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Diagnosis of drug-induced renal tubular toxicity using global gene expression profiles

JOURNAL OF TRANSLATIONAL MEDICINE 2007年第1期5卷 47-47页

作者： Jiang, Ying Gerhold, David L. Holder, Daniel J. Figueroa, David J. Bailey, Wendy J. Guan, Ping Skopek, Thomas R. Sistare, Frank D. Sina, Joseph F. Merck & Co Inc Merck Res Labs Safety Assessment West Point PA 19486 USA Merck & Co Inc Merck Res Labs Biometr Res West Point PA 19486 USA

Toxicogenomics can measure the expression of thousands of genes to identify changes associated with drug induced toxicities. It is expected that toxicogenomics can be an alternative or complementary approach in preclinical drug safety evaluation to identify or predict drug induced toxicities. One of the major concerns in applying toxicogenomics to diagnose or predict drug induced organ toxicity, is how generalizable the statistical classification model is when derived from small datasets? Here we presented that a diagnosis of kidney proximal tubule toxicity, measured by pathology, can successfully be achieved even with a study design of limited number of training studies or samples. We selected a total of ten kidney toxicants, designed the in life study with multiple dose and multiple time points to cover samples at doses and time points with or without concurrent toxicity. We employed SVM (support vector machine) as the classification algorithm for the toxicogenomic diagnosis of kidney proximal tubule toxicity. Instead of applying cross validation methods, we used an independent testing set by dividing the studies or samples into independent training and testing sets to evaluate the diagnostic performance. We achieved a Sn (sensitivity) = 88% and a Sp (specificity) = 91%. The diagnosis performance underscores the potential application of toxicogenomics in a preclinical lead optimization process of drugs entering into development.

关键词： support vector machine support vector machine model Charles River Laboratory Microarray Gene Expression Data support vector machine Algorithm

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Prediction of mucin-type O-glycosylation sites in mammalian proteins using the composition of k-spaced amino acid pairs

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BMC BIOINFORMATICS 2008年第1期9卷 101-101页

作者： Chen, Yong-Zi Tang, Yu-Rong Sheng, Zhi-Ya Zhang, Ziding China Agr Univ Coll Biol Sci Bioinformat Ctr Beijing 100094 Peoples R China Natl Inst Biol Sci Beijing 102206 Peoples R China

Background: As one of the most common protein post-translational modifications, glycosylation is involved in a variety of important biological processes. Computational identification of glycosylation sites in protein sequences becomes increasingly important in the post-genomic era. A new encoding scheme was employed to improve the prediction of mucin-type O-glycosylation sites in mammalian proteins. Results: A new protein bioinformatics tool, CKSAAP_OGlySite, was developed to predict mucin-type O- glycosylation serine/threonine (S/T) sites in mammalian proteins. Using the composition of k-spaced amino acid pairs (CKSAAP) based encoding scheme, the proposed method was trained and tested in a new and stringent O- glycosylation dataset with the assistance of support vector machine (SVM). When the ratio of O- glycosylation to non-glycosylation sites in training datasets was set as 1: 1, 10-fold cross-validation tests showed that the proposed method yielded a high accuracy of 83.1% and 81.4% in predicting O- glycosylated S and T sites, respectively. Based on the same datasets, CKSAAP_OGlySite resulted in a higher accuracy than the conventional binary encoding based method (about +5.0%). When trained and tested in 1: 5 datasets, the CKSAAP encoding showed a more significant improvement than the binary encoding. We also merged the training datasets of S and T sites and integrated the prediction of S and T sites into one single predictor (i. e. S+T predictor). Either in 1: 1 or 1: 5 datasets, the performance of this S+T predictor was always slightly better than those predictors where S and T sites were independently predicted, suggesting that the molecular recognition of O- glycosylated S/T sites seems to be similar and the increase of the S+T predictor's accuracy may be a result of expanded training datasets. Moreover, CKSAAP_OGlySite was also shown to have better performance when benchmarked against two existing predictors. Conclusion: Because of CKSAAP encoding's ability

