CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics最新文献_第2页

Proof-of-concept model of red blood cell with coarse-grained hemoglobin 粗粒血红蛋白红细胞的概念验证模型

CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics Pub Date : 2020-11-19 DOI: 10.1145/3429210.3429228

Mariana Ondrusová, I. Cimrák

引用次数: 0

Automatic ICD-10 codes association to diagnosis: Bulgarian case 自动ICD-10代码与诊断的关联:保加利亚病例

CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics Pub Date : 2020-11-19 DOI: 10.1145/3429210.3429224

Boris Velichkov, Simeon Gerginov, P. Panayotov, S. Vassileva, Gerasim Velchev, I. Koychev, S. Boytcheva

{"title":"Automatic ICD-10 codes association to diagnosis: Bulgarian case","authors":"Boris Velichkov, Simeon Gerginov, P. Panayotov, S. Vassileva, Gerasim Velchev, I. Koychev, S. Boytcheva","doi":"10.1145/3429210.3429224","DOIUrl":"https://doi.org/10.1145/3429210.3429224","url":null,"abstract":"This paper presents an approach for the automatic association of diagnoses in Bulgarian language to ICD-10 codes. Since this task is currently performed manually by medical professionals, the ability to automate it would save time and allow doctors to focus more on patient care. The presented approach employs a fine-tuned language model (i.e. BERT) as a multi-class classification model. As there are several different types of BERT models, we conduct experiments to assess the applicability of domain and language specific model adaptation. To train our models we use a big corpora of about 350,000 textual descriptions of diagnosis in Bulgarian language annotated with ICD-10 codes. We conduct experiments comparing the accuracy of ICD-10 code prediction using different types of BERT language models. The results show that the MultilingualBERT model (Accuracy Top 1 - 81%; Macro F1 - 86%, MRR Top 5 - 88%) outperforms other models. However, all models seem to suffer from the class imbalance in the training dataset. The achieved accuracy of prediction in the experiments can be evaluated as very high, given the huge amount of classes and noisiness of the data. The result also provides evidence that the collected dataset and the proposed approach can be useful in building an application to help medical practitioners with this task and encourages further research to improve the prediction accuracy of the models. By design, the proposed approach strives to be language-independent as much as possible and can be easily adapted to other languages.","PeriodicalId":164790,"journal":{"name":"CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126335540","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

Classification of Protein Crystallization Images using EfficientNet with Data Augmentation 基于数据增强的高效网蛋白质结晶图像分类

CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics Pub Date : 2020-11-19 DOI: 10.1145/3429210.3429220

David William Edwards II, I. Dinç

引用次数: 1

Modeling metabolic fluxes underlying cassava storage root growth through E-Fmin analysis 通过E-Fmin分析模拟木薯贮藏根生长的代谢通量

CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics Pub Date : 2020-11-19 DOI: 10.1145/3429210.3429234

Ratchaprapa Kamsen, S. Kalapanulak, T. Saithong

{"title":"Modeling metabolic fluxes underlying cassava storage root growth through E-Fmin analysis","authors":"Ratchaprapa Kamsen, S. Kalapanulak, T. Saithong","doi":"10.1145/3429210.3429234","DOIUrl":"https://doi.org/10.1145/3429210.3429234","url":null,"abstract":"Cassava (Manihot esculenta Crantz) is a staple crop that has a great impact on global food security. Cassava yield improvement has continuously been researched, resulting in various elite cultivars bred during last decades. To pursue a better yield, it requires deep insight into metabolic process underlying the assimilation and conversion of carbon substrates to storage root biomass. In this study, we employed E-Fmin analysis to model carbon metabolism in storage roots of cassava. The model was constructed based on primary metabolism of carbon assimilation pathway in non-photosynthetic cells and corresponding gene expression data. The model, namely rMeCBMx-EFmin, was able to mimic growth of storage roots measured from Kasetsart 50 (KU50). The rMeCBMx-EFmin highlighted the tentative metabolic flux distribution that carbon substrates were economically converted into cellular biomass of cassava storage roots. The small total flux (3.2749 mmol gDWSRs−1 day−1) with respect to the published model of cassava storage roots (4.4255 mmol gDWSRs−1 day−1) indicated metabolic frugality in the simulated root metabolism. The simulation also showed that alpha-D-glucose-6-phosphate (-D-Glc-6P) partitioned from respiration was a key carbon precursor imported to plastid for storage root biomass production. The knowledge gained would be beneficial for later experimental design of yield enhancement.","PeriodicalId":164790,"journal":{"name":"CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122703469","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

