2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献

Performance Comparison of LASSO Variants with Genome-Wide Association Studies (GWAS) LASSO变异与全基因组关联研究(GWAS)的性能比较

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2021-12-09 DOI: 10.1109/BIBM52615.2021.9669510

Nisha Puthiyedth, Nuoyi Zhang, Ziqing Wang, Yan Yan

引用次数: 0

PocaCNV: A Tool to Detect Copy Number Variants from Population-Scale Genome Sequencing Data PocaCNV:一个从群体规模基因组测序数据中检测拷贝数变异的工具

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2021-12-09 DOI: 10.1109/BIBM52615.2021.9669405

Zhendong Zhang, Yongzhuang Liu, Gaoyang Li, Yadong Wang

引用次数: 0

Dynamic Restricted Column Generation in MIQP for Cancer Radiation Therapy 肿瘤放射治疗MIQP的动态限制柱生成

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2021-12-09 DOI: 10.1109/BIBM52615.2021.9669759

Eva K. Lee, K. Cha

{"title":"Dynamic Restricted Column Generation in MIQP for Cancer Radiation Therapy","authors":"Eva K. Lee, K. Cha","doi":"10.1109/BIBM52615.2021.9669759","DOIUrl":"https://doi.org/10.1109/BIBM52615.2021.9669759","url":null,"abstract":"More than 14 million new cases of cancer are diagnosed globally each year, with approximately 50% of all cancer patients receiving radiation therapy during the course of their disease. Advanced technology using a multi-leaf collimator can deliver non-uniform radiation doses that best conform to the tumor shape while sparing the surrounding normal tissues. Combinatorically, selecting beam angles is computationally taxing and remains a major challenge. Consequently, clinical systems opt to use preselected angles without integrating a combinatorial computational engine within the fluence map optimization process.In this paper, we offer a mixed integer quadratic programming optimization approach to simultaneously determine optimal beam angles and fluence maps. For computational efficiency, a dynamic restricted branch-and-price method is derived to solve these large-scale patient instances. Clinical analyses of prostate and head-and-neck cancer patient cases show that our approach yields treatment plans that are far superior to current best practice in the clinical environment. Specifically, the resulting plans achieve better conformity and homogeneity to the tumor while reducing radiation exposure to the critical structures. Computationally, besides drastically reducing the solution time, the dynamic restricted branch-and-price method provides an additional bonus: every node consists of only a relatively small number of beam variables in the branch-and-bound framework. This implies that in the middle of the solution process, a feasible and clinically acceptable treatment plan is available, and can be obtained by taking a few branching steps on the beam variables that are in the active set.","PeriodicalId":366354,"journal":{"name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115461693","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

SASA-Net: A spatial-aware self-attention mechanism for building protein 3D structure directly from inter-residue distances SASA-Net:基于残基间距离直接构建蛋白质三维结构的空间感知自注意机制

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2021-12-09 DOI: 10.1109/BIBM52615.2021.9669346

Tiansu Gong, Fusong Ju, Shiwei Sun, D. Bu

{"title":"SASA-Net: A spatial-aware self-attention mechanism for building protein 3D structure directly from inter-residue distances","authors":"Tiansu Gong, Fusong Ju, Shiwei Sun, D. Bu","doi":"10.1109/BIBM52615.2021.9669346","DOIUrl":"https://doi.org/10.1109/BIBM52615.2021.9669346","url":null,"abstract":"Protein structure prediction has achieved considerable progresses mainly due to the increased accuracy of inter-residue distance estimation and the application of deep learning techniques.Most of the distance-based ab initio prediction approaches adopt a two-step diagram: constructing a potential function based on the estimated inter-residue distances, and then build a 3D structure that minimizes the potential function. These approaches have proven very promising; however, they still suffer from several limitations, especially the inaccuracies incurred by the handcrafted potential function.Here, we present SASA-Net, a deep learning-based approach that directly learns protein 3D structure from the estimated inter-residue distances. Unlike the existing approach simply representing protein structures as coordinates of atoms, SASA-Net represents protein structures using pose of residues, i.e., the coordinate system of each individual residue in which all backbone atoms of this residue are fixed. The key element of SASA-Net is a spatial-aware self-attention mechanism, which is able to adjust a residue’s pose according to all other residues’ features and the estimated distances between residues. By iteratively applying the spatial-aware self-attention mechanism, SASA-Net continuously improves the structure and finally acquires a structure with high accuracy. Using the CATH35 proteins as representatives, we demonstrate that SASA-Net is able to accurately and efficiently build structures from the estimated inter-residue distances. The high accuracy and efficiency of SASA-Net enables an end-to-end neural network model for protein structure prediction through combining SASA-Net and an neural network for inter-residue distance prediction. Source code of SASA-Net is available at https://github.con gongtiansu/SASA-Net/","PeriodicalId":366354,"journal":{"name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"19 17-19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116703024","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

