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bioinformatics 2020 - 11th international conference on bioinformatics Models, Methods and Algorithms, Proceedings;Part of 13th international Joint conference on biomedical engineering Systems and Technologies, BIOSTEC 2020

BIOINFORMATICS 2020 - 11th International Conference on Bioin...

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8th international Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies, HEART 2017

ISBN: (纸本)9789897583988

The proceedings contain 41 papers. The topics discussed include: prediction of dynamical properties of biochemical pathways with graph neural networks;a novel method for the inverse QSAR/QSPR based on artificial neural networks and mixed integer linear programming with guaranteed admissibility;transferability of deep learning algorithms for malignancy detection in confocal laser endomicroscopy images from different anatomical locations of the upper gastrointestinal tract;an optimized method for 3D body scanning applications based on kinect fusion;a convolutional neural network for spot detection in microscopy images;inferring the synaptical weights of leaky integrate and fire asynchronous neural networks: modeled as timed automata;discovering trends in environmental time-series with supervised classification of metatranscriptomic reads and empirical mode decomposition;and how to realize device interoperability and information security in mhealth applications.

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Unsupervised Domain Adaptation by Cross-Prototype Contrastive Learning for Medical Image Segmentation

Unsupervised Domain Adaptation by Cross-Prototype Contrastiv...

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2023 IEEE international conference on bioinformatics and Biomedicine, BIBM 2023

作者： Cai, Zhuotong Xin, Jingmin Dong, Siyuan You, Chenyu Shi, Peiwen Zeng, Tianyi Zhang, Jiazhen Onofrey, John A. Zheng, Nanning Duncan, James S. Xi'an Jiaotong University Institution of Artificial Intelligence and Robotics Xi'an China Yale University Department of Biomedical Engineering New Haven United States Yale University Department of Radiology and Biomedical Imaging New Haven United States Yale University Department of Electrical Engineering New Haven United States

ISBN: (纸本)9798350337488

Unsupervised Domain Adaptation (UDA), which aligns the labeled source distribution to the unlabeled target distribution, has shown remarkable achievement in the medical image segmentation task. Previous UDA methods unilaterally consider the global distribution alignment through explicit category-based loss while good separation and discrimination of class are insufficiently explored, resulting in the sub-aligned distribution across domains. In this paper, we propose cross-prototype contrastive learning method (CPCL) for UDA segmentation through class centroid alignment. Specifically, to reduce the intra-class distance and increase the inter-class distance, we first introduce prototype-feature contrastive learning to align the pixel-level features and the same-class global prototype across domains. Secondly, we further present prototype-prototype contrastive learning to align the same class prototypes between the source domain and target domain for compact category centroid and better global domain distribution alignment. Extensive experiments on two public cardiac datasets demonstrate that the proposed CPCL achieves superior domain adaptation performance as compared with the state-of-the-art. © 2023 IEEE.

关键词： Cross-prototype Contrastive Learning Medical Image Segmentation Unsupervised Domain Adaptation

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Screening, confirmation and validation of mismatch AS F-primers for hypertension-related genotyping

Screening, confirmation and validation of mismatch AS F-prim...

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3rd international conference on Biomedicine and bioinformatics engineering, ICBBE 2023

作者： Yang, Ping Lan, Wei Su, Quanlin Li, Chunyan Zheng, Jie Zhang, Jing Hu, Yue Lan, Wenjun Institute of Biomedical Engineering Qilu University of Technology Jinan Shandong250000 China College of Traditional Chinese Medicine Xinjiang Medical University Urumqi830011 China

ISBN: (纸本)9781643684321

In this study, screening, confirmation and validation of mismatch allele-specific (AS) forward (F)-primers are executed to establish a quadruplex amplification analysis (real-Time PCR) for discrimination of CYP2D6*10, ADRB1, NPPA and CYP3A5*3 genotypes associated with hypertensive pharmacogenomics. To significantly distinguish heterozygote and homozygote, I"Cq (differences in threshold cycles between the wild-Type F-primer amplification assay and the mutant-Type F-primer amplification assay) was utilized to determine outcomes. Detection of plasmid by uniplex real-Time PCR was used to screen the mismatch AS F-primers. Robustness assessment and agreement analysis were employed to confirm and validate initially selected F-primers, respectively. Robustness assessment confirmed that except of ADRB1 (0.7-0.9), amplification efficiency ranged from 0.9 to 1.1. No statistically significant difference was found between the analysis and NGS. Therefore, the optimized F-primer as polymorphism recognition molecules can benefit the genotyping guiding drug delivery in anti-hypertension treatment. © 2023 IOS Press. All rights reserved.

