2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)最新文献

An Improved Method for Finding Attractors of Large-Scale Asynchronous Boolean Networks 一种寻找大规模异步布尔网络吸引子的改进方法

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562947

Giang V. Trinh, K. Hiraishi

引用次数: 4

Identification of Genes Associated with Alzheimer's Disease using Evolutionary Computation 利用进化计算鉴定与阿尔茨海默病相关的基因

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562876

Guangyao Chen, James Sargant, S. Houghten, T. K. Collins

引用次数: 0

Random Forest to Predict Eucalyptus as a Potential Herb in Preventing Covid19 随机森林预测桉树作为预防covid - 19的潜在草药

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562940

N. Ramadhanti, W. Kusuma, I. Batubara, R. Heryanto

{"title":"Random Forest to Predict Eucalyptus as a Potential Herb in Preventing Covid19","authors":"N. Ramadhanti, W. Kusuma, I. Batubara, R. Heryanto","doi":"10.1109/CIBCB49929.2021.9562940","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562940","url":null,"abstract":"The covid-19 pandemic had been on the rise since the beginning of 2020. In Indonesia itself, the first case was identified on 3rd March 2020, then peaked at around the end of January 2021. Even though the recent number of covid-19 cases is not as much as the peak time, the positive case has been increasing from around 2600 to 6300 cases every day in the last month. This phenomenon is urging people to take better care of their health. One of the alternatives Indonesian takes to maintain and increase their health is using herbal medicine. Indonesia is one of the countries with a flourishing number of herbal species. Eucalyptus is one of herbal plants with lots of benefits. Even before the pandemic eucalyptus oil has been used for daily use by many in Indonesia. In this study, we predict the compounds in eucalyptus which have any interaction with protein in SARS-COV-2 virus using machine learning method, namely Random Forest. This is one of the applications of the drug-discovery method, drug repurposing, which used existing drug-target interaction data as a model to predict drug compounds with unidentified interaction with targets. Applying this method, we predicted some compounds found in eucalyptus, such as alpha-terpinene, and 1,8-cineole might have an interaction with covid-19 protein thus eucalyptus can be used as a preventive measure.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132619862","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

Vaccinating a Population is a Changing Programming Problem 为人群接种疫苗是一个不断变化的规划问题

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562943

Sumaiya Amin, S. Houghten, J. Hughes

引用次数: 3

A comparison of multi-objective optimization algorithms to identify drug target combinations 多目标优化算法识别药物靶标组合的比较

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562773

S. Spolaor, D. Papetti, P. Cazzaniga, D. Besozzi, M. S. Nobile

{"title":"A comparison of multi-objective optimization algorithms to identify drug target combinations","authors":"S. Spolaor, D. Papetti, P. Cazzaniga, D. Besozzi, M. S. Nobile","doi":"10.1109/CIBCB49929.2021.9562773","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562773","url":null,"abstract":"Combination therapies represent one of the most effective strategy in inducing cancer cell death and reducing the risk to develop drug resistance. The identification of putative novel drug combinations, which typically requires the execution of expensive and time consuming lab experiments, can be supported by the synergistic use of mathematical models and multi-objective optimization algorithms. The computational approach allows to automatically search for potential therapeutic combinations and to test their effectiveness in silico, thus reducing the costs of time and money, and driving the experiments toward the most promising therapies. In this work, we couple dynamic fuzzy modeling of cancer cells with different multi-objective optimization algorithm, and we compare their performance in identifying drug target combinations. Specifically, we perform batches of optimizations with 3 and 4 objective functions defined to achieve a desired behavior of the system (e.g., maximize apop-tosis while minimizing necrosis and survival), and we compare the quality of the solutions included in the Pareto fronts. Our results show that both the choice of the multi-objective algorithm and the formulation of the optimization problem have an impact on the identified solutions, highlighting the strengths as well as the limitations of this approach.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123318087","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

Predicting Influenza A Viral Host Using PSSM and Word Embeddings 利用PSSM和词嵌入预测甲型流感病毒宿主

