2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)最新文献_第2页

Deep Learning for Brain Tumor Segmentation using Magnetic Resonance Images 基于磁共振图像的深度学习脑肿瘤分割

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562890

Surbhi Gupta, Manoj Gupta

引用次数: 15

Adversarial Deep Evolutionary Learning for Drug Design 药物设计的对抗性深度进化学习

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562949

Sheriff Abouchekeir, A. Tchagang, Yifeng Li

{"title":"Adversarial Deep Evolutionary Learning for Drug Design","authors":"Sheriff Abouchekeir, A. Tchagang, Yifeng Li","doi":"10.1109/CIBCB49929.2021.9562949","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562949","url":null,"abstract":"The design of a new therapeutic agent is a time-consuming and expensive process. The rise of machine intelligence provides a grand opportunity of expeditiously discovering novel drug candidates through smart search in the vast molecular structural space. In this paper, we propose a new approach called adversarial deep evolutionary learning (ADEL) to search for novel molecules in the latent space of an adversarial generative model and keep improving the latent representation space. In ADEL, a custom-made adversarial autoencoder (AAE) model is developed and trained under a deep evolutionary learning (DEL) process. This involves an initial training of the AAE model, followed by an integration of multi-objective evolutionary optimization in the continuous latent representation space of the AAE rather than the discrete structural space of molecules. By using the AAE, an arbitrary distribution can be provided to the training of AAE such that the latent representation space is set to that distribution. This allows for a starting latent space from which new samples can be produced. Throughout the process of learning, new samples of high-quality are generated after each iteration of training and then added back into the full dataset. Therefore, allowing for a more comprehensive procedure of understanding the data structure. This combination of evolving data and continuous learning not only enables improvement in the generative model, but the data as well. By comparing ADEL to the previous work in DEL, we see that ADEL can obtain better property distributions.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"1 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":"122815010","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

Retinal Disease Classification from OCT Images Using Deep Learning Algorithms 基于深度学习算法的OCT图像视网膜疾病分类

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562919

Jongwoo Kim, L. Tran

{"title":"Retinal Disease Classification from OCT Images Using Deep Learning Algorithms","authors":"Jongwoo Kim, L. Tran","doi":"10.1109/CIBCB49929.2021.9562919","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562919","url":null,"abstract":"Optical Coherence Tomography (OCT) is a noninvasive test that takes cross-section pictures of the retina layer of the eye and allows ophthalmologists to diagnose based on the retina's layers. Therefore, it is an important modality for the detection and quantification of retinal diseases and retinal abnormalities. Since OCT provides several images for each patient, it is a time consuming work for ophthalmologists to analyze the images. This paper proposes deep learning models that categorize patients' OCT images into four categories such as Choroidal neovascularization (CNV), Diabetic macular edema (DME), Drusen, and Normal. Two different models are proposed. One is using three binary Convolutional Neural Network (CNN) classifiers and the other is using four binary CNN classifiers. Several CNNs, such as VGG16, VGG19, ResNet50, ResNet152, DenseNet121, and InceptionV3, are adapted as feature extractors to develop the binary classifiers. Among them, the proposed model using VGG16 for CNV vs. Other classes, VGG16 for DME vs. other classes, VGG19 for Drusen vs. Other classes, and InceptionV3 for Normal vs. other classes shows the best performance with 0.987 accuracy, 0.987 sensitivity, and 0.996 specificity. The binary classifier for Normal class has 0.999 accuracy. These results show their potential to work as a second reader for ophthalmologists.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"27 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":"123235276","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: 8

An Efficient Boolean Modelling Approach for Genetic Network Inference 遗传网络推理的一种高效布尔建模方法