关键词： Genes

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Kinematics Modeling and Simulation Analysis of Human Upper Limbs for Rod Lifting 12

Kinematics Modeling and Simulation Analysis of Human Upper L...

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12th international conference on bioinformatics and biomedical Science, ICBBS 2023

作者： Ji, Yuanzhe Liu, Yali Song, Qiuzhi School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China School of Mechatronical Engineering Institute of Advanced Technology Jinan250101 China

ISBN: (纸本)9798400716140

With the development of technology, exoskeleton robots have wide application in industrialization and civilian. The purpose of this paper is to analyze the kinematics and kinetics during rod lifting, thus providing the necessary suggestion for power grid maintenance exoskeleton design. Firstly, the human motion data was collected through the 3D motion capture system. Secondly, this article proposed an improved four degrees of freedom model for human pole lifting based on DH (Denavit-Hartenberg) method and human anatomy. Thirdly, we conducted dynamic analysis on the pole lifting motion based on the model and in Opensim software. During the pole hanging stage, the maximum torque of the shoulder joint was 27.7N, and the maximum torque of the elbow joint was 28.3N based on the model, with error rates(the ratio of the error between the calculated value and the simulated value to the calculated value) of 11.5% and 2.8% referring to the data in Opensim simulation. During the pole hanging stage, the maximum torque of the shoulder joint was 36.2N, and the maximum torque of the elbow joint was 16.3N, with error rates of 7.5% and 6.7%. Due to the fact that the source of joint torque is muscle force, we further analyzed muscle strength. The results indicated that we can assist with BRA (brachialis) and INFSP (infraspinatus) during the pole lifting stage, and BIClong (biceps brachii, long head) and INFSP (infraspinatus) during the pole hanging phase. This study provides a theoretical basis for the design of structure and control strategy of upper limb exoskeleton. © 2023 ACM.

关键词： Exoskeleton (Robotics)

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deepDGA: biomedical Heterogeneous Network-based Deep Learning Framework for Disease-Gene Association Predictions

deepDGA: Biomedical Heterogeneous Network-based Deep Learnin...

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2022 IEEE international conference on bioinformatics and Biomedicine, BIBM 2022

作者： Wang, Hong Wang, Xiaoqi Liu, Wenjuan Xie, Xiaolan Peng, Shaoliang Hunan University College of Computer Science and Electronic Engineering Changsha China Guilin University of Technology College of Information Science and Engineering Guilin China

ISBN: (纸本)9781665468190

Accurate prediction of disease-gene associations is a crucial tissue in the treatment of diseases. Currently, deep learning-based methods have been proposed to determine the associations between diseases and genes. However, previous network-based models do not consider the semantic characteristics of various biomedical entities and suffer from the problems of cold-start. To this end, this study proposes a heterogeneous network-based deep learning framework (termed deepDGA) to predict disease-gene associations. First, a heterogeneous network with four kinds of biological nodes and eight kinds of edges is constructed. Second, we develop a meta path-driven deep Transformer encoder to learn node representations which contains semantic characteristics of nodes in the heterogeneous network. Finally, the inductive matrix completion algorithm that can solve problem of cold-start, is used for disease-gene association prediction. The results of 5-flod cross-validation and top-ranked predictions suggest that deepDGA is superior to other methods. In addition, we further observe that deepDGA performs the highest predictive ability for specific diseases via the literature verification, KEGG human pathway analyses, and GO enrichment analyses. In summary, deepDGA is an effective framework for predicting the diseases-gene associations. © 2022 IEEE.

关键词： Heterogeneous networks

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DTSC-Net: Semi-supervised 3D biomedical Image Segmentation through Dual-Teacher Simplified Consistency

DTSC-Net: Semi-supervised 3D Biomedical Image Segmentation t...