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562959

Yanhua Xu, D. Wojtczak

引用次数: 1

$mathcal{LAJA}{-}$ Label Attention Transformer Architectures for ICD-10 Coding of Unstructured Clinical Notes $mathcal{LAJA}{-}$用于非结构化临床记录ICD-10编码的标签注意力转换器架构

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562815

V. Mayya, Sowmya S Kamath, V. Sugumaran

{"title":"$mathcal{LAJA}{-}$ Label Attention Transformer Architectures for ICD-10 Coding of Unstructured Clinical Notes","authors":"V. Mayya, Sowmya S Kamath, V. Sugumaran","doi":"10.1109/CIBCB49929.2021.9562815","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562815","url":null,"abstract":"Effective code assignment for patient clinical records in a hospital plays a significant role in the process of standardizing medical records, mainly for streamlining clinical care delivery, billing, and managing insurance claims. The current practice employed is manual coding, usually carried out by trained medical coders, making the process subjective, error-prone, inexact, and time-consuming. To alleviate this cost-intensive process, intelligent coding systems built on patients' structured electronic medical records are critical. Classification of medical diagnostic codes, like ICD-10, is widely employed to categorize patients' clinical conditions and associated diagnoses. In this work, we present a neural model $mathcal{LAJA}$, built on Label Attention Transformer Architectures for automatic assignment of ICD-10 codes. Our work is benchmarked on the CodiEsp dataset, a dataset for automatic clinical coding systems for multilingual medical documents, used in the eHealth CLEF 2020-Multilingual Information Extraction Shared Task. The experimental results reveal that the proposed $mathcal{LAJA}$ variants outperform their basic BERT counterparts by 33-49% in terms of standard metrics like precision, recall, F1-score and mean average precision. The label attention mechanism also enables direct extraction of textual evidence in medical documents that map to the clinical ICD-10 diagnostic codes.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121585843","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: 4

Human Activity Recognition Using Convolutional Neural Networks 基于卷积神经网络的人类活动识别

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562906

Gulustan Dogan, Sinem Sena Ertas, Iremnaz Cay

引用次数: 5

Dynamically Regulated Initialization for S-system Modelling of Genetic Networks 遗传网络s系统建模的动态调节初始化

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562958

Jaskaran Gill, M. Chetty, Adrian B. R. Shatte, J. Hallinan

{"title":"Dynamically Regulated Initialization for S-system Modelling of Genetic Networks","authors":"Jaskaran Gill, M. Chetty, Adrian B. R. Shatte, J. Hallinan","doi":"10.1109/CIBCB49929.2021.9562958","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562958","url":null,"abstract":"Reverse engineering of gene regulatory networks through temporal gene expression data is an active area of research. Among the plethora of modelling techniques under investigation is the decoupled S-system model, which attempts to capture the non-linearity of biological systems in detail. For the model, number of parameters to be estimated are significantly high even when the network is of small or medium scale. Thus, the inference process poses a significant computational burden. In this paper, we propose: (1) a novel population initialization technique, Dynamically Regulated Prediction Initialization (DRPI), which utilises prior knowledge of biological gene expression data to create a feedback loop to produce dynamically regulated high-quality individuals for initial population; (2) an adaptive fitness function; and (3) a method for the maintenance of population diversity. The aim of this work is to reduce the computational complexity of the inference algorithm, to speed up the entire process of reverse engineering. The performance of the proposed algorithm was evaluated against a benchmark dataset and compared with other methods from earlier work. The experimental results show that we succeeded in achieving higher accuracy results in lesser fitness evaluations, considerably reducing the computational burden of the inference process.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117314651","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: 6

SPOT-1D2: Improving Protein Secondary Structure Prediction using High Sequence Identity Training Set and an Ensemble of Recurrent and Residual-convolutional Neural Networks SPOT-1D2:利用高序列同一性训练集和循环和残差卷积神经网络集成改进蛋白质二级结构预测

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562849

Jaspreet Singh, Jaswinder Singh, K. Paliwal, Andrew Busch, Yaoqi Zhou

引用次数: 1