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562881

Hasini Nakulugamuwa Gamage, M. Chetty, Adrian B. R. Shatte, J. Hallinan

{"title":"An Efficient Boolean Modelling Approach for Genetic Network Inference","authors":"Hasini Nakulugamuwa Gamage, M. Chetty, Adrian B. R. Shatte, J. Hallinan","doi":"10.1109/CIBCB49929.2021.9562881","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562881","url":null,"abstract":"The inference of Gene Regulatory Networks (GRNs) from time series gene expression data is an effective approach for unveiling important underlying gene-gene relationships and dynamics. While various computational models exist for accurate inference of GRNs, many are computationally inefficient, and do not focus on simultaneous inference of both network topology and dynamics. In this paper, we introduce a simple, Boolean network model-based solution for efficient inference of GRNs. First, the microarray expression data are discretized using the average gene expression value as a threshold. This step permits an experimental approach of defining the maximum indegree of a network. Next, regulatory genes, including the self-regulations for each target gene, are inferred using estimated multivariate mutual information-based Min-Redundancy Max-Relevance Criterion, and further accurate inference is performed by a swapping operation. Subsequently, we introduce a new method, combining Boolean network regulation modelling and Pearson correlation coefficient to identify the interaction types (inhibition or activation) of the regulatory genes. This method is utilized for the efficient determination of the optimal regulatory rule, consisting AND, OR, and NOT operators, by defining the accurate application of the NOT operation in conjunction and disjunction Boolean functions. The proposed approach is evaluated using two real gene expression datasets for an Escherichia coli gene regulatory network and a fission yeast cell cycle network. Although the Structural Accuracy is approximately the same as existing methods (MIBNI, REVEAL, Best-Fit, BIBN, and CST), the proposed method outperforms all these methods with respect to efficiency and Dynamic Accuracy.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"17 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":"127872118","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: 7

Genome-scale prediction of bacterial promoters 细菌启动子的基因组尺度预测

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562938

Miria Bernardino, R. Beiko

{"title":"Genome-scale prediction of bacterial promoters","authors":"Miria Bernardino, R. Beiko","doi":"10.1109/CIBCB49929.2021.9562938","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562938","url":null,"abstract":"Proteins are responsible for many tasks including cell growth and metabolism. Transcription, the process where genes are used as templates for the production of a messenger RNA intermediate used in the synthesis of proteins, is regulated to ensure that the cell has the appropriate response according to its current needs. An essential step in transcription is the binding of a group of proteins, collectively known as RNA polymerase, to short promoter sequences upstream of the genes to be transcribed. Automated identification of promoters and nearby regulatory sequences can help to predict which genes are likely to be active under a given set of conditions. However, promoters are short, highly variable, and belong to subclasses that sometimes overlap, making their recognition a very difficult problem. Several tools have been developed to identify promoters in DNA, but methods are generally tested on small, balanced subsets of genomic sequence, and the results may not reflect their expected performance on genomes with millions of DNA base pairs in length where only $sim$ 1% of sequence is expected to correspond to promoters. Here we introduce Expositor, a neural-network-based method that uses different types of DNA encodings and tunable sensitivity and specificity parameters. Although the performance of Expositor on balanced datasets was comparable to that of other approaches, at the genome scale our approach finds the highest number of promoters (70% against 46%) with the smallest number of false positives. We also examined the accuracy of Expositor in distinguishing different classes of promoters, and found that misclassification between classes was consistent with the biological similarity between promoters. Expositor source code and pretrained model, and the datasets used for training and testing can be accessed at https://github.com/beiko-lab/Expositor.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"7 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":"128969118","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

A Scaled-2D CNN for Skin Cancer Diagnosis 一种用于皮肤癌诊断的比例二维CNN

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562888

T. H. Rafi, R. Shubair

{"title":"A Scaled-2D CNN for Skin Cancer Diagnosis","authors":"T. H. Rafi, R. Shubair","doi":"10.1109/CIBCB49929.2021.9562888","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562888","url":null,"abstract":"Every year, doctors diagnose skin cancer in around 3 million or more patients across the globe. Currently, it is one of the most widely recognized kinds of cancers for human health. Hence, we need an early diagnosis to prevail any critical condition of the infected patients. Apparently, it can treat with topical drugs, if it diagnoses in an early stage. Hence as an outcome, skin cancer is responsible for less than 1% of all cancer deaths. There are two types of tumors in the skin cancer diseases domain, such as benign and malignant. To develop a robust and early screening system to diagnose skin cancer, it requires an efficient algorithm for prediction, trained with a large dataset. The primary aim of this research is to develop an efficient skin cancer screening process using a robust deep neural network with a large dataset. In this paper, we intend to determine considerate and dangerous types of skin cancer tumors using dermoscopic images from a publicly available dataset. We proposed an efficient and fast scaled 2D-CNN based on EfficientNet-B7 deep neural architecture with image preprocessing. This paper also uses two different pre-trained deep neural architectures, such as VGG19, and ResNet-50 to compare the performance with the proposed architecture. The proposed architecture outperformed the other pre-trained CNN models whereas the proposed architecture achieved higher AUC and accuracy compared to other architectures.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"46 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":"116702978","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