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2022 IEEE international conference on bioinformatics and Biomedicine, BIBM 2022

作者： Zhou, Xiaogen Li, Zhiqiang Tong, Tong Fuzhou University College of Physics and Information Engineering China Fuzhou University Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology China Imperial Vision Technology

ISBN: (纸本)9781665468190

Although those deep learning networks have achieved remarkable performance in various biomedical image segmentation tasks, they rely on massive labeled data for training, which is time-consuming to acquire. To alleviate this challenging issue, semi-supervised learning (SSL) has shown the potential to simultaneously leverage between abundant unlabeled samples and limited labeled data. In this work, we propose a new SSL approach for left atrial and liver tumor segmentation called DTSC-Net. First, two teacher networks and one student network are optimized together through a supervised loss and two unsupervised consistency losses. Furthermore, pseudo labels are yielded by using the first teacher network on unlabeled data. In addition, the weights of the second teacher network are an exponential moving average of the student network's weights. Moreover, the student network learns from the two teacher networks by minimizing a supervised segmentation loss, a pseudo-labeling unsupervised consistency (PUC) loss, and a dual-teacher unsupervised consistency (DUC) loss with respect to the targets of the two teacher networks. Experiment results show that our approach achieves state-of-the-art semi-supervised segmentation performance on the LA2018 and LiTS2017 datasets. © 2022 IEEE.

关键词： Magnetic resonance

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Integrated Helicobacter Pylori Genome Database and Its Analysis 11th

Integrated Helicobacter Pylori Genome Database and Its Analy...

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11th international conference on Computer engineering and Networks, CENet2021

作者： Zheng, Liufeng Guo, Mujuan Liang, Dingfa Ye, Yuannong Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical Engineering School of Biology and Engineering Guizhou Medical University Guiyang550025 China Bioinformatics and Biomedical Big Data Mining Laboratory Department of Medical Informatics School of Big Health Guizhou Medical University Guiyang550025 China Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University Guiyang550025 China

ISBN: (纸本)9789811665530

The shape of helicobacter pylori (HP) is usually helical or S-shaped, which makes it to have the feature of highly divergent. Besides, its distribution also is different greatly in space and time, which is the factor to lead to multiple infection. In addition, in the same infected person, multiple infection also appeared in different regions of strain gene infection origin. Therefore, it is of great significance to study the genomic diversity and evolutionary analysis of Helicobacter pylori. In this paper, we are to analyze the homology of multiple sequences using ClustalX1.83 and meanwhile to construct the homologous genome evolutionary tree by MEGA7.0 to analyze the its function. The purpose of this study is to compare the genomic evolutionary differences of Helicobacter pylori among populations in different regions, and to discuss the significance of its infection prevalence and genomic variation in geographical changes, so as to conduct further discussion on the molecular level research mechanism of Helicobacter pylori. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genome

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MilGNet: A Multi-instance Learning-based Heterogeneous Graph Network for Drug repositioning

MilGNet: A Multi-instance Learning-based Heterogeneous Graph...

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2022 IEEE international conference on bioinformatics and Biomedicine, BIBM 2022

作者： Gu, Yaowen Zheng, Si Zhang, Bowen Kang, Hongyu Li, Jiao Institute of Medical Information Chinese Academy of Medical Sciences Peking Union Medical College Beijing China Tsinghua University Institute for Artificial Intelligence Department of Computer Science and Technology BNRist Beijing China Computational Medicine Beijing Co. Ltd Beijing China Beijing Institute of Technology School of Life Science Department of Biomedical Engineering Beijing China

ISBN: (纸本)9781665468190

The traditional wet-experiment-guided drug discovery is a labor-consuming and time-consuming process. Quite a few computational drug repositioning approaches have been proposed to predict potential drug-disease associations for the discovery of new indications for drugs and new therapies for diseases. Among them, heterogeneous graph neural network-based approaches can learn drug/disease topological representations on heterogeneous graphs and then give precise inferences for unconfirmed drug-disease associations. However, the existing approaches ignored the meta-paths in the drug-disease networks which could enhance the model performance and interpretability. In this study, we first proposed a multi-instance learning-based heterogeneous graph network approach for drug-disease association prediction, which is called MilGNet. Fusing with heterogeneous graph convolutional layer, the MilGNet learns meta-path-level representations for given drug-disease pairs by a novel pseudo meta-path instance generator and a bidirectional translating embedding projector. Then, an attention-based multi-scale interpretable joint predictor is assembled for precise and rational drug-disease association prediction. Comprehensive experiments have demonstrated the effectiveness of MilGNet compared to 6 advanced approaches. Meanwhile, the case study also shows the model interpretability of MilGNet by identifying high confident meta-paths. Our adopted benchmark dataset and source code are available at https://***/gu-yaowen/MilGNet.. © 2022 IEEE.