Convolutional neural network for prediction of COVID-19 from chest X-ray images 卷积神经网络用于胸部x线图像预测COVID-19

CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics Pub Date : 2020-11-19 DOI: 10.1145/3429210.3429219

Debayan Goswami, Anwesha Law, Debasrita Chakraborty, Abhishek Dey

引用次数: 2

An Image Segment-based Classification for Chest X-Ray Image 基于图像分割的胸部x射线图像分类

CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics Pub Date : 2020-11-19 DOI: 10.1145/3429210.3429227

Phongsathorn Kittiworapanya, Kitsuchart Pasupa

{"title":"An Image Segment-based Classification for Chest X-Ray Image","authors":"Phongsathorn Kittiworapanya, Kitsuchart Pasupa","doi":"10.1145/3429210.3429227","DOIUrl":"https://doi.org/10.1145/3429210.3429227","url":null,"abstract":"In late 2019, the first case of COVID-19 was confirmed in Wuhan, China. The number of cases has been rapidly growing since then. Molecular and antigen testing methods are very accurate for the diagnosis of COVID-19. However, with sudden increases of infected cases, laboratory-based molecular test and COVID-19 test kits are in short supply. Because the virus affects an infected patient’s lung, interpreting images obtained from Computed Tomography Scanners and Chest X-ray Radiography (CXR) machines can be an alternative for diagnosis. However CXR interpretation requires experts and the number of experts is limited. Therefore, automatic detection of COVID-19 from CXR images is required. We describe a system for automatic detection of COVID-19 from CXR images. It first segmented images to select only the lung. The segmented part was then fed into a multiclass classification module, which worked well with samples obtained from various sources, which had different aspect ratios, contrast and viewpoints. The system also handled the unbalanced dataset—only a small fraction of images showed COVID-19. Our system achieved 92% of F1-score and 88.1% Marco F1-score on the 3rd Deep Learning and AI Summer/Winter School Hackathon Phase 3—Multi-class COVID-19 Chest X-ray challenge public leaderboard.","PeriodicalId":164790,"journal":{"name":"CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115194923","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: 2

Predicting Dihydroartemisinin Resistance in Plasmodium falciparum using Pathway Activity Inference 利用途径活性推断预测恶性疟原虫双氢青蒿素耐药性

CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics Pub Date : 2020-11-19 DOI: 10.1145/3429210.3429215

Nicola Lawford, Jonathan H. Chan

{"title":"Predicting Dihydroartemisinin Resistance in Plasmodium falciparum using Pathway Activity Inference","authors":"Nicola Lawford, Jonathan H. Chan","doi":"10.1145/3429210.3429215","DOIUrl":"https://doi.org/10.1145/3429210.3429215","url":null,"abstract":"Drug resistance threatens the effectiveness of treatments of infectious diseases, particularly on the global scale where mutation is rapid, mechanisms of resistance are developing or unknown, and limited data is available. Pathway activity inference is a dimensionality reduction method with proven effectiveness in classifying cancer types and drug responses based on transcription data. We propose a novel application of pathway activity inference to predict dihydroartemisinin resistance in the Plasmodium falciparum strain of malaria, a global infectious disease. Optimized pathway activity inference models outperform untransformed gene expression models in both in vitro regression (p = 0.03) and in vivo classification tasks (p = 2 × 10− 9). Optimal methods were found to be mostly ensemble (5 of 12) and/or kernel-based (7 of 12), providing the first evidence of the effectiveness of kernel methods for predicting drug resistance in infectious diseases. Performance metrics of the optimal in vitro model on in vivo data (accuracy , area under receiver operating characteristic curve = 0.63) affirmed the low empirical correlation between resistance measures in the two settings.","PeriodicalId":164790,"journal":{"name":"CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114144239","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

