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XGBoost Model for Chronic Kidney Disease Diagnosis

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2020年第6期17卷 2131-2140页

作者： Ogunleye, Adeola Wang, Qing-Guo Univ Johannesburg Fac Engn & Built Environm Inst Intelligent Syst Auckland Pk South Africa

Chronic Kidney Disease (CKD) is a menace that is affecting 10 percent of the world population and 15 percent of the South African population. The early and cheap diagnosis of this disease with accuracy and reliability will save 20,000 lives in South Africa per year. Scientists are developing smart solutions with Artificial intelligence (AI). In this paper, several typical and recent AI algorithms are studied in the context of CKD and the extreme gradient boosting (XGBoost) is chosen as our base model for its high performance. Then, the model is optimized and the optimal full model trained on all the features achieves a testing accuracy, sensitivity, and specificity of 1.000, 1.000, and 1.000, respectively. Note that, to cover the widest range of people, the time and monetary costs of CKD diagnosis have to be minimized with fewest patient tests. Thus, the reduced model using fewer features is desirable while it should still maintain high performance. To this end, the set-theory based rule is presented which combines a few feature selection methods with their collective strengths. The reduced model using about a half of the original full features performs better than the models based on individual feature selection methods and achieves accuracy, sensitivity and specificity, of 1.000, 1.000, and 1.000, respectively.

关键词： Diseases Kidney Feature extraction computational modeling Sociology Statistics Artificial intelligence Medical diagnosis chronic kidney disease artificial intelligence extreme gradient boosting clinical decision support system

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Representation Learning from Genotypes to Phenotypes: Linking Chemical and Biological Space in Small Molecules and Macromolecules

Representation Learning from Genotypes to Phenotypes: Linkin...

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ieee International Conference on bioinformatics and Biomedicine (BIBM)

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Summary form only. Molecular biology has undergone many disruptions and revolutions that have opened entire domains of scientific enquiry, including bioinformatics. Anfinsen showed us that protein tertiary structure was largely encoded in the amino-acid sequence. John Kendrew’s famous sentences “The way in which the chain of amino acid units in a protein molecule is coiled and folded in space has been worked out for the first time. The protein is myoglobin, the molecule of which contains 2,600 atoms.” instigated decades of seminal computational studies on macromolecular structure, dynamics, and function by Scheraga, Karplus, Levitt, Warshel, and others. Powerful computational concepts and techniques were debuted in these studies, many of which we would now categorize as “Artificial intelligence” (AI). In my own work, I focused heavily, and still do, on the question of representation, which is central to AI. Then came the data revolution and revival of neural networks, and most recently AlphaFold2. Despite these rapid advances, we are still unable to link chemical and biological space. In this talk I will overview work in my laboratory on AI frameworks that link protein sequence variation to dynamics-governed dysfunction. I will also describe some of our recent Machine Learning frameworks for mapping protein structure spaces and generating small molecules in-silico with controllable biological properties. I hope lessons of success and failure from my laboratory will inspire us to further advance scientific enquiry and address those interesting, complex, and messy questions that molecular biology never fails to provide.

关键词： Protein engineering bioinformatics US Government Biological information theory Molecular biology Education Chemicals

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A Synchronous iEEG Data Acquisition Framework for Dual Brain Interchange Systems

A Synchronous iEEG Data Acquisition Framework for Dual Brain...

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Midwest symposium on Circuits and Systems (MWSCAS)

作者： Amir Hossein Ayyoubi Behrang Fazli Besheli Jhan L. Okkabaz Chandra Prakash Swamy Kai J. Miller Gregory A. Worrell Nuri F. Ince Department of Neurologic Surgery Mayo Clinic Rochester MN USA Department of Bioinformatics and Computational Biology University of Minnesota Minneapolis MN USA Department of Neurology Mayo Clinic Rochester MN USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

This study presents a new data acquisition Framework for synchronous dual Brain Interchange (BIC) systems recording. The setup expands the capacity for data recording by offering access to up to 64 channels. The environment utilizes our Simulink model, incorporating functionalities for synchronization using a master clock and email-based status updates. We evaluated the framework in the lab simulations, and we observed a 38 ms post-synchronization delay between the systems. We also demonstrated that this error can be minimized to as low as 5 ms through adjustments in the master clock resolution and data buffer size. We estimated units' sampling frequency with high accuracy to avoid desynchronization. We evaluated the setup on the intracranial EEG (iEEG) recording simultaneously with the clinical system and performed spike detection on the post-synchronized iEEG. We observed over 95% similarity rate between the dual BIC and clinical system. Additionally, we explored the optimal configuration for ground and reference connections between systems to achieve the highest signal quality, along with investigating the implications of frequency interference in dual-system operations.