关键词： Forecasting

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A MICROFLUDIC PLATFORM AS AN IN VITRO MODEL FOR biomedical EXPERIMENTATION - A CELL MIGRATION STUDY 21

A MICROFLUDIC PLATFORM AS AN <i>IN VITRO</i> MODEL FOR BIOME...

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21st IEEE international conference on bioinformatics and Bioengineering (IEEE BIBE)

作者： Milivojevic, Nevena Caballero, David Carvalho, Mariana R. Kokanovic, Mihajlo Zivanovic, Marko Filipovic, Nenad Reis, Rui L. Oliveira, Joaquim M. Univ Kragujevac Inst Informat Technol Jovana Cvij Bb Kragujevac 34000 Serbia Univ Kragujevac BioIRC Bioengn Res & Dev Ctr Prvoslava Stojanov 6 Kragujevac 34000 Serbia Univ Minho 3Bs Res Grp I3Bs Res Inst Biomat Biodegradables & Biomimet European Inst Excellence Tissue Engn & Regenerat AvePkParque Ciencia & Tecnol P-4805017 Barco Guimaraes Portugal ICVS 3Bs PT Govt Associate Lab Braga Portugal Univ Kragujevac Fac Engn Sestre Janjic 6 Kragujevac 34000 Serbia

ISBN: (纸本)9781665442619

Preclinical experimentation demands for highly reliable and physiologically-relevant systems capable of recapitulating the complex human physiology. Further technological advances are in great need for improving our understanding about critical biological processes involved in tissue development or cancer progression, and for the discovery and screening of novel pharmacological drugs. Traditional in vitro models, albeit widely employed, fail to reproduce the complexity of the native scenario. Similarly, in vivo animal models poorly mimic the human condition and they are ethically questionable. During the last two decades, a new paradigm in preclinical modelling has emerged aiming to solve the limitations of the previous methods. The combination of advanced tissue engineering, cell biology and nanotechnology, has resulted in the development of cutting-edge microfluidics-based models with an unprecedented ability to recreate the native habitat of cells within a microengineered chip. Among the diverse variety of micro- and bio- fabrication techniques, UV-photolithography and soft lithography are considered the gold-standard methods for the fabrication of microfluidic chips to their simplicity, versatility, and rapid prototyping. In this paper, we describe a protocol for the fabrication of a microfluidic chip by UV-photolithography and replica molding, and an example of its use in cell migration assays.

关键词： Microfluidics organ-on-chip UV-photolithography replica molding cell migration

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5th international Work-conference on bioinformatics and biomedical engineering, IWBBIO 2017

5th International Work-Conference on Bioinformatics and Biom...

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5th international Work-conference on bioinformatics and biomedical engineering, IWBBIO 2017

ISBN: (纸本)9783319561530

The proceedings contain 64 papers. The special focus in this conference is on bioinformatics and biomedical engineering. The topics include: Investigation of DNA sequences utilizing frequency-selective nanopore structures;comprehensive study of instable regions in pseudomonas aeruginosa;mixed-integer programming model for profiling disease biomarkers from gene expression studies;mixed-integer programming model for profiling disease biomarkers from gene expression studies;pairwise and incremental multi-stage alignment of metagenomes;an expanded association approach for rare germline variants with copy-number alternation;prediction of calmodulin binding proteins using short-linear motifs;application of a membrane protein structure prediction web service gpcrm to a gastric inhibitory polypeptide receptor model;exploring symmetric substructures in protein interaction networks for pairwise alignment;analysis of soil data from eastern of morocco based on data mining process;a multi-objective optimization framework for multiple sequence alignment with metaheuristics;analysis of gene expression discretization techniques in microarray biclustering;analysis of informative features for negative selection in protein function prediction;differential network analysis of anti-sense regulation;nonlinear control and simulation of a dielectric elastomer actuator-based compression bandage on flexible human calf;management of data structures generated during simulations of the evolution of multicellular systems;computational prediction of host-pathogen interactions through omics data analysis and machine learning;machine learning approaches for predicting high utilizers in health care and a case study on the integration of heterogeneous data sources in public health.

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