What can we contribute to cancer research and treatment from Computer Science or Mathematics? How do we adapt our expertise for them
|
|
- Charles Woods
- 5 years ago
- Views:
Transcription
1 From Bioinformatics to Health Information Technology Outline What can we contribute to cancer research and treatment from Computer Science or Mathematics? How do we adapt our expertise for them Introduction to lung cancer problems Brief review on microarray technology An existing computer algorithm, FCM Adaptation of FCM for biological problems Case and control study Software integration
2 Genomic Factors Associated with Lung Cancer Lung Cancer Lung cancer is the leading cause of death among cancer victims in the United States. It claims more lives than colon, prostate, and breast cancer combined. Smoking is the most significant factor for lung cancer. But, only 10% of ever smokers have lung cancers.
3 Observed and projected lung cancer death rates, United States, The observed death rates are based on data published by the National Center for Health Statistics, Centers for Disease Control. The dotted lines represent straight line projections of the observed slope from in men and from in women. ( National Expenditures for Medical Treatment for the Most Common Cancers Based on Cancer Prevalence in 1998 and Cancer-Specific Costs for , projected to 2004 using the medical care component of the Consumer Price Index. (
4 Low Survival Rate Type 5-Year survival for all stages Early Detection Late Detection Lung 14.9% 48.7% 21% Breast 86.6% 97.0% 23.2% Prostate 97.5% 100% 34.0% Colon 62.3% 90.1% 9.2% Lung Cancer Interesting questions: 1. Are there factors other than smoking attributed to lung cancers? 2. How does second-hand smoking contribute to cancer? Goals: To predict the effectiveness of lung cancer treatments
5 Approach Case control studies Cases are group of lung cancer patients Controls are group of normal people Identify causal factors for lung cancer other than smoking Environmental factors Genetic factors: metabolic pathway genes Interaction between environmental factors and the metabolic pathway genes Biomedical Informatics Research Data Collection Data Mining Software Tool Clinical Information Blood SNPs ing Modeli Lung Tissue Microarray Genetic Information
6 Microarray Technology: Genes Attributed to Cancer Microarray Experiment To understand the roles certain genes play in the progression of cancer, cancer tissue is taken and used in microarray experiment.
7 Gene Expression There are over 10,000 different probes used. Each dot represents the location of a gene probe. Probe GSM10966GSM10966GSM10966GSM10966GSM10966GSM10966GSM10966GSM10966 Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Fuzzy Clustering The algorithm assigns a gene to a given number of clusters Each gene may belong to more than one cluster with different degrees of membership
8 Fuzzy Clustering The method produces a set of cluster centroids and a membership table A. Gasch and M. Eisen, "Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering," Genome Biology, vol. 3, pp. 1-22, Fuzzy C-Means Clustering A set of N samples with their features as X={x 1, x 2 2,, x N } T x i =[x i1, x i2, x ip ] is sample i with its p features A cluster c j =[c j1,c j2, c jp ] The fuzzy membership u ij of sample i to a cluster c j
9 Fuzzy C-Means (FCM) Clustering Randomly initialize membership matrix u ij ( ) ( t ) Repeat until u t u 1 for t=1, Find sets j 1 j C ; d, 0 1N ( t 1) m u ij i cluster centroids Compute N ( t1) u ij i1 m i ; j=1,2, C. I i d i j and I i 1,2,... C Ii Compute membership as t u ij 2 C m1 k1 ( t) d ij ( t) dik 0 i I i 1 i I I i i 1. Adaptation of Fuzzy Clustering for Bioinformatics Problems
10 Kernels and Validity Indexes Different kernnels/distance metrics Distance metrics: Euclidean distance based; Pearson correlation based Choice of fuzziness, m Different validity indexes Crisp: WCSS, FOM, etc. Fuzzy: Xie s, Partition coefficient. Etc. Different Versions of Fuzzy Clustering Methods are categorized according to the objective function and the metrics used in the method Objective Function J Metrics m Data Sets K-means [3] Correlation 2 Yeast J-means [1] Euclidean 1.15 ~ 1.75 Cancer, Blood C-means [2, 5, 6] Euclidean 1.1 ~ 2.54 Serum, Sporulation, Yeast, Cancer, Cell line
11 Adapting the Kernel Initialization: 1. Classify genes into biological processes based on Gene Ontology terms; (0) 2. Use pre-classified genes to initialize j, and the membership u ij ; 3. Normalize membership u C ij, by uij 1 foreachgenei i. j 1 Apply FCM with a squared Pearson correlation distance, 2 d ij 1 X i, C where X i, C is the Pearson correlation between a j gene x i and a cluster c j. Fuzzy WCSS Index
12 Gene Expression References 1. Zhang, M., et al. A Fuzzy C-Means Algorithm Using a Correlation Metrics and Gene Ontology. in The 19th International Conference on Pattern Recognition Tampa, Florida, USA. 2. Zhang, M., W. Zhang, H. Sicotte and P. Yang, 2009, Validating a Correlation- Based Fuzzy C-means Clustering Algorithm, IEEE EMBC, submitted. 24
13 GEO Databases Single Nucleotide Polymorphisms: Genomic Variations in Disease
14 Single Nucleotide Polymorphisms SNPs are single bases at a particular locus where individual people p have differences in their sequences. SNPs are another form of genomic variation in population Population Based Each ethnic group has its own collection of SNPs. Human SNPs classified by major or minor alleles. major alleles are common for all human minor alleles are useful within an ethnic group You should know the average frequency of alleles of the population you are studying!!