Multi-modal Information Fusion-powered Regional Covid-19 Epidemic Forecasting 基于多模式信息融合的区域Covid-19疫情预测

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2021-12-09 DOI: 10.1109/BIBM52615.2021.9669328

Honglu Zhang, Yonghui Xu, Lei Liu, Xudong Lu, Xijie Lin, Zhongmin Yan, Li-zhen Cui, C. Miao

{"title":"Multi-modal Information Fusion-powered Regional Covid-19 Epidemic Forecasting","authors":"Honglu Zhang, Yonghui Xu, Lei Liu, Xudong Lu, Xijie Lin, Zhongmin Yan, Li-zhen Cui, C. Miao","doi":"10.1109/BIBM52615.2021.9669328","DOIUrl":"https://doi.org/10.1109/BIBM52615.2021.9669328","url":null,"abstract":"With the current raging spread of the COVID19, early forecasting of the future epidemic trend is of great significance to public health security. The COVID-19 is virulent and spreads widely. An outbreak in one region often triggers the spread of others, and regions with relatively close association would show a strong correlation in the spread of the epidemic. In the real world, many factors affect the spread of the outbreak between regions. These factors exist in the form of multimodal data, such as the time-series data of the epidemic, the geographic relationship, and the strength of social contacts between regions. However, most of the current work only uses historical epidemic data or single-modal geographic location data to forecast the spread of the epidemic, ignoring the correlation and complementarity in multi-modal data and its impact on the disease spread between regions. In this paper, we propose a Multimodal InformatioN fusion COVID-19 Epidemic forecasting model (MINE). It fuses inter-regional and intra-regional multi-modal information to capture the temporal and spatial relevance of the COVID-19 spread in different regions. Extensive experimental results show that the proposed method achieves the best results compared to state-of-art methods on benchmark datasets.","PeriodicalId":366354,"journal":{"name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116958389","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

A quantitative device for elbow joint function rehabilitation based on the concept of traditional Chinese fracture rehabilitation 基于中国传统骨折康复概念的肘关节功能康复定量装置

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2021-12-09 DOI: 10.1109/BIBM52615.2021.9669411

Dejian Wei, Zhenzhen Zhao, Ya-nan Feng, Hui Cao, Jing Liu, Junzhong Zhang

{"title":"A quantitative device for elbow joint function rehabilitation based on the concept of traditional Chinese fracture rehabilitation","authors":"Dejian Wei, Zhenzhen Zhao, Ya-nan Feng, Hui Cao, Jing Liu, Junzhong Zhang","doi":"10.1109/BIBM52615.2021.9669411","DOIUrl":"https://doi.org/10.1109/BIBM52615.2021.9669411","url":null,"abstract":"Early rehabilitation exercises after elbow fracture treatment can affect the degree of joint function recovery. Due to the different levels of endurance and pain tolerance of patients, blind training not only makes patients to suffer pain, but also tends to cause postoperative complications. Therefore, this paper introduces a wearable assistive elbow rehabilitation training device that can quantify the patient's recovery level. The device uses pressure sensors to collect the pressure value of the patient's arm when squeezing the muscles, tri-axial gyroscopes and tri-axial accelerometers to perceive the angular velocity and acceleration of the patient's arm during the rehabilitation exercise. The collected data are processed by an internal processor and presented in a touch screen, which in turn provides a reference for the physician to develop a rehabilitation training program. In addition, the device also provides voice alerts when the patient's rehabilitation training reaches an unreasonable level or range of motion, helping the patient to carry out quantized rehabilitation training and promote normal fracture healing.","PeriodicalId":366354,"journal":{"name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125054559","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

