Artificial Intelligence in Medicine RSS feed: Current Issue. Artificial Intelligence in Medicine publishes original articles from a wide variety of interdisciplinary perspectives concerning the theory and practice of artificial intelligence (AI) in medicine, human biology, and health care. Particular attention is given to: • AI-based clinical decision making • medical knowledge engineering • knowledge-based and agent-based systems • computational intelligence in bio- and clinical medicine • intelligent medical information systems • AI in medical education • intelligent devices and instruments • automated reasoning and metareasoning in medicine • methodological, philosophical, ethical, and social issues of AI in medicine AIIM features: • original research contributions • methodological reviews • survey papers • special issue articles • position papers • historical perspectives • editorials • guest editorials • letters to the editor • book reviews This journal is included in CITE - The Computational Intelligence Website !http://www.aiimjournal.com/?rss=yesElsevier Inc.en © 2010 Published by Elsevier Inc. All rights reserved. Artificial Intelligence in Medicine0933-3657482-3February 2010 © 2010 Published by Elsevier Inc. All rights reserved. healthpermissions@elsevier.comhttp://www.aiimjournal.com/article/PIIS0933365710000072/abstract?rss=yesEditorial Board10.1016/S0933-3657(10)00007-2Artificial Intelligence in Medicine 48, 2 (2010)2010-02-01Artificial Intelligence in Medicine2010-02-01482-3S0933-3657(10)X0002-1CO2CO2http://www.aiimjournal.com/article/PIIS0933365709000980/abstract?rss=yesThe advances of high-throughput biotechnologies have shifted the focus of biomedical science from studying individual molecules towards analysing the interactions of the complex molecular and cellular networks that control whole biological systems. This greatly fosters the collaborative interactions between engineering, informatics, and biomedical science, and prompts the emergence of systems biology and systems medicine that aims to understand how the individual components of a biological system interact in time and space to determine the functioning of the system and how an appropriate approach can be developed for the effective treatment of diseases.Artificial intelligence in biomedical engineering and informatics: An introduction and reviewYonghong Peng, Yufeng Zhang, Lipo Wang10.1016/j.artmed.2009.07.007Artificial Intelligence in Medicine 48, 2 (2010)2010-02-01Artificial Intelligence in Medicine2010-02-01482-3S0933-3657(10)X0002-1Guest Editorial7173http://www.aiimjournal.com/article/PIIS0933365709000979/abstract?rss=yesAbstract: Objective: The limitation of small sample size of functional genomics experiments has made it necessary to integrate DNA microarray experimental data from different sources. However, experimentation noises and biases of different microarray platforms have made integrated data analysis challenging. In this work, we propose an integrative computational framework to identify candidate biomarker genes from publicly available functional genomics studies.Methods: We developed a new framework, Gaussian Mixture Modeling-Coupled Information Gain (GMM-IG). In this framework, we first apply a two-component Gaussian mixture model (GMM) to estimate the conditional probability distributions of gene expression data between two different types of samples, for example, normal versus cancer. An expectation-maximization algorithm is then used to estimate the maximum likelihood parameters of a mixture of two Gaussian models in the feature space and determine the underlying expression levels of genes. Gene expression results from different studies are discretized, based on GMM estimations and then unified. Significantly differentially-expressed genes are filtered and assessed with information gain (IG) measures.Results: DNA microarray experimental data for lung cancers from three different prior studies was processed using the new GMM-IG method. Target gene markers from a gene expression panel were selected and compared with several conventional computational biomarker data analysis methods. GMM-IG showed consistently high accuracy for several classification assessments. A high reproducibility of gene selection results was also determined from statistical validations. Our study shows that the GMM-IG framework can overcome poor reliability issues from single-study DNA microarray experiment while maintaining high accuracies by combining true signals from multiple studies.Conclusions: We present a conceptually simple framework that enables reliable integration of true differential gene expression signals from multiple microarray experiments. This novel computational method has been shown to generate interesting biomarker panels for lung cancer studies. It is promising as a general strategy for future panel biomarker development, especially for applications that requires integrating experimental