关键词： Software packages Data acquisition Interference Brain modeling Frequency estimation Electroencephalography Recording

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Co-evolving morphology and control of soft robots using a single genome

Co-evolving morphology and control of soft robots using a si...

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ieee symposium Series on computational intelligence (SSCI)

作者： Fabio Tanaka Claus Aranha Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Department of Computer Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9781665487696

When simulating soft robots, both their morphology and their controllers play important roles in task performance. This paper introduces a new method to co-evolve these two components in the same process. We do that by using the hyperNEAT algorithm to generate two separate neural networks in one pass, one responsible for the design of the robot body structure and the other for the control of the robot. The key difference between our method and most existing approaches is that it does not treat the development of the morphology and the controller as separate processes. Similar to nature, our method derives both the “brain” and the “body” of an agent from a single genome and develops them together. While our approach is more realistic and doesn't require an arbitrary separation of processes during evolution, it also makes the problem more complex because the search space for this single genome becomes larger and any mutation to the genome affects “brain” and the “body” at the same time. Additionally, we present a new speciation function that takes into consideration both the genotypic distance, as is the standard for NEAT, and the similarity between robot bodies. By using this function, agents with very different bodies are more likely to be in different species, this allows robots with different morphologies to have more specialized controllers since they won't crossover with other robots that are too different from them. We evaluate the presented methods on four tasks and observe that even if the search space was larger, having a single genome makes the evolution process converge faster when compared to having separated genomes for body and control. The agents in our population also show morphologies with a high degree of regularity and controllers capable of coordinating the voxels to produce the necessary movements.

关键词： Robot kinematics Sociology Morphology Genomics Process control Soft robotics bioinformatics

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V-Net Based Liver and Tumor Segmentation Using 3D ConditionalRandom Fields and Graph Cuts Method 14

V-Net Based Liver and Tumor Segmentation Using 3D Conditiona...

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14th International symposium on computational intelligence and Design, ISCID 2021

作者： Ramzan, Zaki Li, Wei Zeeshan Raza, Hafiz Muhammad Sabir, Muhammad Waheed School of Electrical and Control Engineering SUST Department of Automation Xi'an China Genomics and Computational Biology Lab COMSATS University Islamabad Sahiwal Campus Pakistan Xi'An University of Science and Technology Xi'an China

ISBN: (纸本)9781665438568

Automatic liver segmentation from 3D biomedical images is an essential task for many clinical applications and also helpful to radiologists and doctors to analyze and segment the liver tumor faster and accurately. However, it is still a challenging task due to the complex background of the liver, fuzzy boundary, and different appearance of liver and tumor. The present study was performed to propose more efficient deep learning based approach for liver and tumor segmentation from 3D CT scans using V-Net and two different post-processing methods: Conditional Random Field (CRF) and Graph Cuts. In propose approach, using 3-slice input and ResNet as base network outer performs by applying CRF as a post-processing step. Results are evaluated using different metrics that are used for biomedical images processing and with different combinations of parameters such as 3-slice input using ResNet with CRF and Graph Cuts. Results were significant by the proposed approach as compared to other state-of-the- art approaches. Tumor segmentation part are better than other approaches having 96.60%, 5.10%, -1.5%, 0.96mm, and 12.76mm for DSC, VOE, RVD, ASD, and MSD respectively for liver segmentation and 83.91%, 38.92%, -2.6%, 1.13mm and 5.76mm for DSC, VOE, RVD, ASD and MSD respectively for tumor segmentation. © 2021 ieee.