DeepGREP: A deep convolutional neural network for predicting gene-regulating effects of small molecules DeepGREP:用于预测小分子基因调控作用的深度卷积神经网络

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562920

Benan Bardak, Mehmet Tan

引用次数: 0

One Moose, Two Moose, Three Fields, More? 一只驼鹿，两只驼鹿，三块田地，更多?

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562871

D. Ashlock, J. A. Brown, S. Houghten, M. Makhmutov

引用次数: 0

Discovering Missing Edges in Drug-Protein Networks: Repurposing Drugs for SARS-CoV-2 发现药物-蛋白质网络缺失的边缘:重新利用药物治疗SARS-CoV-2

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562855

Fatemeh Zaremehrjardi, Athar Omidi, Cristina D. Sciortino, Ryan E. R. Reid, Ryan Lukeman, J. Hughes, O. Soufan

{"title":"Discovering Missing Edges in Drug-Protein Networks: Repurposing Drugs for SARS-CoV-2","authors":"Fatemeh Zaremehrjardi, Athar Omidi, Cristina D. Sciortino, Ryan E. R. Reid, Ryan Lukeman, J. Hughes, O. Soufan","doi":"10.1109/CIBCB49929.2021.9562855","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562855","url":null,"abstract":"The COVID-19 pandemic, caused by the SARS-CoV-2 virus, led to a global health crisis, with more than 157 million cases confirmed infected by May 2021. Effective medication is desperately needed. Predicting drug-target interaction (DTI) is an important step to discover novel uses of chemical structures. Here, we develop a pipeline to predict novel DTIs based on the proteins of the coronavirus. Different datasets (human/SARS-CoV-2 Protein-Protein interaction (PPI), Drug-Drug similarity (DD sim), and DTIs) are used and combined. After mapping all datasets onto a heterogeneous graph, path-related features are extracted. We then applied various machine learning (ML) algorithms to model our dataset and predict novel DTIs among unlabeled pairs. Possible drugs identified by the models with a high frequency are reported. In addition, evidence of the efficiency of the predicted medicines by the models against COVID-19 are presented. The proposed model can then be generalized to contain other features that provide a context to predict medicine for different diseases.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"220 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":"124366395","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

Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions 使用临床药物表示提高死亡率和住院时间预测

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI: 10.1109/CIBCB49929.2021.9562819

Batuhan Bardak, Mehmet Tan

{"title":"Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions","authors":"Batuhan Bardak, Mehmet Tan","doi":"10.1109/CIBCB49929.2021.9562819","DOIUrl":"https://doi.org/10.1109/CIBCB49929.2021.9562819","url":null,"abstract":"Drug representations have played an important role in cheminformatics. However, in the healthcare domain, drug representations have been underused relative to the rest of Electronic Health Record (EHR) data, due to the complexity of high dimensional drug representations and the lack of proper pipeline that will allow to convert clinical drugs to their representations. Time-varying vital signs, laboratory measurements, and related time-series signals are commonly used to predict clinical outcomes. In this work, we demonstrated that using clinical drug representations in addition to other clinical features has significant potential to increase the performance of mortality and length of stay (LOS) models. We evaluate the two different drug representation methods (Extended -Connectivity Fingerprint- ECFP and SMILES-Transformer embedding) on clinical outcome predictions. The results have shown that the proposed multimodal approach achieves substantial enhancement on clinical tasks over baseline models. U sing clinical drug representations as additional features improve the LOS prediction for Area Under the Receiver Operating Characteristics (AUROC) around %6 and for Area Under Precision-Recall Curve (AUPRC) by around % 5. Furthermore, for the mortality prediction task, there is an improvement of around % 2 over the time series baseline in terms of AUROC and %3.5 in terms of AUPRC. The code for the proposed method is available at https://github.com/tanlab/MIMIC-III-Clinical-Drug-Representations.","PeriodicalId":163387,"journal":{"name":"2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"52 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":"122912902","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