15 HapMap HapMap Project The international HapMap consortium has identified >1 million SNPs Samples from four populations 1 SNP every 2 kb of genomic sequence
16 Use SNPs as Markers SNPs are reliable markers Most genes contain at least one SNP Combinations of alleles are associated with particular disease. Study of evolution Understand how a subpopulation adapted to the environment by comparing the differences in their SNPs DNA fingerprinting for criminal or parental verification. Genotype-specific medication HapMap Data and Haploview
17 Haploview Haploview
18 The Goal is to Determine the Best Treatments or to Improve Patient s Quality of Life Prototype Software Architecture EHR database Mayo Clinic... EHR database Clinic X R Prediction Model Model Prediction output Presentation Input Form Current Patient s Data CSS Styles View Model Manager - Model Interface (JRI) Variable Definition -XML Controller View Manager - Web Form Generator - Presentation Generator
19 Prototype Web-based based Tool References 1. Zhang, M., Olson, S., Francioni, J., Gegg-Harrison, T., Meng, N., Sun, Z., and Yang, P., Integrating R Models with Web Technologies. HEALTHINF 2009, Porto, Portugal, January Gegg-Harrison, T., Zhang, M., Meng, N., Sun, Z., and Yang, P., 2009, Porting a Cancer Treatment Prediction to a Mobile Device, IEEE EMBC, submitted. 38
CS2220 Introduction to Computational Biology
CS2220 Introduction to Computational Biology WEEK 8: GENOME-WIDE ASSOCIATION STUDIES (GWAS) 1 Dr. Mengling FENG Institute for Infocomm Research Massachusetts Institute of Technology mfeng@mit.edu PLANS
More informationType II Fuzzy Possibilistic C-Mean Clustering
IFSA-EUSFLAT Type II Fuzzy Possibilistic C-Mean Clustering M.H. Fazel Zarandi, M. Zarinbal, I.B. Turksen, Department of Industrial Engineering, Amirkabir University of Technology, P.O. Box -, Tehran, Iran
More informationPredicting Heart Attack using Fuzzy C Means Clustering Algorithm
Predicting Heart Attack using Fuzzy C Means Clustering Algorithm Dr. G. Rasitha Banu MCA., M.Phil., Ph.D., Assistant Professor,Dept of HIM&HIT,Jazan University, Jazan, Saudi Arabia. J.H.BOUSAL JAMALA MCA.,M.Phil.,
More informationData Mining in Bioinformatics Day 7: Clustering in Bioinformatics
Data Mining in Bioinformatics Day 7: Clustering in Bioinformatics Karsten Borgwardt February 21 to March 4, 2011 Machine Learning & Computational Biology Research Group MPIs Tübingen Karsten Borgwardt:
More informationNew Enhancements: GWAS Workflows with SVS
New Enhancements: GWAS Workflows with SVS August 9 th, 2017 Gabe Rudy VP Product & Engineering 20 most promising Biotech Technology Providers Top 10 Analytics Solution Providers Hype Cycle for Life sciences
More informationUnsupervised MRI Brain Tumor Detection Techniques with Morphological Operations
Unsupervised MRI Brain Tumor Detection Techniques with Morphological Operations Ritu Verma, Sujeet Tiwari, Naazish Rahim Abstract Tumor is a deformity in human body cells which, if not detected and treated,
More informationSNPrints: Defining SNP signatures for prediction of onset in complex diseases
SNPrints: Defining SNP signatures for prediction of onset in complex diseases Linda Liu, Biomedical Informatics, Stanford University Daniel Newburger, Biomedical Informatics, Stanford University Grace
More informationA Network Partition Algorithm for Mining Gene Functional Modules of Colon Cancer from DNA Microarray Data