关键词： Computerized tomography

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Protein Crystallization Identification via Fuzzy Model on Linear Neighborhood Representation

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1986-1995页

作者： Ding, Yijie Tang, Jijun Guo, Fei Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou 215009 Jiangsu Peoples R China Tianjin Univ Sch Comp Sci & Technol Coll Intelligence & Comp Tianjin 300072 Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA

X-ray crystallography is the most popular approach for analyzing protein 3D structure. However, the success rate of protein crystallization is very low (2-10 percent). To reduce the cost of time and resources, lots of computation-based methods are developed to detect the protein crystallization. Improving the accuracy of predicting protein crystallization is very important for the determination of protein structure by X-ray crystallography. At present, many machine learning methods are used to predict protein crystallization. In this article, we propose a Fuzzy Support Vector Machine based on Linear Neighborhood Representation (FSVM-LNR) to predict the crystallization propensity of proteins. Proteins are represented by three types of features (PsePSSM, PSSM-DWT, MMI-PS), and these features are serially combined and fed into FSVM-LNR. FSVM-LNR can filter outliers by membership score, which is calculated via reconstruction residuals of k nearest samples. To evaluate the performance of our predictive model, we test FSVM-LNR on the datasets of TRAIN3587, TEST3585 and TEST500. Our method achieves better Mathew's correlation coefficient (MCC) on TRAIN3587 (MCC: 0.56) and TEST3585 (MCC: 0.58). Although the performance of independent test is not the best on TEST500, FSVM-LNR also has a certain predictability (MCC: 0.70) in the identification of protein crystallization. The good performance on the datasets proves the effectiveness of our method and the better performance on large datasets further demonstrates the stability and superiority of our method.

关键词： Protein crystallization prediction fuzzy support vector machine sparse representation membership function protein feature extraction

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Lesion Segmentation in Ultrasound Using Semi-Pixel-Wise Cycle Generative Adversarial Nets

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2555-2565页

作者： Xing, Jie Li, Zheren Wang, Biyuan Qi, Yuji Yu, Bingbin Zanjani, Farhad Ghazvinian Zheng, Aiwen Duits, Remco Tan, Tao Zhejiang Canc Hosp Hangzhou 310022 Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai 200240 Peoples R China Tokyo Inst Technol Dept Comp Tokyo 1528550 Japan Yale Univ Dept Biomed Engn New Haven CT 06520 USA German Res Ctr Artificial Intelligence Robot Innovat Ctr Bremen Germany Eindhoven Univ Technol Dept Elect Engn NL-5612 AZ Eindhoven Netherlands Eindhoven Univ Technol Dept Math & Comp Sci NL-5612 AZ Eindhoven Netherlands

Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the breast cancer. The area and the shape information of a lesion is very helpful for clinicians to make diagnostic decisions. In this study we propose a new deep-learning scheme, semi-pixel-wise cycle generative adversarial net (SPCGAN) for segmenting the lesion in 2D ultrasound. The method takes the advantage of a fully convolutional neural network (FCN) and a generative adversarial net to segment a lesion by using prior knowledge. We compared the proposed method to a fully connected neural network and the level set segmentation method on a test dataset consisting of 32 malignant lesions and 109 benign lesions. Our proposed method achieved a Dice similarity coefficient (DSC) of 0.92 while FCN and the level set achieved 0.90 and 0.79 respectively. Particularly, for malignant lesions, our method increases the DSC (0.90) of the fully connected neural network to 0.93 significantly (p<0.001). The results show that our SPCGAN can obtain robust segmentation results. The framework of SPCGAN is particularly effective when sufficient training samples are not available compared to FCN. Our proposed method may be used to relieve the radiologists' burden for annotation.

关键词： Lesion segmentation deep learning generative adversarial networks breast cancer ultrasound image analysis

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Colored Network Motif Analysis by Dynamic Programming Approach: An Application in Host Pathogen Interaction Network

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第2期18卷 550-561页

作者： Biswas, Sourav Ray, Sumanta Bandyopadhyay, Sanghamitra Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India Aliah Univ Comp Sci Dept Kolkata 700160 India

Network motifs are subgraphs of a network which are found with significantly higher frequency than that expected in similar random networks. Motifs are small building blocks of a network and they have emerged as a way to uncover topological properties of complex networks. A special yet not much explored type of motif is the 'colored motif' where color (type) of each node, and hence the edges, in the motif is distinguishable from each other. A traditional motif is defined as a recurring structure in a network, whereas colored motif introduces detailed information about the color of the nodes. G-trie is a data structure to efficiently store a given set of subgraphs by exploiting the topological overlaps within them. In this article we have implemented a modified g-trie to store colored subgraphs and developed a method to discover colored motifs. Our method uses an approximate enumeration for counting the subgraphs to reduce the runtime. We have applied our method to find colored motifs of size three in a host pathogen protein-protein interaction network having two types of proteins namely HIV-1 and human proteins, and four types of edges. Here, we have discovered eight motifs, six of which contain both HIV-1 and human proteins, while the remaining two contain only human proteins.

