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2005 Proceedings of the 3rd Asia-Pacific Bioinformatics Conference  
We studied two cancer classification problems with mass spectrometry data and used SVM-RFE to select a small subset of peaks as input variables for the classification.  ...  with mass spectrometry data.  ...  Acknowledgment We thank Michael Wagner at Cincinnati Children's Hospital Medical Center for sharing with us his preprocessed Lung Cancer dataset.  ... 
doi:10.1142/9781860947322_0019 fatcat:vkgwsjvn3vck3j4dqppyj4chwy

Diagnosis of Early Relapse in Ovarian Cancer Using Serum Proteomic Profiling

Jung Hun Oh, Jean Gaol, Animesh Nandi, Prem Gurnani, Lynne Knowles, John Schorge, Kevin P. Rosenblatt
2005 Genome Informatics Series  
Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry data has been increasingly analyzed for identifying biomarkers to help early detection of the disease.  ...  Here, we propose a new algorithm SVM-MB/RFE (SVM-Markov Blanket/Recursive Feature Elimination) based on SVM-RFE, which identifies biomarkers for predicting the early recurrence of ovarian cancer.  ...  Recently, SVM based on recursive feature elimination (SVM-RFE) was proposed for gene selection in cancer classification [6, 8] .  ... 
doi:10.11234/gi1990.16.2_195 fatcat:onqpmfjzhvaqzenrd3ciguyiei

Recursive feature elimination for brain tumor classification using desorption electrospray ionization mass spectrometry imaging

B. Gholami, I. Norton, A. R. Tannenbaum, N. Y. R. Agar
2012 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
Mass spectrometry, a well-known analytical technique used to identify molecules in a given sample based on their mass, can significantly improve the problem of tumor type classification.  ...  Feature selection could result in improvements in classifier performance, discovery of biomarkers, improved data interpretation, and patient treatment.  ...  Peak Matching Next, we discuss a feature extraction framework for mass spectrometry data which is also referred to as peak matching or binning.  ... 
doi:10.1109/embc.2012.6347180 pmid:23367115 pmcid:PMC3649005 fatcat:6ovbgreolrg7fi6cd5kafwepve

Diagnosis of early relapse in ovarian cancer using serum proteomic profiling

Jung Hun Oh, Jean Gao, Animesh Nandi, Prem Gurnani, Lynne Knowles, John Schorge
2005 Genome Informatics Series  
Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry data has been increasingly analyzed for identifying biomarkers to help early detection of the disease.  ...  Here, we propose a new algorithm SVM-MB/RFE (SVM-Markov Blanket/Recursive Feature Elimination) based on SVM-RFE, which identifies biomarkers for predicting the early recurrence of ovarian cancer.  ...  Recently, SVM based on recursive feature elimination (SVM-RFE) was proposed for gene selection in cancer classification [6, 8] .  ... 
pmid:16901102 fatcat:z4sfvbascvaofibn4wz3iuqdj4

Proteomic Cancer Classification with Mass Spectrometry Data

Jagath C Rajapakse, Kai-Bo Duan, Wee Kiang Yeo
2005 American Journal of Pharmacogenomics  
In this section, we will present the usefulness of spectrum graph attempts to represent the different types of ions for SVM-RFE for peak selection for cancer classification with MS each peak in the MS-MS  ...  For instance, we presented the importance of number of selected peaks confined to less than 20. feature selection by demonstrating SVM-RFE method on lung and ovarian cancer data.  ... 
doi:10.2165/00129785-200505050-00001 pmid:16196498 fatcat:srtqlyws5rcybi5hkjkyarojc4

Identification ofN-Glycan Serum Markers Associated with Hepatocellular Carcinoma from Mass Spectrometry Data

Zhiqun Tang, Rency S. Varghese, Slavka Bekesova, Christopher A. Loffredo, Mohamed Abdul Hamid, Zuzana Kyselova, Yehia Mechref, Milos V. Novotny, Radoslav Goldman, Habtom W. Ressom
2010 Journal of Proteome Research  
Specifically, mass spectrometry data were analyzed with a peak selection procedure which incorporates multiple random sampling strategies with recursive feature selection based on support vector machines  ...  This study identified candidate glycan biomarkers associated with hepatocellular carcinoma by mass spectrometry.  ...  Network Associate Membership Grant, and the Prevent Cancer Foundation Grant awarded to H.W.R.  ... 
doi:10.1021/pr900397n pmid:19764807 pmcid:PMC2867345 fatcat:jk7rytb3fjhatgeltl4fnchxma

Clustering Mass Spectral Peaks Increases Recognition Accuracy and Stability of SVM-based Feature Selection

Mikhail Pyatnitskiy, Maria Karpova, Sergei Moshkovskii, Andrey Lisitsa, Alexander Archakov
2010 Journal of Proteomics & Bioinformatics  
We recommend clustering of peaks as a filter dimensionality reduction for further use in mass spectral studies.  ...  Mass spectral profiling of serum or plasma is one of the tools widely used to make experimental diagnostic systems for different cancer types.  ...  Acknowledgements This work was supported by the Program "Proteomics for Medicine and Biotechnology" of Russian Academy of Medical Sciences.  ... 
doi:10.4172/jpb.1000120 fatcat:3nautn3xhbfmxdr3ouhtw2xdgq

Highly-accurate metabolomic detection of early-stage ovarian cancer

David A. Gaul, Roman Mezencev, Tran Q. Long, Christina M. Jones, Benedict B. Benigno, Alexander Gray, Facundo M. Fernández, John F. McDonald
2015 Scientific Reports  
High performance mass spectrometry was employed to interrogate the serum metabolome of early-stage ovarian cancer (OC) patients and age-matched control women.  ...  The resulting spectral features were used to establish a linear support vector machine (SVM) model of sixteen diagnostic metabolites that are able to identify early-stage OC with 100% accuracy in our patient  ...  for this study.  ... 
doi:10.1038/srep16351 pmid:26573008 pmcid:PMC4647115 fatcat:pco5hvscf5ccpiv7wz6ofv6tzu