关键词： support vector machine Encode Scheme support vector machine model Matthew Correlation Coefficient support vector machine Algorithm

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Overview of BioCreative II gene mention recognition

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GENOME BIOLOGY 2008年第sup2期9卷 S2-S2页

作者： Smith, Larry Tanabe, Lorraine K. Johnson Nee Ando, Rie Kuo, Cheng-Ju Chung, I-Fang Hsu, Chun-Nan Lin, Yu-Shi Klinger, Roman Friedrich, Christoph M. Ganchev, Kuzman Torii, Manabu Liu, Hongfang Haddow, Barry Struble, Craig A. Povinelli, Richard J. Vlachos, Andreas Baumgartner, William A., Jr. Hunter, Lawrence Carpenter, Bob Tsai, Richard Tzong-Han Dai, Hong-Jie Liu, Feng Chen, Yifei Sun, Chengjie Katrenko, Sophia Adriaans, Pieter Blaschke, Christian Torres, Rafael Neves, Mariana Nakov, Preslav Divoli, Anna Mana-Lopez, Manuel Mata, Jacinto Wilbur, W. John Natl Lib Med Natl Ctr Biotechnol Informat Bethesda MD 20894 USA IBM TJ Watson Res Ctr Yorktown Hts NY USA Natl Yang Ming Univ Inst Bioinformat Taipei 112 Taiwan Acad Sinica Inst Informat Sci Taipei Taiwan Fraunhofer Inst Algorithms & Sci Comp Dept Bioinformat Schloss Birlinghoven Sankt Augustin Germany Univ Penn Dept Comp & Informat Sci Philadelphia PA 19104 USA Georgetown Univ Med Ctr Dept Biostat Bioinformat & Biomath Washington DC 20007 USA Univ Edinburgh Sch Informat Edinburgh EH8 9YL Midlothian Scotland Marquette Univ Dept Math Stat & Comp Sci Milwaukee WI 53233 USA Marquette Univ Dept Elect & Comp Engn Milwaukee WI 53233 USA Univ Cambridge Comp Lab Cambridge CB2 3QG England Univ Colorado Sch Med Ctr Computat Pharmacol Denver CO USA Alias I Inc Brooklyn NY USA Yuan Ze Univ Dept Comp Sci & Engn Tao Yuan Taiwan Natl Tsing Hua Univ Dept Comp Sci Hsinchu 30043 Taiwan Vrije Univ Brussels Computat Modeling Lab Brussels Belgium Harbin Inst Technol Sch Comp Sci & Technol Harbin 150006 Peoples R China Univ Amsterdam Inst Informat Human Comp Studies Lab Amsterdam Netherlands Bioalma Madrid Spain Univ Complutense Madrid Fac Informat Madrid Spain Univ Calif Berkeley Dept Elect Engn & Comp Sci Div Comp Sci Berkeley CA 94720 USA Bulgarian Acad Sci Inst Parallel Proc Linguist Modeling Dept Sofia Bulgaria Univ Calif Berkeley Sch Informat Berkeley CA 94720 USA Univ Huelva Dept Tecnol Informac Huelva Spain

Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions. A variety of different methods were used and the results varied with a highest achieved F-1 score of 0.8721. Here we present brief descriptions of all the methods used and a statistical analysis of the results. We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions.