results generated from different research centers or with different technology platforms.A GMM-IG framework for selecting genes as expression panel biomarkersMingyi Wang, Jake Y. Chen10.1016/j.artmed.2009.07.006Artificial Intelligence in Medicine 48, 2 (2010)2010-02-01Artificial Intelligence in Medicine2010-02-01482-3S0933-3657(10)X0002-1Special Issue Articles7582http://www.aiimjournal.com/article/PIIS0933365709001031/abstract?rss=yesAbstract: Objective: In recent years, several machine learning approaches have been applied to modeling the specificity of the human immunodeficiency virus type 1 (HIV-1) protease cleavage domain. However, the high dimensional domain dataset contains a small number of samples, which could misguide classification modeling and its interpretation. Appropriate feature selection can alleviate the problem by eliminating irrelevant and redundant features, and thus improve prediction performance.Methods: We introduce a new feature subset selection method, FS-MLP, that selects relevant features using multi-layered perceptron (MLP) learning. The method includes MLP learning with a training dataset and then feature subset selection using decompositional approach to analyze the trained MLP. Our method is able to select a subset of relevant features in high dimensional, multi-variate and non-linear domains.Results: Using five artificial datasets that represent four data types, we verified the FS-MLP performance with seven other feature selection methods. Experimental results showed that the FS-MLP is superior at high dimensional, multi-variate and non-linear domains. In experiments with HIV-1 protease cleavage dataset, the FS-MLP selected a set of 14 highly relevant features among 160 original features. On a validation set of 131 test instances, classifiers that used the 14 features showed about 95% accuracy which outperformed other seven methods in terms of accuracy and the number of features.Conclusions: Our experimental results indicate that the FS-MLP is effective in analyzing multi-variate, non-linear and high dimensional datasets such as HIV-1 protease cleavage dataset. The 14 relevant features which were selected by the FS-MLP provide us with useful insights into the HIV-1 cleavage site domain as well. The FS-MLP is a useful method for computational sequence analysis in general.An MLP-based feature subset selection for HIV-1 protease cleavage site analysisGilhan Kim, Yeonjoo Kim, Heuiseok Lim, Hyeoncheol Kim10.1016/j.artmed.2009.07.010Artificial Intelligence in Medicine 48, 2 (2010)2010-02-01Artificial Intelligence in Medicine2010-02-01482-3S0933-3657(10)X0002-1Special Issue Articles8389http://www.aiimjournal.com/article/PIIS0933365709001006/abstract?rss=yesAbstract: Objective: The importance of gene expression data in cancer diagnosis and treatment has become widely known by cancer researchers in recent years. However, one of the major challenges in the computational analysis of such data is the curse of dimensionality because of the overwhelming number of variables measured (genes) versus the small number of samples. Here, we use a two-step method to reduce the dimension of gene expression data and aim to address the problem of high dimensionality.Methods: First, we extract a subset of genes based on statistical characteristics of their corresponding gene expression levels. Then, for further dimensionality reduction, we apply diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set, in order to obtain efficient representation of data geometric descriptions. Finally, a neural network clustering theory, fuzzy ART, is applied to the resulting data to generate clusters of cancer samples.Results: Experimental results on the small round blue-cell tumor data set, compared with other widely used clustering algorithms, such as the hierarchical clustering algorithm and K-means, show that our proposed method can effectively identify different cancer types and generate high-quality cancer sample clusters.Conclusion: The proposed feature selection methods and diffusion maps can achieve useful information from the multidimensional gene expression data and prove effective at addressing the problem of high dimensionality inherent in gene expression data analysis.Clustering of high-dimensional gene expression data with feature filtering methods and diffusion mapsRui Xu, Steven Damelin, Boaz Nadler, Donald C. Wunsch10.1016/j.artmed.2009.06.001Artificial Intelligence in Medicine 48, 2 (2010)2010-02-01Artificial Intelligence in Medicine2010-02-01482-3S0933-3657(10)X0002-1Special Issue Articles9198http://www.aiimjournal.com/article/PIIS093336570900102X/abstract?rss=yesAbstract: Objective: Schizophrenia is a chronic psychiatric disorder that affects about 1% of the population globally. A tremendous amount of effort has been expended in the past decade, including more than 2400 association studies, to identify genes influencing susceptibility to the disorder. However, few genes or