Method A Network Partition Algorithm for Mining Gene Functional Modules of Colon Cancer from DNA Microarray Data Xiao-Gang Ruan, Jin-Lian Wang*, and Jian-Geng Li Institute of Artificial Intelligence and
More information38 Int'l Conf. Bioinformatics and Computational Biology BIOCOMP'16
38 Int'l Conf. Bioinformatics and Computational Biology BIOCOMP'16 PGAR: ASD Candidate Gene Prioritization System Using Expression Patterns Steven Cogill and Liangjiang Wang Department of Genetics and
More informationWhite Paper Estimating Complex Phenotype Prevalence Using Predictive Models
White Paper 23-12 Estimating Complex Phenotype Prevalence Using Predictive Models Authors: Nicholas A. Furlotte Aaron Kleinman Robin Smith David Hinds Created: September 25 th, 2015 September 25th, 2015
More informationComparative Study of K-means, Gaussian Mixture Model, Fuzzy C-means algorithms for Brain Tumor Segmentation
Comparative Study of K-means, Gaussian Mixture Model, Fuzzy C-means algorithms for Brain Tumor Segmentation U. Baid 1, S. Talbar 2 and S. Talbar 1 1 Department of E&TC Engineering, Shri Guru Gobind Singhji
More informationInformative Gene Selection for Leukemia Cancer Using Weighted K-Means Clustering
IOSR Journal of Pharmacy and Biological Sciences (IOSR-JPBS) e-issn: 2278-3008, p-issn:2319-7676. Volume 9, Issue 4 Ver. V (Jul -Aug. 2014), PP 12-16 Informative Gene Selection for Leukemia Cancer Using
More informationMachine Learning! Robert Stengel! Robotics and Intelligent Systems MAE 345,! Princeton University, 2017
Machine Learning! Robert Stengel! Robotics and Intelligent Systems MAE 345,! Princeton University, 2017 A.K.A. Artificial Intelligence Unsupervised learning! Cluster analysis Patterns, Clumps, and Joining
More informationData analysis in microarray experiment
16 1 004 Chinese Bulletin of Life Sciences Vol. 16, No. 1 Feb., 004 1004-0374 (004) 01-0041-08 100005 Q33 A Data analysis in microarray experiment YANG Chang, FANG Fu-De * (National Laboratory of Medical
More informationGene-microRNA network module analysis for ovarian cancer
Gene-microRNA network module analysis for ovarian cancer Shuqin Zhang School of Mathematical Sciences Fudan University Oct. 4, 2016 Outline Introduction Materials and Methods Results Conclusions Introduction
More informationStatistical Analysis of Single Nucleotide Polymorphism Microarrays in Cancer Studies
Statistical Analysis of Single Nucleotide Polymorphism Microarrays in Cancer Studies Stanford Biostatistics Workshop Pierre Neuvial with Henrik Bengtsson and Terry Speed Department of Statistics, UC Berkeley
More informationT. R. Golub, D. K. Slonim & Others 1999
T. R. Golub, D. K. Slonim & Others 1999 Big Picture in 1999 The Need for Cancer Classification Cancer classification very important for advances in cancer treatment. Cancers of Identical grade can have
More informationAn Efficient Diseases Classifier based on Microarray Datasets using Clustering ANOVA Extreme Learning Machine (CAELM)
www.ijcsi.org 8 An Efficient Diseases Classifier based on Microarray Datasets using Clustering ANOVA Extreme Learning Machine (CAELM) Shamsan Aljamali 1, Zhang Zuping 2 and Long Jun 3 1 School of Information
More informationStructural Variation and Medical Genomics
Structural Variation and Medical Genomics Andrew King Department of Biomedical Informatics July 8, 2014 You already know about small scale genetic mutations Single nucleotide polymorphism (SNPs) Deletions,
