳

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 2019-05-29 DOI: 10.1145/3340074.3340087

Maliha Atteeq, Muhammad Fahad Khan, Adnan N. Qureshi

引用次数: 2

New Insights to Hydrogen Bonds to Provide Stability in the EGFR Related to Non-small Cell Lung Cancer 非小细胞肺癌相关EGFR中氢键稳定性的新发现

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 2019-05-29 DOI: 10.1145/3340074.3340079

Avirup Ghosh, Hong Yan

{"title":"New Insights to Hydrogen Bonds to Provide Stability in the EGFR Related to Non-small Cell Lung Cancer","authors":"Avirup Ghosh, Hong Yan","doi":"10.1145/3340074.3340079","DOIUrl":"https://doi.org/10.1145/3340074.3340079","url":null,"abstract":"Lung cancer is the most common cancer in the world, but it is one of the most preventable. Non-small cell lung cancer accounts for approximately 85% of all lung cancers. Epidermal growth factor receptor or EGFR is the class of high-affinity cell surface receptors which are essential in regulating biological processes including cell differentiation, cell survival or death, and cellular metabolism. An amino acid substitution at the 858th position of EGFR, from a Leucine(L) to an Arginine(R) causes L858R mutation within exon 21, which encodes part of the kinase domain and drives to NSCLC. For over 60% of EGFR-muted NSCLC, another mutation T790M can cause drug resistance to erlotinib or gefitinib. In our research work, we considered three structures of EGFR, wild-type, with L858R mutation and with L858R/T790M drug-resistance mutation. The number of hydrogen bond decreases when the EGFR becomes mutated and it reduces even more in its drug-resistance structure. We perform 200 frames of molecular dynamics (MD) simulation to analyze the behavioral changes in hydrogen bonds for all three structures. Since the hydrogen bonds contribute to the conformational stability of the protein and molecular recognition, the knowledge, and results achieved from this study lead to useful insight into the mechanism of NSCLC drug resistance.","PeriodicalId":196396,"journal":{"name":"Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117158242","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}

引用次数: 0

Diffusion Kernel based Fast Adaptive Clustering of Single Cell RNA-seq Data 基于扩散核的单细胞RNA-seq数据快速自适应聚类

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 2019-05-29 DOI: 10.1145/3340074.3340084

Samina Kausar, Xu Huahu, R. Mehmood, Muhammad Shahid Iqbal

{"title":"Diffusion Kernel based Fast Adaptive Clustering of Single Cell RNA-seq Data","authors":"Samina Kausar, Xu Huahu, R. Mehmood, Muhammad Shahid Iqbal","doi":"10.1145/3340074.3340084","DOIUrl":"https://doi.org/10.1145/3340074.3340084","url":null,"abstract":"Recently, with the advent of high throughput single-cell technologies, it has become possible to quantify the whole transcriptome of individual cells; however, it remains challenging to discover intrinsic rare cell-types from high throughput genes expression data. To overcome this challenge, various unsupervised clustering based approaches have been proposed such as GiniClust, SC3 and SIMLR clustering. These approaches identified appropriate rare cell types based on the ambiguous parametric settings and employed clustering algorithms are inefficient to discover meaningful clusters adaptively. However, the appropriate signal of interest can be observed along with the robust filtration, normalization, and transformation of raw count samples of single-cell data. Filtration, normalization, and transformation have become the essential primary procedure for down-stream analysis of single-cell data and to eliminate the risk of biological variation and technical noise. In this paper, we will evaluate the various methods to detect the rare cell-types from a large population and develop fast novel diffusion kernel based unsupervised framework (DKBUF) to identify rare cell types from single-cell RNA-seq data, more in an adaptive and attractive fashion. The DKBUF filters the non-stable genes, normalizes the genes, attractively detects subpopulations within single-cell datasets, and visualizes the discovered distinct subpopulations. Extensive experiments on single-cell datasets and comparisons with state-of-the-art methods validate the robustness of the DKBUF.","PeriodicalId":196396,"journal":{"name":"Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130175033","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}

引用次数: 0

Automatic Breast Cancer Grading of Histological Images using Dilated Residual Network 基于扩张残差网络的乳腺癌组织图像自动分级

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 2019-05-29 DOI: 10.1145/3340074.3340077

Yanyuet Man, Hailong Yao

{"title":"Automatic Breast Cancer Grading of Histological Images using Dilated Residual Network","authors":"Yanyuet Man, Hailong Yao","doi":"10.1145/3340074.3340077","DOIUrl":"https://doi.org/10.1145/3340074.3340077","url":null,"abstract":"Breast cancer is one of the leading causes of female death worldwide. Histological evaluation of the breast biopsies is essential in the early detection. Recently, deep learning methods are developed to automatically grade breast cancer of histological images. For the critical local and global features of histological images, few existing deep learning methods effectively extract both of them. Most methods extract one at the loss of the other, with degraded multi-class classification accuracy. In this paper, we propose an effective breast cancer classification method of histology images based on a modified dilated residual network (DRN). The proposed method effectively captures the global feature while maintaining the local information, and thus achieves notably high multi-class classification accuracy. Experimental results show that for the four-class breast cancer classification problem, an accuracy of 89.5% can be obtained, which outperforms all the prevalent methods. In comparison to the manual diagnosis accuracy of 89% from pathologists, the proposed automatic diagnosis method is practical and promising.","PeriodicalId":196396,"journal":{"name":"Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133587559","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}

引用次数: 1

A Deep Learning Approach for Slice to Volume Biomedical Image Integration 切片到体生物医学图像集成的深度学习方法