markers have been reliably replicated. The wealth of this information calls for an integration of gene association data, evidence-based gene ranking, and follow-up replication in large sample. The objective of this study is to develop and evaluate evidence-based gene ranking methods and to examine the features of top-ranking candidate genes for schizophrenia.Methods: We proposed a gene-based approach for selecting and prioritizing candidate genes by combining odds ratios (ORs) of multiple markers in each association study and then combining ORs in multiple studies of a gene. We named it combination–combination OR method (CCOR). CCOR is similar to our recently published method, which first selects the largest OR of the markers in each study and then combines these ORs in multiple studies (i.e., selection–combination OR method, SCOR), but differs in selecting representative OR in each study. Features of top-ranking genes were examined by Gene Ontology terms and gene expression in tissues.Results: Our evaluation suggested that the SCOR method overall outperforms the CCOR method. Using the SCOR, a list of 75 top-ranking genes was selected for schizophrenia candidate genes (SZGenes). We found that SZGenes had strong correlation with neuro-related functional terms and were highly expressed in brain-related tissues.Conclusion: The scientific landscape for schizophrenia genetics and other complex disease studies is expected to change dramatically in the next a few years, thus, the gene-based combined OR method is useful in candidate gene selection for follow-up association studies and in further artificial intelligence in medicine. This method for prioritization of candidate genes can be applied to other complex diseases such as depression, anxiety, nicotine dependence, alcohol dependence, and cardiovascular diseases.Gene- and evidence-based candidate gene selection for schizophrenia and gene feature analysisJingchun Sun, Leng Han, Zhongming Zhao10.1016/j.artmed.2009.07.009Artificial Intelligence in Medicine 48, 2 (2010)2010-02-01Artificial Intelligence in Medicine2010-02-01482-3S0933-3657(10)X0002-1Special Issue Articles99106http://www.aiimjournal.com/article/PIIS0933365709001018/abstract?rss=yesAbstract: Objective: Many complex pathways are described as hierarchical structures in which a pathway is recursively partitioned into several sub-pathways, and organized hierarchically as a tree. The hierarchical structure provides a natural way to visualize the global structure of a complex pathway. However, none of the previous research on pathway visualization explores the hierarchical structures provided by many complex pathways. In this paper, we aim to develop algorithms that can take advantages of hierarchical structures, and give layouts that explore the global structures as well as local structures of pathways.Methods: We present a new hierarchically organized layout algorithm to produce layouts for hierarchically organized pathways. Our algorithm first decomposes a complex pathway into sub-pathway groups along the hierarchical organization, and then partition each sub-pathway group into basic components. It then applies conventional layout algorithms, such as hierarchical layout and force-directed layout, to compute the layout of each basic component. Finally, component layouts are joined to form a final layout of the pathway. Our main contribution is the development of algorithms for decomposing pathways and joining layouts.Results: Experiment shows that our algorithm is able to give comprehensible visualization for pathways with hierarchies, cycles as well as complex structures. It clearly renders the global component structures as well as the local structure in each component. In addition, it runs very fast, and gives better visualization for many examples from previous related research.Hierarchically organized layout for visualization of biochemical pathwaysJyh-Jong Tsay, Bo-Liang Wu, Yu-Sen Jeng10.1016/j.artmed.2009.06.002Artificial Intelligence in Medicine 48, 2 (2010)2010-02-01Artificial Intelligence in Medicine2010-02-01482-3S0933-3657(10)X0002-1Special Issue Articles107117http://www.aiimjournal.com/article/PIIS0933365709001043/abstract?rss=yesAbstract: Objective: To develop regulatory network to explore and model the regulatory relationships of protein biomarkers and classify different disease groups.Methods: Regulatory network is constructed to be a hopfield-like network with nodes representing biomarkers and directional connections to be regulations in between. The input to the network is the measured expression levels of biomarkers, and the output is the summation of regulatory strengths from other biomarkers. The network is optimized towards minimizing the energy function that is defined as the measure of the disagreement between the input and output of the network. To simulate more complicated regulations, a sigmoid kernel function is imposed on each node to construct a