关键词： support vector machine model Unify Medical Language System Name Entity Recognition Feature Conjunction Gene Mention

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support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2008年第1期8卷 56-56页

作者： Verplancke, T. Van Looy, S. Benoit, D. Vansteelandt, S. Depuydt, P. De Turck, F. Decruyenaere, J. Univ Ghent Fac Med Dept Intens Care Med State Univ Ghent Hosp B-9000 Ghent Belgium Univ Ghent Fac Engn Dept Informat Technol B-9000 Ghent Belgium Univ Ghent Fac Sci Dept Appl Math & Comp Sci B-9000 Ghent Belgium

Background: Several models for mortality prediction have been constructed for critically ill patients with haematological malignancies in recent years. These models have proven to be equally or more accurate in predicting hospital mortality in patients with haematological malignancies than ICU severity of illness scores such as the APACHE II or SAPS II [1]. The objective of this study is to compare the accuracy of predicting hospital mortality in patients with haematological malignancies admitted to the ICU between models based on multiple logistic regression (MLR) and support vector machine (SVM) based models. Methods: 352 patients with haematological malignancies admitted to the ICU between 1997 and 2006 for a life-threatening complication were included. 252 patient records were used for training of the models and 100 were used for validation. In a first model 12 input variables were included for comparison between MLR and SVM. In a second more complex model 17 input variables were used. MLR and SVM analysis were performed independently from each other. Discrimination was evaluated using the area under the receiver operating characteristic (ROC) curves (+/- SE). Results: The area under ROC curve for the MLR and SVM in the validation data set were 0.768 (+/- 0.04) vs. 0.802 (+/- 0.04) in the first model (p = 0.19) and 0.781 (+/- 0.05) vs. 0.808 (+/- 0.04) in the second more complex model (p = 0.44). SVM needed only 4 variables to make its prediction in both models, whereas MLR needed 7 and 8 variables in the first and second model respectively. Conclusion: The discriminative power of both the MLR and SVM models was good. No statistically significant differences were found in discriminative power between MLR and SVM for prediction of hospital mortality in critically ill patients with haematological malignancies.

关键词： support vector machine Hospital Mortality support vector machine model Multiple Logistic Regression model Risk Prediction model

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PNImodeler: web server for inferring protein-binding nucleotides from sequence data

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BMC GENOMICS 2015年第3-Sup期16卷 S6-S6页

作者： Im, Jinyong Tuvshinjargal, Narankhuu Park, Byungkyu Lee, Wook Huang, De-Shuang Han, Kyungsook Inha Univ Dept Comp Sci & Engn Inchon South Korea Tongji Univ Coll Elect & Informat Engn Machine Learning & Syst Biol Lab Shanghai 201804 Peoples R China

Background: Interactions between DNA and proteins are essential to many biological processes such as transcriptional regulation and DNA replication. With the increased availability of structures of protein-DNA complexes, several computational studies have been conducted to predict DNA binding sites in proteins. However, little attempt has been made to predict protein binding sites in DNA. Results: From an extensive analysis of protein-DNA complexes, we identified powerful features of DNA and protein sequences which can be used in predicting protein binding sites in DNA sequences. We developed two support vector machine (SVM) models that predict protein binding nucleotides from DNA and/or protein sequences. One SVM model that used DNA sequence data alone achieved a sensitivity of 73.4%, a specificity of 64.8%, an accuracy of 68.9% and a correlation coefficient of 0.382 with a test dataset that was not used in training. Another SVM model that used both DNA and protein sequences achieved a sensitivity of 67.6%, a specificity of 74.3%, an accuracy of 71.4% and a correlation coefficient of 0.418. Conclusions: Predicting binding sites in double-stranded DNAs is a more difficult task than predicting binding sites in single-stranded molecules. Our study showed that protein binding sites in double-stranded DNA molecules can be predicted with a comparable accuracy as those in single-stranded molecules. Our study also demonstrated that using both DNA and protein sequences resulted in a better prediction performance than using DNA sequence data alone. The SVM models and datasets constructed in this study are available at http://***/pnimodeler.