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 2019-05-29 DOI: 10.1145/3340074.3340089

B. Almogadwy, Kenneth McLeod, A. Burger

{"title":"A Deep Learning Approach for Slice to Volume Biomedical Image Integration","authors":"B. Almogadwy, Kenneth McLeod, A. Burger","doi":"10.1145/3340074.3340089","DOIUrl":"https://doi.org/10.1145/3340074.3340089","url":null,"abstract":"Biomedical atlas images obtained from multiple sources need to be aligned and transformed into a single coordinate system so as to be able to integrate and relate these different sets of data. Formally known as image registration, this process of image pre-processing has proven to be integral in a wide array of computer vision ap- plications, most notably in the area of medical imaging. During the last decade slice-to-volume registration, a particular case of image registration problem, has received further attention in the medical imaging community due to the emergence of several medi- cal applications of slice-to-volume mapping. This paper proposes a Convolutional Neural Network (CNN) based deep learning ap- proach for registering a 2D image slice to the 3D volume of images in a Biomedical atlas. The proposed CNN model is trained to de- termine the distance and pitch values that are used to describe the position of the 2D slice in the atlas coordinate system. High-level features are automatically extracted from the training dataset of images, which addresses the limitation of shallow machine learning techniques for handcrafted features followed by the classification task. Then on the basis of predicted values of distance and pitch, the target image is registered to the 3D volume of images. Experimental results showing the effect on the similarity of images with variation in distance and the impact of varying the distances among the classes on the regression are presented. It was observed that using the successive images at a distance of 10 lead to the maxi- mum accuracy. These results demonstrate the applicability of the proposed approach to slice-to-volume image registration.","PeriodicalId":196396,"journal":{"name":"Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124980910","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}

引用次数: 5

DNA Digital Data Storage based on Distributed Method 基于分布式方法的DNA数字数据存储

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 2019-05-29 DOI: 10.1145/3340074.3340082

Yu Wang, Yang Zhang, Yi Zhao

引用次数: 0

Analysis of the Target Genes of Transcription Factor ZNF536 in Lung Adenocarcinoma 转录因子ZNF536在肺腺癌中的靶基因分析

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 2019-05-29 DOI: 10.1145/3340074.3340095

Xintong Xu

{"title":"Analysis of the Target Genes of Transcription Factor ZNF536 in Lung Adenocarcinoma","authors":"Xintong Xu","doi":"10.1145/3340074.3340095","DOIUrl":"https://doi.org/10.1145/3340074.3340095","url":null,"abstract":"Lung cancer is becoming one of the most common and deadly cancers. We calculated the mutation frequency of all the transcription factors from the downloaded lung adenocarcinoma (LUAD) data from TCGA and found ZNF536 had a relatively high mutation frequency. To further reveal the potential functional roles of ZNF536 on lung adenocarcinoma, RNA-Seq data of LUAD were downloaded, and classified into the mutant and wild groups based on ZNF536 mutant states, then the differentially expressed genes between these two groups were calculated. A p-value lower than 0.05 suggests a significant difference. As a result, among a total of 20,531 genes, 1,174 genes were upregulated and 863 genes were downregulated in the ZNF536 mutant group compared with the wild group. Functional enrichment analysis revealed that these dysregulated genes were mainly related to cell cycle, mismatch repair, and DNA replication, and so on. By reviewing studies on lung ADC by other scientists, upregulated genes, HDAC2, EP300, MAPK1, KRAS, NRAS, which regulate the initiation, growth, invasion and metastasis of lung ADC cells. Taken together, these findings suggest that ZNF536 plays a critical part in the development of lung ADC and may serve as a potential target for new medications in treating lung ADC.","PeriodicalId":196396,"journal":{"name":"Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology","volume":"36 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126150682","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}

引用次数: 1

Tensor Decomposition of Non-EEG Physiological Signals for Visualization and Recognition of Human Stress 基于张量分解的非脑电图生理信号可视化与识别

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 2019-05-29 DOI: 10.1145/3340074.3340096

Thi T.T. Pham, Héctor Rodriguez Déniz, T. Pham

{"title":"Tensor Decomposition of Non-EEG Physiological Signals for Visualization and Recognition of Human Stress","authors":"Thi T.T. Pham, Héctor Rodriguez Déniz, T. Pham","doi":"10.1145/3340074.3340096","DOIUrl":"https://doi.org/10.1145/3340074.3340096","url":null,"abstract":"Recognition of physical and mental responses to stress is important for the purpose of stress management to reduce its negative effects in health. Wearable technology, mainly using electroencephalogram (EEG), provides information such as tracking fitness activity, disease detection, and neurological states of individuals. However, the recording of EEG signals from a wearable device is inconvenient. This study introduces the application of tensor decomposition of non-EEG data for visualizing and tracking neurological status with implication to human stress recognition. Results obtained from testing the proposed method using a PhyioNet database show visualizations that can well separate four groups of neurological statuses obtained from twenty healthy subjects, and very high up to 100% classification of the neurological statuses. The investigation suggests the potential application of tensor decomposition for the analysis of physiological measurements collected from multiple sensors. The proposed study can significantly contribute to the advancement of wearable technology for human stress monitoring.","PeriodicalId":196396,"journal":{"name":"Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133781483","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}

引用次数: 3

Computational Procedure for Analysis of Fish Diversity in Greece 希腊鱼类多样性分析的计算程序

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 2019-05-29 DOI: 10.1145/3340074.3340091

K. Onkov, G. Tegos

引用次数: 0