non-linear regulatory network.Results: Two datasets have been used as test beds, one dataset includes patients of nasopharyngeal carcinoma with different responses to chemotherapy drug, and the other consists of patients of severe acute respiratory syndrome, influenza, and control normals. The regulatory networks among protein biomarkers were reconstructed for different disease conditions in each dataset. We demonstrated our methods have better classification capability when comparing with conventional methods including Fisher linear discriminant (FLD), K-nearest neighborhood (KNN), linear support vector machines (linSVM) and radial basis function based support vector machines (rbfSVM).Conclusion: The derived networks can effectively capture the unique regulatory patterns of protein markers associated with different patient groups and hence can be used for disease classification. The discovered regulation relationships can potentially provide insights to revealing the molecular signaling pathways.In this paper, a novel technique of regulatory network is proposed on purpose of modeling biomarker regulations and classifying different disease groups. The network is composed of a certain number of nodes that are directionally connected in between in which nodes denote predictors and connections to be the regulation relationship. The network is optimized towards minimizing its energy function with biomarker expression data acquired from a specific patient group, thus the optimized network can model the regulatory relationship of biomarkers under the same circumstance. To simulate more complicated regulations, a sigmoid kernel function is imposed on each node to construct a non-linear regulatory network. The regulatory network can extract unique features of each disease condition, thus one immediate application of regulatory network is to classifying different diseases. We demonstrated that regulatory network is capable of performing disease classification through comparing with conventional methods including FLD, KNN, linSVM and rbfSVM on two protein datasets. We believe our method is promising in mining knowledge of protein regulations and be powerful for disease classification.Method of regulatory network that can explore protein regulations for disease classificationHong Qiang Wang, Hai Long Zhu, William C.S. Cho, Timothy T.C. Yip, Roger K.C. Ngan, Stephen C.K. Law10.1016/j.artmed.2009.07.011Artificial Intelligence in Medicine 48, 2 (2010)2010-02-01Artificial Intelligence in Medicine2010-02-01482-3S0933-3657(10)X0002-1Special Issue Articles119127http://www.aiimjournal.com/article/PIIS0933365709000992/abstract?rss=yesAbstract: Objective: Accurate cancer prognosis prediction is critical to cancer treatment. There have been many prognosis models based on clinical markers, but few of them are satisfied in clinical applications. And with the development of microarray technologies, cancer researchers have discovered many genes as new markers from the gene expression data and have further developed powerful prognosis models based on these so-called genetic biomarkers. However, the application of such biomarkers still suffers from some problems. The first one is there are a great number of genes and a few samples in the gene expression data so that it is difficult to select a unified gene set to establish a stable classifier for prognosis. The second one is that, due to the experimental and technical reasons, there are existing noises and redundancies in gene expression data, which may lead to building a prognosis predictor with poor performance. The last but not the least one is the microarray experiments are so expensive currently that it is hard to obtain abundant samples. Therefore, it is practical to develop prognosis methods mainly based on conventional clinical markers in real cancer treatment applications. This paper aims to establish an accurate classification model for cancer prognosis, in order to make full use of the invaluable information in clinical data, especially which is usually ignored by most of the existing methods when they aim for high prediction accuracies.Methods: First, this paper gives the formal description of general classification problem, and presents a novel mixture classification model to make full use of the invaluable information in clinical data, which is similar to the traditional ensemble classification models except for putting strict constraints on the construction of mapping functions to avoid voting process. Then, a two-layer instance of the proposed model, named as MRS (Mixture of Rough set and Support vector machine), is constructed by integrating rough set and support vector machine (SVM) classification methods, in which, the rough set classifier acts as the first layer to identify some singular samples in data, and the SVM classifier acts as the second layer to classify the remaining samples. Finally, MRS is used to make prognosis prediction on two open