关键词： Image color analysis Proteins Data structures Color Pathogens Complex networks Network motif colored network motif G-trie G-table network analysis host pathogen interaction network modified G-trie

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CCFS: A Confidence-Based Cost-Effective Feature Selection Scheme for Healthcare Data Classification

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第3期18卷 902-911页

作者： Chen, Yiyuan Wang, Yufeng Cao, Liang Jin, Qun Nanjing Univ Posts & Telecommun Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Engn Res Ctr Hlth Serv Syst Based Ubiquitous Wire Jiangsu Key Lab Wireless Commun Nanjing 210003 Jiangsu Peoples R China Alibaba Grp Hangzhou 310052 Zhejiang Peoples R China Waseda Univ Networked Informat Syst Lab Dept Human Informat & Cognit Sci Fac Human Sci Saitama 3591192 Japan

Feature selection (FS) is one of the fundamental data processing techniques in various machine learning algorithms, especially for classification of healthcare data. However, it is a challenging issue due to the large search space. Binary Particle Swarm Optimization (BPSO) is an efficient evolutionary computation technique, and has been widely used in FS. In this paper, we proposed a Confidence-based and Cost-effective feature selection (CCFS) method using BPSO to improve the performance of healthcare data classification. Specifically, first, CCFS improves search effectiveness by developing a new updating mechanism that designs the feature confidence to explicitly take into account the fine-grained impact of each dimension in the particle on the classification performance. The feature confidence is composed of two measurements: the correlation between feature and categories, and historically selected frequency of each feature. Second, considering the fact that the acquisition costs of different features are naturally different, especially for medical data, and should be fully taken into account in practical applications, besides the classification performance, the feature cost and the feature reduction ratio are comprehensively incorporated into the design of fitness function. The proposed method has been verified in various UCI public datasets and compared with various benchmark schemes. The thoroughly experimental results show the effectiveness of the proposed method, in terms of accuracy and feature selection cost.

关键词： Feature extraction Particle swarm optimization Medical services Classification algorithms Genetic algorithms Error analysis Machine learning algorithms Data classification feature selection binary particle swarm optimization swarm intelligence

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AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1831-1840页

作者： Zhang, Jinhao Zhang, Zehua Pu, Lianrong Tang, Jijun Guo, Fei Tianjin Univ Sch Comp Sci & Technol Coll Intelligence & Comp Tianjin 300350 Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China Tianjin Univ Key Lab Syst Bioengn Minist Educ Tianjin 300072 Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA

Anti-inflammatory peptides (AIEs) have recently emerged as promising therapeutic agent for treatment of various inflammatory diseases, such as rheumatoid arthritis and Alzheimer's disease. Therefore, detecting the correlation between amino acid sequence and its anti-inflammatory property is of great importance for the discovery of new AIEs. To address this issue, we propose a novel prediction tool for accurate identification of peptides as anti-inflammatory epitopes or non anti-inflammatory epitopes. Most of all, we encode the original peptide sequence for better mining and exploring the information and patterns, based on the three feature representations as amino acid contact, position specific scoring matrix, physicochemical property. At the same time, we exploit several feature extraction models and utilize one feature selection model, in order to construct many base classifiers from various feature representations. More specifically, we develop an effective classification model, with which we can extract and learn a set of informative features from the ensemble classifier chain model with different group of base classifiers. Furthermore, in order to test the predictive power of our model, we conduct the comparative experiments on the leave-one-out cross-validation and the independent test. It shows that our novel predictor performs great accurate for identification of AIEs as well as existing outstanding prediction tools. Source codes are available at https://***/guofei-tju/Ensemble-classifier-chain-model.

关键词： Anti-inflammatory peptides feature representation amino acid contact feature extraction ensemble classifier chain

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