Allosteric Modulation of Small Molecule Drugs on ACE2 Conformational Change upon Binding to SARS-CoV-2 Spike Protein 小分子药物对ACE2与SARS-CoV-2刺突蛋白结合后构象变化的变构调节

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2021-12-09 DOI: 10.1109/BIBM52615.2021.9669438

Duen-Shian Wang, Hamed S. Hayatshahi, Vindi M. Jayasinghe-Arachchige, Jin Liu

{"title":"Allosteric Modulation of Small Molecule Drugs on ACE2 Conformational Change upon Binding to SARS-CoV-2 Spike Protein","authors":"Duen-Shian Wang, Hamed S. Hayatshahi, Vindi M. Jayasinghe-Arachchige, Jin Liu","doi":"10.1109/BIBM52615.2021.9669438","DOIUrl":"https://doi.org/10.1109/BIBM52615.2021.9669438","url":null,"abstract":"Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused a worldwide pandemic (COVID-19). Drug repurposing studies, including drugs such as dexamethasone (DEX), chloroquine (CQ), and telmisartan (TLS), have been performed in COVID-19 clinical trials. DEX and CQ have been demonstrated in vitro to bind angiotensin-converting enzyme 2 (ACE2), a cellular entry receptor utilized by SARS-CoV-2. However, how DEX/CQ bind to ACE2 and their mechanisms of action are still unknown. We demonstrated that DEX, CQ, and TLS disrupt the interactions between SARS-CoV-2 spike protein and human ACE2 via binding to an allosteric site close to the viral spike protein binding region at the peptidase domain of ACE2, causing a conformational change of the ACE2. We defined four conformational states of ACE2 based on the two helices distances. Our molecular dynamics simulations suggested that binding to the viral spike protein shifted ACE2 conformation populations away from “Open” conformation. Such conformation population shift is further enhanced by the Delta variant. The binding of the drugs to ACE2 rescues this conformation population shift allosterically to keep ACE2 in “Open” conformation mostly. Our findings provide a potential insight that modulating the conformation of ACE2 may prevent SARS-CoV-2 invasion due to unfavored poses for spike protein binding.","PeriodicalId":366354,"journal":{"name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126060062","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

Automated Molecule Generation using Deep Q-Learning and Graph Neural Networks 使用深度q -学习和图神经网络的自动分子生成

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2021-12-09 DOI: 10.1109/BIBM52615.2021.9669667

Riza Isik, Mehmet Tan

{"title":"Automated Molecule Generation using Deep Q-Learning and Graph Neural Networks","authors":"Riza Isik, Mehmet Tan","doi":"10.1109/BIBM52615.2021.9669667","DOIUrl":"https://doi.org/10.1109/BIBM52615.2021.9669667","url":null,"abstract":"The concept of generating molecular structures with specific desirable characteristics underlies some of the crucial problems in drug discovery. In this paper, we present a model which constructs new molecules for specific desired properties. This model uses graph neural networks to generate molecular representations, and combining these representations with Reinforcement Learning architecture (Deep Q-Learning), builds new molecules. We used two different graph neural network architectures: Graph Convolutional Network and Graph Attention Network. We compared the molecular representations obtained from these two models with the Morgan Fingerprint representation in three separate experiments using the same Reinforcement Learning design. These experiments are single-objective optimization (drug-likeness), optimizing the penalized LogP with similarity constraint, and multi-objective optimization (drug-likeness with similarity constraint). The results show that the Reinforcement Learning models trained with molecular representations obtained from graph neural networks are more successful than the model trained with Morgan Fingerprint representation.","PeriodicalId":366354,"journal":{"name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123288240","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