breast cancer datasets. One dataset, denoted as BRC-1 hereafter, is a high quality, publicly available dataset of 97 breast cancer tumors of node-negative patients. The other, denoted as BRC-2 hereafter, uses baseline human primary breast tumor data from LBL breast cancer cell collection containing 174 samples.Results: We have done two experiments on BRC-1 and BRC-2, respectively. In the first experiment, the BRC-1 dataset is divided into train set with 78 patients (34 ones belonging to poor prognosis group and 44 ones belonging to good prognosis group) and test set with 19 patients (12 ones belonging to poor prognosis group and 7 ones belonging to good prognosis). After trained on the train set, the MRS can correctly classify all the 12 patients with poor prognosis, and 6 of 7 patients with good prognosis in the test set. The results are better than previous researches, even better than the 70-gene based biomarkers. And in the second experiment, we construct the classifiers using BRC-2 dataset, and compare MRS with other representative methods in Weka software by 5-fold cross-validation, and comparison results show that MRS has higher prediction accuracy than those methods.Conclusions: The proposed mixture classification model can easily integrate methods with different characteristics. It can overcome the shortcomings of traditional voting-based ensemble models and thus can make full use of the information in clinical data. The experimental results illustrate that our implemented MRS classifier can predict the breast cancer prognosis more accurately than previous prognostic methods.Mixture classification model based on clinical markers for breast cancer prognosisTao Zeng, Juan Liu10.1016/j.artmed.2009.07.008Artificial Intelligence in Medicine 48, 2 (2010)2010-02-01Artificial Intelligence in Medicine2010-02-01482-3S0933-3657(10)X0002-1Special Issue Articles129137http://www.aiimjournal.com/article/PIIS0933365709001055/abstract?rss=yesAbstract: Objective: Traditional Chinese medicine (TCM) is a scientific discipline, which develops the related theories from the long-term clinical practices. The large-scale clinical data are the core empirical knowledge source for TCM research. This paper introduces a clinical data warehouse (CDW) system, which incorporates the structured electronic medical record (SEMR) data for medical knowledge discovery and TCM clinical decision support (CDS).Materials and methods: We have developed the clinical reference information model (RIM) and physical data model to manage the various information entities and their relationships in TCM clinical data. An extraction-transformation-loading (ETL) tool is implemented to integrate and normalize the clinical data from different operational data sources. The CDW includes online analytical processing (OLAP) and complex network analysis (CNA) components to explore the various clinical relationships. Furthermore, the data mining and CNA methods are used to discover the valuable clinical knowledge from the data.Results: The CDW has integrated 20,000 TCM inpatient data and 20,000 outpatient data, which contains manifestations (e.g. symptoms, physical examinations and laboratory test results), diagnoses and prescriptions as the main information components. We propose a practical solution to accomplish the large-scale clinical data integration and preprocessing tasks. Meanwhile, we have developed over 400 OLAP reports to enable the multidimensional analysis of clinical data and the case-based CDS. We have successfully conducted several interesting data mining applications. Particularly, we use various classification methods, namely support vector machine, decision tree and Bayesian network, to discover the knowledge of syndrome differentiation. Furthermore, we have applied association rule and CNA to extract the useful acupuncture point and herb combination patterns from the clinical prescriptions.Conclusion: A CDW system consisting of TCM clinical RIM, ETL, OLAP and data mining as the core components has been developed to facilitate the tasks of TCM knowledge discovery and CDS. We have conducted several OLAP and data mining tasks to explore the empirical knowledge from the TCM clinical data. The CDW platform would be a promising infrastructure to make full use of the TCM clinical data for scientific hypothesis generation, and promote the development of TCM from individualized empirical knowledge to large-scale evidence-based medicine.Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision supportXuezhong Zhou, Shibo Chen, Baoyan Liu, Runsun Zhang, Yinghui Wang, Ping Li, Yufeng Guo, Hua Zhang, Zhuye Gao, Xiufeng Yan10.1016/j.artmed.2009.07.012Artificial Intelligence in Medicine 48, 2 (2010)2010-02-01Artificial Intelligence in Medicine2010-02-01482-3S0933-3657(10)X0002-1Special Issue Articles139152http://www.aiimjournal.com/article/PIIS0933365709001602/abstract?rss=yesAbstract: Objective: Re-construction of a genetic regulatory network from a given time-series gene expression data is an important research topic in systems biology. One of the main difficulties in building a genetic regulatory network lies in the fact that practical data set has a huge number of genes vs. a small number of sampling time points. In this paper, we propose a new linear regression model that may overcome this difficulty for uncovering the regulatory relationship in a genetic network.Methods: The proposed multiple regression model makes use of the scale-free property of a real biological network. In particular, a filter is constructed by using this scale-free property and some appropriate statistical tests to remove redundant interactions among the genes. A model is then constructed by minimizing the gap between the observed and the predicted data.Results: Numerical examples based on yeast gene expression data are given to demonstrate that the proposed model fits the practical data very well. Some interesting properties of the genes and the underlying network are also observed.Conclusions: In conclusion, we propose a new multiple regression model based on the scale-free property of real biological network for genetic regulatory network inference. Numerical results using yeast cell cycle gene expression dataset show the effectiveness of our method. We expect that the proposed method can be widely used for genetic network inference using high-throughput gene expression data from various species for systems biology discovery.A new multiple regression approach for the construction of genetic regulatory networksShu-Qin Zhang, Wai-Ki Ching, Nam-Kiu Tsing, Ho-Yin Leung, Dianjing Guo10.1016/j.artmed.2009.11.001Artificial Intelligence in Medicine 48, 2 (2010)2010-02-01Artificial Intelligence in Medicine2010-02-01482-3S0933-3657(10)X0002-1Special Issue Articles153160http://www.aiimjournal.com/article/PIIS0933365709001614/abstract?rss=yesAbstract: Objective: The purpose of this study was to integrate knowledge about drugs, drug targets, and topological methods. The goals were to build a system facilitating the study of adverse drug events, to make it easier to find possible explanations, and to group similar drug–drug interaction cases in the adverse drug reaction reports from the US Food and Drug Administration (FDA).Methods: We developed a system that analyses adverse drug reaction (ADR) cases reported by the FDA. The system contains four modules. First, we integrate drug and drug target databases that provide information related to adverse drug reactions. Second, we classify drug and drug targets according to anatomical therapeutic chemical classification (ATC) and drug target ontology (DTO). Third, we build drug target networks based on drug and drug target databases. Finally, we apply topological analysis to reveal drug interaction complexity for each ADR case reported by the FDA.Results: We picked 1952 ADR cases from the years 2005–2006. Our dataset consisted of 1952 cases, of which 1471 cases involved ADR targets, 845 cases involved absorption, distribution, metabolism, and excretion (ADME) targets, and 507 cases involved some drugs acting on the same targets, namely, common targets (CTs). We then investigated the cases involving ADR targets, ADME targets, and CTs using the ATC system and DTO. In the cases that led to death, the average number of common targets (NCTs) was 0.879 and the average of average clustering coefficient (ACC) was 0.067. In cases that did not lead to death, the average NCTs was 0.551, and the average of ACC was 0.039.Conclusions: We implemented a system that can find possible explanations and cluster similar ADR cases reported by the FDA. We found that the average of ACC and the average NCTs in cases leading to death are higher than in cases not leading to death, suggesting that the interactions in cases leading to death are generally more complicated than in cases not leading to death. This indicates that our system can help not only in analysing ADRs in terms of drug–drug interactions but also by providing drug target assessments early in the drug discovery process.Analysis of adverse drug reactions using drug and drug target interactions and graph-based methodsShih-Fang Lin, Ke-Ting Xiao, Yu-Ting Huang, Chung-Cheng Chiu, Von-Wun Soo10.1016/j.artmed.2009.11.002Artificial Intelligence in Medicine 48, 2 (2010)2010-02-01Artificial Intelligence in Medicine2010-02-01482-3S0933-3657(10)X0002-1Special Issue Articles161166http://www.aiimjournal.com/article/PIIS0933365710000138/abstract?rss=yesAuthor Index10.1016/S0933-3657(10)00013-8Artificial Intelligence in Medicine 48, 2 (2010)2010-02-01Artificial Intelligence in Medicine2010-02-01482-3S0933-3657(10)X0002-1167168http://www.aiimjournal.com/article/PIIS093336571000014X/abstract?rss=yesSubject Index10.1016/S0933-3657(10)00014-XArtificial Intelligence in Medicine 48, 2 (2010)2010-02-01Artificial Intelligence in Medicine2010-02-01482-3S0933-3657(10)X0002-1169169