More informationChapter 1. Introduction
Chapter 1 Introduction 1.1 Motivation and Goals The increasing availability and decreasing cost of high-throughput (HT) technologies coupled with the availability of computational tools and data form a
More informationNetwork-assisted data analysis
Network-assisted data analysis Bing Zhang Department of Biomedical Informatics Vanderbilt University bing.zhang@vanderbilt.edu Protein identification in shotgun proteomics Protein digestion LC-MS/MS Protein
More informationComparison of Mamdani and Sugeno Fuzzy Interference Systems for the Breast Cancer Risk
Comparison of Mamdani and Sugeno Fuzzy Interference Systems for the Breast Cancer Risk Alshalaa A. Shleeg, Issmail M. Ellabib Abstract Breast cancer is a major health burden worldwide being a major cause
More informationMammogram Analysis: Tumor Classification
Mammogram Analysis: Tumor Classification Term Project Report Geethapriya Raghavan geeragh@mail.utexas.edu EE 381K - Multidimensional Digital Signal Processing Spring 2005 Abstract Breast cancer is the
More informationBrain Tumor segmentation and classification using Fcm and support vector machine
Brain Tumor segmentation and classification using Fcm and support vector machine Gaurav Gupta 1, Vinay singh 2 1 PG student,m.tech Electronics and Communication,Department of Electronics, Galgotia College
More informationA quick review. The clustering problem: Hierarchical clustering algorithm: Many possible distance metrics K-mean clustering algorithm:
The clustering problem: partition genes into distinct sets with high homogeneity and high separation Hierarchical clustering algorithm: 1. Assign each object to a separate cluster. 2. Regroup the pair
More informationHuman Immunodeficiency Virus (HIV) Diagnosis Using Neuro-Fuzzy Expert System
ORIENTAL JOURNAL OF COMPUTER SCIENCE & TECHNOLOGY An International Open Free Access, Peer Reviewed Research Journal Published By: Oriental Scientific Publishing Co., India. www.computerscijournal.org ISSN:
More informationSurvey on Breast Cancer Analysis using Machine Learning Techniques
Survey on Breast Cancer Analysis using Machine Learning Techniques Prof Tejal Upadhyay 1, Arpita Shah 2 1 Assistant Professor, Information Technology Department, 2 M.Tech, Computer Science and Engineering,
More informationAnalysis of Mammograms Using Texture Segmentation
Analysis of Mammograms Using Texture Segmentation Joel Quintanilla-Domínguez 1, Jose Miguel Barrón-Adame 1, Jose Antonio Gordillo-Sosa 1, Jose Merced Lozano-Garcia 2, Hector Estrada-García 2, Rafael Guzmán-Cabrera
More informationInternational Journal of Digital Application & Contemporary research Website: (Volume 1, Issue 1, August 2012) IJDACR.
Segmentation of Brain MRI Images for Tumor extraction by combining C-means clustering and Watershed algorithm with Genetic Algorithm Kailash Sinha 1 1 Department of Electronics & Telecommunication Engineering,
More informationBiomedical Research 2016; Special Issue: S148-S152 ISSN X
Biomedical Research 2016; Special Issue: S148-S152 ISSN 0970-938X www.biomedres.info Prognostic classification tumor cells using an unsupervised model. R Sathya Bama Krishna 1*, M Aramudhan 2 1 Department
More informationSupplementary Figure 1. Principal components analysis of European ancestry in the African American, Native Hawaiian and Latino populations.
