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Identification of Drug-Drug Interactions Using Chemical Interactions

CURRENT BIOINFORMATICS 2017年第6期12卷 526-534页

作者： Chen, Lei Chu, Chen Zhang, Yu-Hang Zheng, Mingyue Zhu, LiuCun Kong, Xiangyin Huang, Tao Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Inst Biochem & Cell Biol Shanghai 200031 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Inst Hlth Sci Shanghai 200031 Peoples R China Chinese Acad Sci Shanghai Inst Mat Med Drug Discovery & Design Ctr State Key Lab Drug Res Shanghai 201203 Peoples R China Shanghai Univ Coll Life Sci Shanghai 200444 Peoples R China

Background: One drug can affect the activity of another when they are administered together, which can cause adverse drug reactions or sometimes improve therapeutic effects. Therefore, correct identification of drug-drug interactions (DDIs) can help medical workers use various drugs effectively, avoiding adverse effects and improving therapeutic effects. Methods: This study proposed a novel prediction model to identify DDIs. A new metric was constructed to evaluate the similarity of two pairs of drugs using chemical interaction information retrieved from STITCH. Validated DDIs retrieved from DrugBank were employed, from which we constructed all possible pairs of drugs that were deemed as negative samples. The whole dataset was divided into one training dataset and one test dataset. To address the imbalanced data, a complicated dataset compilation strategy was adopted to construct nine training datasets from the original training dataset, reducing the ratio of positive samples and negative samples. Nine predictors based on the nearest neighbor algorithm were built based on these training datasets. The proposed model integrated the above nine predictors by majority voting and its performance was evaluated on the test dataset. Results: The predicted results indicate that the method is quite effective for identification of DDIs. Finally, we also discussed the ability of the method for identifying novel DDIs by investigating the likelihood of some negative samples in the test dataset that were predicted as DDIs being novel DDIs. Conclusion: The proposed method has a good ability for identification of potential DDIs.

关键词： Drug-drug interaction chemical interaction chemical structure similarity nearest neighbor algorithm majority voting imbalanced dataset

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Bayesian Recognition of Inflammatory Processes in Brain Gliomas

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CYBERNETICS AND SYSTEMS ANALYSIS 2017年第3期53卷 366-372页

作者： Gridina, N. Ja. Gupal, A. M. Tarasov, A. L. Natl Acad Med Sci Ukraine AP Romodanov Neurosurg Inst Kiev Ukraine Natl Acad Sci Ukraine VM Glushkov Inst Cybernet Kiev Ukraine

Application of Bayesian recognition procedures to erythrocyte sedimentation rate in brain gliomas has allowed detecting inflammatory processes in a human body. The analysis of results of the recognition methods based on tree network methods, Markov chains, and nearest neighbor algorithm has shown that Bayesian procedure was the most efficient.

关键词： Bayesian recognition procedures brain gliomas Markov chain nearest neighbor algorithm

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Detection of Deception Using Facial Expressions Based on Different Classification algorithms

Detection of Deception Using Facial Expressions Based on Dif...

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Scientific Conference of Electrical Engineering (SCEE)

作者： Harith H. Thannoon Wissam H. Ali Ivan A. Hashim Department of Electrical Engineering Nineveh University Mosul Iraq Department of Electrical Engineering University of Technology-Iraq Baghdad Iraq

ISBN: (纸本)9781728115887;9781728115870

Most psychologists argue that facial behavioral during lying is different from facial behavioral when telling the truth, so the facial behavioral can be used as reliable indicators for spotting liars. The deception detection systems (DDSs) based on facial expressions are non-invasive, mobile and cost-effective. In this work a DDS is dependent on Facial Action Coding System (FACS) for facial features extraction, the main idea of FACS is to describe all facial actions using Action Units (AUs), each AU is related to movement one or more facial muscles. Eight AUs are used which incorporated into a single facial behavior pattern vector; these AUs are AUs 5, 6, 7, 10, 12, 14, 23, and 28. Datasets are collected for 43 subjects (20males, 23 females) most of them between ages 18-25. Four types of classification algorithms are used individually in the last stage of the proposed system; these classifiers are MLP, KNN, VG-RAM, and SVM. The simulation results show that the best results are obtaining when using VG-RAM and KNN classifiers. The main contributions of this work are new classification techniques in DDS, collect real database that can use to measure the performance of any DDS based on facial expressions, and select suitable facial features.