Common peak approach using mass spectrometry data sets for predicting the effects of anticancer drugs on breast cancer

Masaru Ushijima, Satoshi Miyata, Shinto Eguchi, Masanori Kawakita, Masataka Yoshimoto, Takuji Iwase, Futoshi Akiyama, Goi Sakamoto, Koichi Nagasaki, Yoshio Miki, Tetsuo Noda, Yutaka Hoshikawa (+1 others)
2007 Cancer Informatics  
We propose a method for biomarker discovery from mass spectrometry data, improving the common peak approach developed by Fushiki et al. (BMC Bioinformatics, 7:358, 2006).  ...  We used a SELDI-TOF MS data set from laser microdissected cancer tissues for predicting the treatment effects of neoadjuvant therapy using an anticancer drug on breast cancer patients.  ...  Lutz Edler and Machiko Sugiyama for their helpful comments on this study.  ... 
pmid:19455248 pmcid:PMC2675857 fatcat:w5jnahhphjh7xglrkt56e372iq

Support Vector Based T-Score for Gene Ranking [chapter]

Piyushkumar A. Mundra, Jagath C. Rajapakse
2008 Lecture Notes in Computer Science  
feature selection on three benchmark cancer datasets.  ...  The proposed method uses backward elimination of features, similar to Support Vector Machine Recursive Feature Elimination (SVM-RFE) formulation, but achieves better results than SVM-RFE and tscore based  ...  SVM-RFE is a multivariate gene ranking method which uses SVM classifier for ranking. SVM-RFE has also been applied to peak selection of mass spectrometry data for cancer classification [13] .  ... 
doi:10.1007/978-3-540-88436-1_13 fatcat:wajon7je2nckjhbzd4lwwyvbqe

Intelligence Algorithms for Protein Classification by Mass Spectrometry

Zichuan Fan, Fanchen Kong, Yang Zhou, Yiqing Chen, Yalan Dai
2018 BioMed Research International  
Mass spectrometry (MS) is an important technique in protein research.  ...  Protein MS data growing with data volume becomes complicated and large; improvements in classification methods in terms of classifier selection and combinations of different algorithms and preprocessing  ...  Acknowledgments The authors wish to express their gratitude for Fundamental Research Funds for the Central Universities, China (No. XDJK2016C150), and Southwest University (No.  ... 
doi:10.1155/2018/2862458 fatcat:cbbxhjurunftbmnlxmpfkjhfrm

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

Xuegong Zhang, Xin Lu, Qian Shi, Xiu-qin Xu, Hon-chiu E Leung, Lyndsay N Harris, James D Iglehart, Alexander Miron, Jun S Liu, Wing H Wong
2006 BMC Bioinformatics  
Results: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data.  ...  Feature selection and classification algorithms need to be robust to noise and outliers in the data.  ...  Andrea Richardson for helpful discussion on the breast cancer experiment, and Lih-yin Lim for technical assistance in protein profiling in the rat liver cirrhosis study.  ... 
doi:10.1186/1471-2105-7-197 pmid:16606446 pmcid:PMC1456993 fatcat:wyowuu5v7jco3a2ffkd6lxov2m

Classification algorithms for phenotype prediction in genomics and proteomics

Habtom, W. Ressom
2008 Frontiers in Bioscience  
In particular, the paper focuses on the use of these computational methods for gene and peak selection from microarray and mass spectrometry data, respectively.  ...  This paper gives an overview of statistical and machine learning-based feature selection and pattern classification algorithms and their application in molecular cancer classification or phenotype prediction  ...  (57) , which combined GA with KNN for gene selection and cancer classification using microarray data. Protein mass spectrometry Zhu et al.  ... 
doi:10.2741/2712 pmid:17981580 pmcid:PMC2204040 fatcat:xuolvcm6ajh73lfp4c7wi7osiu

Exploratory study on classification of lung cancer subtypes through a combined K-nearest neighbor classifier in breathomics

Chunyan Wang, Yijing Long, Wenwen Li, Wei Dai, Shaohua Xie, Yuanling Liu, Yinchenxi Zhang, Mingxin Liu, Yonghui Tian, Qiang Li, Yixiang Duan
2020 Scientific Reports  
Exhaled breath analysis provides a tremendous potential approach in non-invasive diagnosis of lung cancer but was rarely reported for lung cancer subtypes classification.  ...  The result revealed that the combined algorithm could improve the classification performance of lung cancer subtypes in breathomics and suggested that combining non-invasive exhaled breath analysis with  ...  These supervised classifiers have been applied to the classification of lung cancer subtypes in breath analysis, for instance, for analyzing sensor array data 17 , and mass spectrometry data due to their  ... 
doi:10.1038/s41598-020-62803-4 pmid:32246031 fatcat:lxb737l7u5cejg4du4o2sx5mui

Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines

Wei Guan, Manshui Zhou, Christina Y Hampton, Benedict B Benigno, L DeEtte Walker, Alexander Gray, John F McDonald, Facundo M Fernández
2009 BMC Bioinformatics  
The performance of support vector machines (SVM) for the classification of liquid chromatography/time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or "panels  ...  Results: In this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated.  ...  Foundation, the Ovarian Cycle Foundation, the Larry and Beth Lawrence Foundation and the Georgia Cancer Coalition to JFM.  ... 
doi:10.1186/1471-2105-10-259 pmid:19698113 pmcid:PMC2741455 fatcat:s5kjag4rpjdmtfytcxbpotluj4
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