Supplementary Figure. Principal components analysis of European ancestry in the African American, Native Hawaiian and Latino populations. a Eigenvector 2.5..5.5. African Americans European Americans e
More informationLocal Fuzzy c-means Clustering for Medical Spectroscopy Images
Applied Mathematical Sciences, Vol. 5, 211, no. 3, 1449-1458 Local Fuzzy c-means Clustering for Medical Spectroscopy Images Andrés Barrea CIEM - Universidad Nacional de Córdoba abarrea@mate.uncor.edu Abstract
More informationFurther Mathematics 2018 CORE: Data analysis Chapter 3 Investigating associations between two variables
Chapter 3: Investigating associations between two variables Further Mathematics 2018 CORE: Data analysis Chapter 3 Investigating associations between two variables Extract from Study Design Key knowledge
More informationIdentification of Tissue Independent Cancer Driver Genes
Identification of Tissue Independent Cancer Driver Genes Alexandros Manolakos, Idoia Ochoa, Kartik Venkat Supervisor: Olivier Gevaert Abstract Identification of genomic patterns in tumors is an important
More informationImplementation of Inference Engine in Adaptive Neuro Fuzzy Inference System to Predict and Control the Sugar Level in Diabetic Patient
, ISSN (Print) : 319-8613 Implementation of Inference Engine in Adaptive Neuro Fuzzy Inference System to Predict and Control the Sugar Level in Diabetic Patient M. Mayilvaganan # 1 R. Deepa * # Associate
More informationIntelligent Patient Profiling for Diagnosis, Staging and Treatment Selection in Colon Cancer
Intelligent Patient Profiling for Diagnosis, Staging and Treatment Selection in Colon Cancer Yorgos Goletsis, Member, IEEE, Themis P. Exarchos, Student member, IEEE, Nikolaos Giannakeas, Student member,
More informationEnhanced Detection of Lung Cancer using Hybrid Method of Image Segmentation
Enhanced Detection of Lung Cancer using Hybrid Method of Image Segmentation L Uma Maheshwari Department of ECE, Stanley College of Engineering and Technology for Women, Hyderabad - 500001, India. Udayini
More informationApplications. DSC 410/510 Multivariate Statistical Methods. Discriminating Two Groups. What is Discriminant Analysis
DSC 4/5 Multivariate Statistical Methods Applications DSC 4/5 Multivariate Statistical Methods Discriminant Analysis Identify the group to which an object or case (e.g. person, firm, product) belongs:
More informationAn SVM-Fuzzy Expert System Design For Diabetes Risk Classification
An SVM-Fuzzy Expert System Design For Diabetes Risk Classification Thirumalaimuthu Thirumalaiappan Ramanathan, Dharmendra Sharma Faculty of Education, Science, Technology and Mathematics University of
More informationIdentification of regions with common copy-number variations using SNP array
Identification of regions with common copy-number variations using SNP array Agus Salim Epidemiology and Public Health National University of Singapore Copy Number Variation (CNV) Copy number alteration
More informationFUZZY DATA MINING FOR HEART DISEASE DIAGNOSIS
FUZZY DATA MINING FOR HEART DISEASE DIAGNOSIS S.Jayasudha Department of Mathematics Prince Shri Venkateswara Padmavathy Engineering College, Chennai. ABSTRACT: We address the problem of having rigid values
More informationA Versatile Algorithm for Finding Patterns in Large Cancer Cell Line Data Sets
A Versatile Algorithm for Finding Patterns in Large Cancer Cell Line Data Sets James Jusuf, Phillips Academy Andover May 21, 2017 MIT PRIMES The Broad Institute of MIT and Harvard Introduction A quest
More informationEfficacy of the Extended Principal Orthogonal Decomposition Method on DNA Microarray Data in Cancer Detection
202 4th International onference on Bioinformatics and Biomedical Technology IPBEE vol.29 (202) (202) IASIT Press, Singapore Efficacy of the Extended Principal Orthogonal Decomposition on DA Microarray
More informationDeveloping a Fuzzy Database System for Heart Disease Diagnosis
Developing a Fuzzy Database System for Heart Disease Diagnosis College of Information Technology Jenan Moosa Hasan Databases are Everywhere! Linguistic Terms V a g u e Hazy Nebulous Unclear Enigmatic Uncertain
More informationThe Application of Image Processing Techniques for Detection and Classification of Cancerous Tissue in Digital Mammograms
The Application of Image Processing Techniques for Detection and Classification of Cancerous Tissue in Digital Mammograms Angayarkanni.N 1, Kumar.D 2 and Arunachalam.G 3 1 Research Scholar Department of