关键词： face recognition feature extraction image classification image coding multilayer perceptrons muscle nearest neighbour methods object detection random-access storage support vector machines Facial Expression random access machine Multilayer perceptrons Feature extraction Object detection Deception image coding Support Vector Network Image classification direct digital synthesis Sorting algorithm Facial Recognition nearest neighbor algorithm Face Face

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High-Resolution Systems for Automated Diagnosis of Hepatitis

High-Resolution Systems for Automated Diagnosis of Hepatitis

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Scientific Conference of Electrical Engineering (SCEE)

作者： Hadeel N. Abdullah Bassam H. Abd Sara H. Muhi Department of Electrical Engineering University of Technology - Iraq Baghdad Iraq

ISBN: (纸本)9781728115887;9781728115870

This study aims to optimize the accuracy of diseases diagnosis, where many studies have been conducted to challenge the highest diagnostic accuracy of hepatitis disease because the early and correct diagnosis increases the chance of saving the patient's life from this deadly disease. Therefore, in this paper, we have done a good test for three classifications, namely: support vector machine (SVM), multilayer perceptron (MLP) and K-nearest neighbor (KNN). The accuracy of the KNN overcome the rest of the classifier with 100% accuracy for the diagnosis of hepatitis disease. We used the same division of data used in previous works for a fair comparison using the datasets gotten from the UCI machine learning database, with 19 features. This result is the best yet.

关键词： diseases medical diagnostic computing multilayer perceptrons nearest neighbour methods patient diagnosis pattern classification support vector machines medical diagnostic computing Multilayer perceptrons patient diagnosis Support Vector Network Pattern recognition automatic diagnosis Hepatitis Disease diagnosis maladjustment nearest neighbor algorithm

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Classification of Subjects with Parkinson's Disease Using Gait Data Analysis

Classification of Subjects with Parkinson's Disease Using Ga...

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International Conference on Automation and Computational Engineering (ICACE)

作者： Yash Mittra Vipul Rustagi Department of Information Technology Guru Gobind Singh Indraprastha University New Delhi India

ISBN: (纸本)9781538654651;9781538654644

Gait Analysis of Parkinson's Disease (PD) Patients and Control Objects has been analyzed to show differences in PD Patients and Control Objects. Using data provided by Phisonet's Gaitpdb database (in which 8 sensors have been applied to each foot of the subjects to calculate the Vertical Ground Reaction Forces (VGRF)), data compression has been performed using 7 statistical functions to get a representative image of the data. The statistical functions namely Minimum, Maximum, Mean, Median, Standard Deviation, Skewness, and Kurtosis have been used to compress over 3 million tuples into 310 tuples. Finally, various Machine Learning techniques have been applied to the transformed dataset to perform detection of Parkinson's Disease. The classification has been performed using Logistic Regression, Decision Trees, Random Forest, SVM (Linear Kernel), SVM (RBF Kernel), SVM (Poly Kernel) and k-nearest Neighbours. Experiments with Principal Component Analysis for data compression have also been performed and their incompetence (with reasons) has been stated.

关键词： data analysis data compression decision trees diseases gait analysis medical diagnostic computing nearest neighbour methods pattern classification principal component analysis radial basis function networks random forests regression analysis support vecto medical diagnostic computing Radial basis function networks Data compression gait analysis Decision Trees Pattern recognition principal components analysis Parkinson Disease nearest neighbor algorithm Data analysis regression analysis maladjustment statistical distribution

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The Design and Application of Multi-Sensor Data Fusion in Open System Architecture of Avionics 12

The Design and Application of Multi-Sensor Data Fusion in Op...

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12th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Li, Jing Hu, Xiaoguang Huang, Dayu Zhou, Qing Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Key Lab Sci & Technol Avion Integrat Technol Shanghai 200233 Peoples R China

ISBN: (纸本)9781509061617

To meet the pressing needs of developments in avionics and military capability, in this paper, an application instance of fundamental multi-radars data fusion based on Kalman filtering to open system architecture platform of avionics is proposed and carried out. The multi-radar data fusion process scheme is designed in four core modules: coordinate conversion module, data compressing module, the nearest neighbor algorithm module and Kalman filtering algorithm module. Based on the open system architecture and relevant hierarchical design, the application platform of data fusion system is built up. A friendly radar stimulation man-machine interface is designed to vividly show the whole radar detection process and achieve the data detection targets. Finally, the data fusion process is successfully applied in open system architecture and efficiently realizes the real-time tasks. Detailed data fusion results arc shown in several broken line graphs.

关键词： Data Fusion Kalman filtering Open System Architecture of Avionics nearest neighbor algorithm linear system

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Weighted Passive nearest neighbor algorithm with Feature Scaling:a Newly-Developed Supervised Classifier

Weighted Passive Nearest Neighbor Algorithm with Feature Sca...