关键词： support vector machine Positive Predictive Value Negative Predictive Value support vector machine model Matthews Correlation Coefficient

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Using machine learning algorithms to guide rehabilitation planning for home care clients

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2007年第1期7卷 41-41页

作者： Zhu, Mu Zhang, Zhanyang Hirdes, John P. Stolee, Paul Univ Waterloo Dept Stat & Actuarial Sci Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Hlth Studies & Gerontol Waterloo ON N2L 3G1 Canada Homewood Hlth Ctr Homewood Res Inst Guelph ON Canada Univ Waterloo Sch Optometry Waterloo ON N2L 3G1 Canada Univ Waterloo Res Inst Aging Waterloo ON N2L 3G1 Canada

Background: Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms - support vector machine (SVM) and K-Nearest Neighbors (KNN) - to guide rehabilitation planning for home care clients. Methods: This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP. Results: The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP. Conclusion: machine learning algorithms achieved superior predictions than the current protocol. machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.

关键词： support vector machine machine Learning Algorithm support vector machine model Original Scale support vector machine Algorithm

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GPMiner: an integrated system for mining combinatorial cis-regulatory elements in mammalian gene group

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BMC GENOMICS 2012年第Sup1期13卷 S3-S3页

作者： Lee, Tzong-Yi Chang, Wen-Chi Hsu, Justin Bo-Kai Chang, Tzu-Hao Shien, Dray-Ming Yuan Ze Univ Dept Comp Sci & Engn Tao Yuan 320 Taiwan Natl Cheng Kung Univ Inst Trop Plant Sci Tainan 701 Taiwan Natl Chiao Tung Univ Inst Bioinformat & Syst Biol Hsinchu 300 Taiwan Asia Pacific Inst Creat Dept Multimedia & Game Sci Miaoli 351 Taiwan

Background: Sequence features in promoter regions are involved in regulating gene transcription initiation. Although numerous computational methods have been developed for predicting transcriptional start sites (TSSs) or transcription factor (TF) binding sites (TFBSs), they lack annotations for do not consider some important regulatory features such as CpG islands, tandem repeats, the TATA box, CCAAT box, GC box, over-represented oligonucleotides, DNA stability, and GC content. Additionally, the combinatorial interaction of TFs regulates the gene group that is associated with same expression pattern. To investigate gene transcriptional regulation, an integrated system that annotates regulatory features in a promoter sequence and detects co-regulation of TFs in a group of genes is needed. Results: This work identifies TSSs and regulatory features in a promoter sequence, and recognizes co-occurrence of cis-regulatory elements in co-expressed genes using a novel system. Three well-known TSS prediction tools are incorporated with orthologous conserved features, such as CpG islands, nucleotide composition, over-represented hexamer nucleotides, and DNA stability, to construct the novel Gene Promoter Miner (GPMiner) using a support vector machine (SVM). According to five-fold cross-validation results, the predictive sensitivity and specificity are both roughly 80%. The proposed system allows users to input a group of gene names/symbols, enabling the co-occurrence of TFBSs to be determined. Additionally, an input sequence can also be analyzed for homogeneity of experimental mammalian promoter sequences, and conserved regulatory features between homologous promoters can be observed through cross-species analysis. After identifying promoter regions, regulatory features are visualized graphically to facilitate gene promoter observations. Conclusions: The GPMiner, which has a user-friendly input/output interface, has numerous benefits in analyzing human and mouse promoters. The

关键词： Regulatory Feature support vector machine model Transcriptional Start Site Proximal Promoter Region Gene Transcriptional Regulation

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In silico prediction of the granzyme B degradome

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BMC GENOMICS 2011年第Sup3期12卷 S11-S11页

作者： Wee, Lawrence J. K. Er, Esmond P. S. Ng, Lisa F. P. Tong, J. C. Inst Infocomm Res Data Min Dept Singapore 138632 Singapore Singapore Immunol Network Singapore 138648 Singapore Natl Univ Singapore Yong Loo Lin Sch Med Dept Biochem Singapore 117597 Singapore