More informationCOMBINING CATEGORICAL AND PRIMITIVES-BASED EMOTION RECOGNITION. University of Southern California (USC), Los Angeles, CA, USA
COMBINING CATEGORICAL AND PRIMITIVES-BASED EMOTION RECOGNITION M. Grimm 1, E. Mower 2, K. Kroschel 1, and S. Narayanan 2 1 Institut für Nachrichtentechnik (INT), Universität Karlsruhe (TH), Karlsruhe,
More informationGene expression analysis for tumor classification using vector quantization
Gene expression analysis for tumor classification using vector quantization Edna Márquez 1 Jesús Savage 1, Ana María Espinosa 2, Jaime Berumen 2, Christian Lemaitre 3 1 IIMAS, Universidad Nacional Autónoma
More informationComparison of discrimination methods for the classification of tumors using gene expression data
Comparison of discrimination methods for the classification of tumors using gene expression data Sandrine Dudoit, Jane Fridlyand 2 and Terry Speed 2,. Mathematical Sciences Research Institute, Berkeley
More informationK MEAN AND FUZZY CLUSTERING ALGORITHM PREDICATED BRAIN TUMOR SEGMENTATION AND AREA ESTIMATION
K MEAN AND FUZZY CLUSTERING ALGORITHM PREDICATED BRAIN TUMOR SEGMENTATION AND AREA ESTIMATION Yashwanti Sahu 1, Suresh Gawande 2 1 M.Tech. Scholar, Electronics & Communication Engineering, BERI Bhopal,
More informationIntroduction to Machine Learning. Katherine Heller Deep Learning Summer School 2018
Introduction to Machine Learning Katherine Heller Deep Learning Summer School 2018 Outline Kinds of machine learning Linear regression Regularization Bayesian methods Logistic Regression Why we do this
More informationCHAPTER - 7 FUZZY LOGIC IN DATA MINING
CHAPTER - 7 FUZZY LOGIC IN DATA MINING 7.1. INTRODUCTION Fuzzy logic is an approach of data mining that involves computing the data based on the probable predictions and clustering as opposed to the traditional
More informationHuman population sub-structure and genetic association studies
Human population sub-structure and genetic association studies Stephanie A. Santorico, Ph.D. Department of Mathematical & Statistical Sciences Stephanie.Santorico@ucdenver.edu Global Similarity Map from
More informationSubLasso:a feature selection and classification R package with a. fixed feature subset
SubLasso:a feature selection and classification R package with a fixed feature subset Youxi Luo,3,*, Qinghan Meng,2,*, Ruiquan Ge,2, Guoqin Mai, Jikui Liu, Fengfeng Zhou,#. Shenzhen Institutes of Advanced
More informationEstimating the Number of Clusters in DNA Microarray Data
Estimating the Number of Clusters in DNA Microarray Data N. Bolshakova 1, F. Azuaje 2 1 Department of Computer Science, Trinity College Dublin, Ireland 2 School of Computing and Mathematics, University
More informationSegmentation of Tumor Region from Brain Mri Images Using Fuzzy C-Means Clustering And Seeded Region Growing
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 18, Issue 5, Ver. I (Sept - Oct. 2016), PP 20-24 www.iosrjournals.org Segmentation of Tumor Region from Brain
More informationECG Beat Recognition using Principal Components Analysis and Artificial Neural Network
International Journal of Electronics Engineering, 3 (1), 2011, pp. 55 58 ECG Beat Recognition using Principal Components Analysis and Artificial Neural Network Amitabh Sharma 1, and Tanushree Sharma 2
More informationGlobal variation in copy number in the human genome
Global variation in copy number in the human genome Redon et. al. Nature 444:444-454 (2006) 12.03.2007 Tarmo Puurand Study 270 individuals (HapMap collection) Affymetrix 500K Whole Genome TilePath (WGTP)
More informationAn Edge-Device for Accurate Seizure Detection in the IoT
An Edge-Device for Accurate Seizure Detection in the IoT M. A. Sayeed 1, S. P. Mohanty 2, E. Kougianos 3, and H. Zaveri 4 University of North Texas, Denton, TX, USA. 1,2,3 Yale University, New Haven, CT,
More informationClass discovery in Gene Expression Data: Characterizing Splits by Support Vector Machines
Class discovery in Gene Expression Data: Characterizing Splits by Support Vector Machines Florian Markowetz and Anja von Heydebreck Max-Planck-Institute for Molecular Genetics Computational Molecular Biology
More informationNearest Shrunken Centroid as Feature Selection of Microarray Data
Nearest Shrunken Centroid as Feature Selection of Microarray Data Myungsook Klassen Computer Science Department, California Lutheran University 60 West Olsen Rd, Thousand Oaks, CA 91360 mklassen@clunet.edu
More informationA prediction model for type 2 diabetes using adaptive neuro-fuzzy interface system.