Ensemble Clustering-based Cervical Spondylosis Fine-classification 基于集合聚类的颈椎病精细分类

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2021-12-09 DOI: 10.1109/BIBM52615.2021.9669295

Nana Wang, Chunjie Luo, Yunyou Huang, Jianfeng Zhan

{"title":"Ensemble Clustering-based Cervical Spondylosis Fine-classification","authors":"Nana Wang, Chunjie Luo, Yunyou Huang, Jianfeng Zhan","doi":"10.1109/BIBM52615.2021.9669295","DOIUrl":"https://doi.org/10.1109/BIBM52615.2021.9669295","url":null,"abstract":"State-of-the-art and state-of-the-practice clinical work fail to fine-classify early cervical spondylosis (CS) illness and hence can not provide personalized treatment. Surface electromyography (sEMG) is an essential physiological signal that can locate muscle problems associated with cervical spine dysfunction, and it is promising to provide sEMG-based CS fine-classification. However, the state-of-the-art clustering approaches on sEMG data have the following drawbacks: (1) the clustering results are unstable due to different algorithms or multiple values of a resolution parameter. (2) The samples of the same individual in the same state are clustered in different groups. This paper proposes an individual-centered three-layer ensemble clustering framework (in short, ITECF) to fine-classify cervical spondylosis (CS). ITECF mainly consists of three parts: base clusters generation, individual consensus, base cluster consensus. We define an individual consistency index named ICS to measure the consistency of samples of the same individual in the same partition and propose an ICS-based consensus to transform the sample-centered base clusters into individual-centered ones for partition selection. We evaluate our approaches against five clustering algorithms and five ensemble clustering frameworks on the three real-world sEMG data set using four metrics: Silhouette Coefficient (SC), Davies Bouldin score (DB), Calinski-Harabaz Index (CH), and ICS. The results show that ITECF outperforms the state-of-the-art algorithms. ITECF obtains eight fine-classifications of CS for the first time, each with different muscle pathological problems in muscle coordination, balance, and strength. And, this fine-classification has the potential for personalized treatment.","PeriodicalId":366354,"journal":{"name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123529509","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

A Mild Depression Recognition with Classifier Combination Method Based on Differential Evolution 基于差分进化的分类器组合方法识别轻度抑郁症

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2021-12-09 DOI: 10.1109/BIBM52615.2021.9669693

Yalin Li, Bin Hu, Fang Zheng, Xiangwei Zheng

{"title":"A Mild Depression Recognition with Classifier Combination Method Based on Differential Evolution","authors":"Yalin Li, Bin Hu, Fang Zheng, Xiangwei Zheng","doi":"10.1109/BIBM52615.2021.9669693","DOIUrl":"https://doi.org/10.1109/BIBM52615.2021.9669693","url":null,"abstract":"Depression is one of the most common mental disorders affecting people, but its recognition rate is low due to subjectivity and other factors. Mild depression, in particular, has milder and less recognizable symptoms. In this study, we used electroencephalography (EEG) and machine learning methods to identify mild depression. Our approach was to construct a new weighted combinatorial classifier model to distinguish patients with mild depression from normal controls. In this experiment, 10 mildly depressed patients and 10 normal controls watched different emotional facial pictures, recorded their EEG signals and preprocessed them, and then extracted linear and nonlinear features to construct feature vectors. K-nearest neighbor(KNN), support vector machine(SVM), logistic regression(LR), random forest(RF) and back propagation neural network(BPNN) were selected as five individual classifiers, and the differential evolution algorithm(DE) was used to optimize the weights to improve the overall performance of the recognition model. The experimental results showed that the classification accuracy of the proposed method was better than that of the individual classifiers, and the highest 99.09% was achieved when the number of iterations was 50, indicating that the fusion model had a higher recognition accuracy of mild depression than the single modes. At the same time, compared with other combination strategies, this method was also better than other strategies. This research may provide a means to identify mild depression.","PeriodicalId":366354,"journal":{"name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125503304","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