Background: Granzyme B is a serine protease which cleaves at unique tetrapeptide sequences. It is involved in several signaling cross-talks with caspases and functions as a pivotal mediator in a broad range of cellular processes such as apoptosis and inflammation. The granzyme B degradome constitutes proteins from a myriad of functional classes with many more expected to be discovered. However, the experimental discovery and validation of bona fide granzyme B substrates require time consuming and laborious efforts. As such, computational methods for the prediction of substrates would be immensely helpful. Results: We have compiled a dataset of 580 experimentally verified granzyme B cleavage sites and found distinctive patterns of residue conservation and position-specific residue propensities which could be useful for in silico prediction using machine learning algorithms. We trained a series of support vector machines (SVM) classifiers employing Bayes Feature Extraction to predict cleavage sites using sequence windows of diverse lengths and compositions. The SVM classifiers achieved accuracy and A(ROC) scores between 71.00% to 86.50% and 0.78 to 0.94 respectively on independent test sets. We have applied our prediction method on the Chikungunya viral proteome and identified several regulatory domains of viral proteins to be potential sites of granzyme B cleavage, suggesting direct antiviral activity of granzyme B during host-viral innate immune responses. Conclusions: We have compiled a comprehensive dataset of granzyme B cleavage sites and developed an accurate SVM-based prediction method utilizing Bayes Feature Extraction to identify novel substrates of granzyme B in silico. The prediction server is available online, together with reference datasets and supplementary materials.

关键词： support vector machine Cleavage Site support vector machine Classifier support vector machine model support vector machine Method

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Analysis of power system load forecasting based on neural networks

Analysis of power system load forecasting based on neural ne...

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作者： Xuhan Lang Shien-Ming Wu School of Intelligent Engineering South China University of Technology

In this research,our main goal was to improve power load forecasting accuracy by considering the impact of meteorological factors on the total power of the electrical system,examining existing load data,local weather,wind direction,and other parameters affecting total power *** divided the data from the past three years into a training dataset,comprising 75% of the data,and a testing dataset with the remaining 25%.We employed a basic machine learning technique(support vector machine) and three distinct neural network approaches(Artificial Neural Network,Convolutional Neural Network,and Long-Short Term Memory Network) to develop analytical *** experimentation,the LSTM model achieved a loss value of 0.0034 and required 1426.78 seconds of training time across *** the time expense and model complexity,we chose the LSTM model to forecast power load at 15-minute intervals for the subsequent ten days,achieving a satisfactory prediction and fitting *** results suggest that the LSTM model is a promising method for optimizing performance and reliability in electrical power systems.

关键词： power system load prediction support vector machine model Artificial Neural Network model convolutional neural network model Long-Short Term Memory model

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Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM

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BMC bioinformatics 2014年第1期15卷 340页

作者： Liqi Li Sanjiu Yu Weidong Xiao Yongsheng Li Lan Huang Xiaoqi Zheng Shiwen Zhou Hua Yang Department of General Surgery Xinqiao Hospital Third Military Medical University Chongqing 400037 China. xqzheng@***.

BACKGROUND:Identification of the recombination hot/cold spots is critical for understanding the mechanism of recombination as well as the genome evolution process. However, experimental identification of recombination spots is both time-consuming and costly. Developing an accurate and automated method for reliably and quickly identifying recombination spots is thus urgently needed. RESULTS:Here we proposed a novel approach by fusing features from pseudo nucleic acid composition (PseNAC), including NAC, n-tier NAC and pseudo dinucleotide composition (PseDNC). A recursive feature extraction by linear kernel support vector machine (SVM) was then used to rank the integrated feature vectors and extract optimal features. SVM was adopted for identifying recombination spots based on these optimal features. To evaluate the performance of the proposed method, jackknife cross-validation test was employed on a benchmark dataset. The overall accuracy of this approach was 84.09%, which was higher (from 0.37% to 3.79%) than those of state-of-the-art tools. CONCLUSIONS:Comparison results suggested that linear kernel SVM is a useful vehicle for identifying recombination hot/cold spots.

关键词： support vector machine Feature Selection support vector machine model Benchmark Dataset Ranking List

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