Identification of Gene Subnetwork Biomarkers of Lung Cancer from RNA-seq Data 基于RNA-seq数据的肺癌基因亚网络生物标志物鉴定

CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics Pub Date : 2020-11-19 DOI: 10.1145/3429210.3429212

Kritsada Sreebunpeng, Jonathan H. Chan, A. Meechai

{"title":"Identification of Gene Subnetwork Biomarkers of Lung Cancer from RNA-seq Data","authors":"Kritsada Sreebunpeng, Jonathan H. Chan, A. Meechai","doi":"10.1145/3429210.3429212","DOIUrl":"https://doi.org/10.1145/3429210.3429212","url":null,"abstract":"In recent years, the increasing availability of cancer RNA-seq datasets has provided unprecedented information and opportunities for the discovery of biomarkers for cancer. In this study, we tested our previously published Gene Sub-Network-based Feature Selection (GSNFS) method to identify gene-subnetwork biomarkers with RNA-seq-based gene expression data of lung cancer. In addition, five different filter-based feature selection techniques were explored to rank identified subnetworks. We found that the majority of the top 10 ranked subnetworks were associated with cancer pathways such as the MAPK signalling pathway. With Support Vector Machine (SVM) as a classifier based on the Area Under Curve (AUC) of the Receiver Operating Characteristic (ROC) curve using 10-fold cross-validation and cross-dataset validation, we showed that gene subnetwork biomarkers obtained by RNA-seq-based GSNFS analysis had excellent classification performance. Additionally, when comparing the top-ranked subnetworks obtained from RNA-seq-based GSNFS analysis with those top-ranked subnetworks previously obtained from DNA microarray-based GSNFS analysis, we could categorize subnetworks and found unique pathways of cancer for each data-based analysis.","PeriodicalId":164790,"journal":{"name":"CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130295515","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

Towards Automated Comprehension and Alignment of Cardiac Models at the System Invariant Level 在系统不变水平上实现心脏模型的自动理解和对齐

CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics Pub Date : 2020-11-19 DOI: 10.1145/3429210.3429225

Samuel Huang, Madeline Diep, Kuk Jin Jang, E. Cherry, F. Fenton, R. Cleaveland, Mikael Lindvall, R. Mangharam, Adam Porter

{"title":"Towards Automated Comprehension and Alignment of Cardiac Models at the System Invariant Level","authors":"Samuel Huang, Madeline Diep, Kuk Jin Jang, E. Cherry, F. Fenton, R. Cleaveland, Mikael Lindvall, R. Mangharam, Adam Porter","doi":"10.1145/3429210.3429225","DOIUrl":"https://doi.org/10.1145/3429210.3429225","url":null,"abstract":"The study of cardiac arrhythmias has spurred the development of models across a variety of formulations and scales and designed for different purposes, each with distinct configuration spaces. Nevertheless, these models should be able to exhibit equivalent behavior when their contexts overlap. Configuring models to both support this context equivalence and still exhibit intended behavioral characteristics can be challenging. Due to the complexity of this problem, automation can be desirable. We present a framework aimed at automating the comprehension and alignment of cardiac model behaviors. For model comprehension, we mine a set of properties (invariants) that a model with given configuration will exhibit when executed. Comprehension can be extended to model alignment: we perform comprehension of one model, and then mine a set of configurations for a second, each of which produces invariants aligned to the invariants of the first. The configuration spaces of the two models under study need not be related in any way; rather, the systems are compared by means of the system invariants that they each exhibit. We model system invariants as association rules, a well-studied representation used in the field of data mining. We apply our methodology to two one-dimensional models of cardiac tissue. One model is the well-known differential-equations-based Fenton-Karma model representing the electrophysiology of interconnected cardiac cells, while the other is a timed automaton representation of cardiac tissue designed to enable formal analysis. We demonstrate alignment of the models with respect to activation rates and path conductance. We expect this methodology can be generalized beyond cardiac models.","PeriodicalId":164790,"journal":{"name":"CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122508357","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

CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics 第十一届计算系统生物学与生物信息学国际学术会议论文集

CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics Pub Date : 1900-01-01 DOI: 10.1145/3429210

引用次数: 1