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2011 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS 2011)

作者： KaiYan Feng,JunHui Gao,Lei Liu,YiXue Li Shanghai Center for Bioinformatics Technology,100,Qinzhou Road,Shanghai,China. YuDong Cai the Institute of Systems Biology,Shanghai University,Shanghai,China. KaiRui Feng Simcyp Limited,Blades Enterprise Centre,John Street,Sheffield S2 4SU,United Kingdom

K nearest neighbor algorithm (k-NN) is an instance-based lazy classifier that does not need to delineate the entire boundaries between *** some classification tasks that constantly need a training procedure may favor k-NN if high efficiency is ***,k-NN is prone to be affected by the underlying data *** this paper,we define a new neighborhood relationship,called passive nearest neighbors,which is deemed to be able to counteract with the variation of data *** on which we develop a new classifier called weighted passive nearest neighbor algorithm (WPNNA).Furthermore,WPNNA is equipped with feature scaling,abbreviated as WPNNA-FS,to improve the *** results show that WPNNA is a good complement to the current state-of-the-art of classification,and WPNNA-FS outperforms other classifiers on the majority of some benchmark datasets.

关键词： Lazy Classifier nearest neighbor algorithm Weighted Passive nearest neighbor algorithm

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An Improved Method for Detecting False Alarm in Arrhythmia ICU Patients

An Improved Method for Detecting False Alarm in Arrhythmia I...

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IEEE International Conference on Research in Computational Intelligence and Communication Networks

作者： Tishya Manna Aleena Swetapadma School of Computer Engineering KIIT Deemed to be University

ISBN: (纸本)9781538676394;9781538676387

In this work false alarm have been detected in intensive care unit (ICU) patients suffering from arrhythmia based on k nearest neighbor (k-NN) algorithm. The input features used are electrocardiogram (ECG) and arterial blood pressure (ABP). Proposed k-NN based method works better with three nearest neighbors and Euclidean distance matrix. Proposed method has accuracy up to 99.95% for all the test samples. Hence it can be implemented in real time in hospitals.

关键词： Electrocardiogram nearest neighbor algorithm Arrhythmia ABP ICU patients

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Quadtree-Based KNN Search on Road Networks

Quadtree-Based KNN Search on Road Networks

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International Conference on Computer Technology, Electronics and Communication (ICCTEC)

作者： Yiru Wen Hailing Xiong College of Computer and Information Science Southwest University ChongQing China Business College of Southwest University ChongQing China

ISBN: (纸本)9781538657850

This paper proposed a novel k-nearest neighbor search algorithm based on quadtree, called the QkNN. Three steps are made in the QkNN. First, moving objects are indexed by quadtree and the cell where the query point q is located is determined. Furthermore, query scope is extended according to the smallest nearest neighbor cell around q, the cells where k-nearest neighbor moving objects are located is initially determined, the search space is effectively pruned. Finally, knearest neighbor moving objects are found by sorting the road distance between q and objects in search scope. The analysis and experimental results show that the algorithm has good performance in large amount of data and uneven distribution of moving objects.

关键词： learning (artificial intelligence) nearest neighbour methods pattern classification quadtrees query processing search problems traffic information systems quaternary tree Traffic information systems tabu search query analysis Pattern recognition road network nearest neighbor algorithm neighbor Moving

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Predicting miRNA's target from primary structure by the nearest neighbor algorithm

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MOLECULAR DIVERSITY 2010年第4期14卷 719-729页

作者： Lin, Kao Qian, Ziliang Lu, Lin Lu, Lingyi Lai, Lihui Gu, Jieyi Zeng, Zhenbing Li, Haipeng Cai, Yudong Chinese Acad Sci Shanghai Inst Biol Sci CAS MPG Partner Inst Computat Biol Shanghai 200031 Peoples R China Chinese Acad Sci Grad Sch Beijing 100039 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Key Lab Mol Syst Biol Bioinformat Ctr Shanghai 200031 Peoples R China Shanghai Jiao Tong Univ Dept Biomed Engn Shanghai 200040 Peoples R China E China Normal Univ Sch Life Sci Shanghai 200062 Peoples R China E China Normal Univ Inst Software Engn Shanghai 200062 Peoples R China Shanghai Univ Inst Syst Biol Shanghai 200444 Peoples R China

We used a machine learning method, the nearest neighbor algorithm (NNA), to learn the relationship between miRNAs and their target proteins, generating a predictor which can then judge whether a new miRNA-target pair is true or not. We acquired 198 positive (true) miRNA-target pairs from Tarbase and the literature, and generated 4,888 negative (false) pairs through random combination. A 0/1 system and the frequencies of single nucleotides and di-nucleotides were used to encode miRNAs into vectors while various physicochemical parameters were used to encode the targets. The NNA was then applied, learning from these data to produce a predictor. We implemented minimum redundancy maximum relevance (mRMR) and properties forward selection (PFS) to reduce the redundancy of our encoding system, obtaining 91 most efficient properties. Finally, via the Jackknife cross-validation test, we got a positive accuracy of 69.2% and an overall accuracy of 96.0% with all the 253 properties. Besides, we got a positive accuracy of 83.8% and an overall accuracy of 97.2% with the 91 most efficient properties. A web-server for predictions is also made available at http://***:8080/miRTP/***.

关键词： miRNA Target Predict nearest neighbor algorithm Minimum redundancy maximum relevance Properties forward selection

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