Biomedical Research 208; Special Issue: S69-S74 ISSN 0970-938X www.biomedres.info A prediction model for type 2 diabetes using adaptive neuro-fuzzy interface system. S Alby *, BL Shivakumar 2 Research
More informationBRAIN TUMOR SEGMENTATION USING K- MEAN CLUSTERIN AND ITS STAGES IDENTIFICATION
ABSTRACT BRAIN TUMOR SEGMENTATION USING K- MEAN CLUSTERIN AND ITS STAGES IDENTIFICATION Sonal Khobarkhede 1, Poonam Kamble 2, Vrushali Jadhav 3 Prof.V.S.Kulkarni 4 1,2,3,4 Rajarshi Shahu College of Engg.
More informationDOES THE BRCAX GENE EXIST? FUTURE OUTLOOK
CHAPTER 6 DOES THE BRCAX GENE EXIST? FUTURE OUTLOOK Genetic research aimed at the identification of new breast cancer susceptibility genes is at an interesting crossroad. On the one hand, the existence
More informationClustering analysis of cancerous microarray data
Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2014, 6(9): 488-493 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 Clustering analysis of cancerous microarray data
More informationIdentification and Classification of Coronary Artery Disease Patients using Neuro-Fuzzy Inference Systems
Journal of mathematics and computer Science 13 (2014) 136-141 Identification and Classification of Coronary Artery Disease Patients using Neuro-Fuzzy Inference Systems Saeed Ayat 1, Asieh Khosravanian
More informationDeveloping Better Medicine
SURF 2013 Marietta L. Harrison, PhD Director, Oncological Sciences Center in Discovery Park Professor, Medicinal Chemistry and Molecular Pharmacology How we do it today One size fits all Medicines aren
More informationNature Methods: doi: /nmeth.3115
Supplementary Figure 1 Analysis of DNA methylation in a cancer cohort based on Infinium 450K data. RnBeads was used to rediscover a clinically distinct subgroup of glioblastoma patients characterized by
More informationFuzzy Decision Analysis in Negotiation between the System of Systems Agent and the System Agent in an Agent-Based Model
Fuzzy Decision Analysis in Negotiation between the System of Systems Agent and the System Agent in an Agent-Based Model Paulette Acheson, Cihan Dagli Engineering Management & Systems Engineering Department
More informationBrain Tumor Detection of MRI Image using Level Set Segmentation and Morphological Operations
Brain Tumor Detection of MRI Image using Level Set Segmentation and Morphological Operations Swati Dubey Lakhwinder Kaur Abstract In medical image investigation, one of the essential problems is segmentation
More informationReleasing SNP Data and GWAS Results with Guaranteed Privacy Protection
integrating Data for Analysis, Anonymization, and SHaring Releasing SNP Data and GWAS Results with Guaranteed Privacy Protection Xiaoqian Jiang, PhD and Shuang Wang, PhD Overview Introduction idash healthcare
More informationIntroduction to Genetics and Genomics
2016 Introduction to enetics and enomics 3. ssociation Studies ggibson.gt@gmail.com http://www.cig.gatech.edu Outline eneral overview of association studies Sample results hree steps to WS: primary scan,
More informationCURRICULUM VITA OF Xiaowen Chen
Xiaowen Chen College of Bioinformatics Science and Technology Harbin Medical University Harbin, 150086, P. R. China Mobile Phone:+86 13263501862 E-mail: hrbmucxw@163.com Education Experience 2008-2011
More informationThe Minuscule and the Massive
The Minuscule and the Massive Our genomes could easily hang on a thumb drive on our necks, muses the Harvard School of Public Health Dean for Academic Affairs, David Hunter, MBBS, MPH, ScD, envisioning
More informationMethylMix An R package for identifying DNA methylation driven genes
MethylMix An R package for identifying DNA methylation driven genes Olivier Gevaert May 3, 2016 Stanford Center for Biomedical Informatics Department of Medicine 1265 Welch Road Stanford CA, 94305-5479
More informationGenetics and Pharmacogenetics in Human Complex Disorders (Example of Bipolar Disorder)
Genetics and Pharmacogenetics in Human Complex Disorders (Example of Bipolar Disorder) September 14, 2012 Chun Xu M.D, M.Sc, Ph.D. Assistant professor Texas Tech University Health Sciences Center Paul
More informationKeywords Missing values, Medoids, Partitioning Around Medoids, Auto Associative Neural Network classifier, Pima Indian Diabetes dataset.
Volume 7, Issue 3, March 2017 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Medoid Based Approach
More informationA Biclustering Based Classification Framework for Cancer Diagnosis and Prognosis
A Biclustering Based Classification Framework for Cancer Diagnosis and Prognosis Baljeet Malhotra and Guohui Lin Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada T6G 2E8
More informationStepwise Knowledge Acquisition in a Fuzzy Knowledge Representation Framework
Stepwise Knowledge Acquisition in a Fuzzy Knowledge Representation Framework Thomas E. Rothenfluh 1, Karl Bögl 2, and Klaus-Peter Adlassnig 2 1 Department of Psychology University of Zurich, Zürichbergstraße
More informationMISSING DATA ESTIMATION FOR CANCER DIAGNOSIS SUPPORT
MISSING DATA ESTIMATION FOR CANCER DIAGNOSIS SUPPORT Witold Jacak (a), Karin Proell (b) (a) Department of Software Engineering Upper Austria University of Applied Sciences Hagenberg, Softwarepark 11, Austria
More informationVisualizing Temporal Patterns by Clustering Patients
Visualizing Temporal Patterns by Clustering Patients Grace Shin, MS 1 ; Samuel McLean, MD 2 ; June Hu, MS 2 ; David Gotz, PhD 1 1 School of Information and Library Science; 2 Department of Anesthesiology
More informationIN SILICO EVALUATION OF DNA-POOLED ALLELOTYPING VERSUS INDIVIDUAL GENOTYPING FOR GENOME-WIDE ASSOCIATION STUDIES OF COMPLEX DISEASE.
IN SILICO EVALUATION OF DNA-POOLED ALLELOTYPING VERSUS INDIVIDUAL GENOTYPING FOR GENOME-WIDE ASSOCIATION STUDIES OF COMPLEX DISEASE By Siddharth Pratap Thesis Submitted to the Faculty of the Graduate School
More informationROUGH SETS APPLICATION FOR STUDENTS CLASSIFICATION BASED ON PERCEPTUAL DATA
ROUGH SETS APPLICATION FOR STUDENTS CLASSIFICATION BASED ON PERCEPTUAL DATA Asheesh Kumar*, Naresh Rameshrao Pimplikar*, Apurva Mohan Gupta* VIT University Vellore -14 India Abstract - Now days, Artificial
More informationA Review on Brain Tumor Detection in Computer Visions
International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 14 (2014), pp. 1459-1466 International Research Publications House http://www. irphouse.com A Review on Brain
More informationMRI Image Processing Operations for Brain Tumor Detection
MRI Image Processing Operations for Brain Tumor Detection Prof. M.M. Bulhe 1, Shubhashini Pathak 2, Karan Parekh 3, Abhishek Jha 4 1Assistant Professor, Dept. of Electronics and Telecommunications Engineering,
More informationCOMPARATIVE STUDY ON FEATURE EXTRACTION METHOD FOR BREAST CANCER CLASSIFICATION
COMPARATIVE STUDY ON FEATURE EXTRACTION METHOD FOR BREAST CANCER CLASSIFICATION 1 R.NITHYA, 2 B.SANTHI 1 Asstt Prof., School of Computing, SASTRA University, Thanjavur, Tamilnadu, India-613402 2 Prof.,
More informationA FUZZY LOGIC BASED CLASSIFICATION TECHNIQUE FOR CLINICAL DATASETS
A FUZZY LOGIC BASED CLASSIFICATION TECHNIQUE FOR CLINICAL DATASETS H. Keerthi, BE-CSE Final year, IFET College of Engineering, Villupuram R. Vimala, Assistant Professor, IFET College of Engineering, Villupuram
More informationImproved Intelligent Classification Technique Based On Support Vector Machines
Improved Intelligent Classification Technique Based On Support Vector Machines V.Vani Asst.Professor,Department of Computer Science,JJ College of Arts and Science,Pudukkottai. Abstract:An abnormal growth
More informationAn Introduction to Quantitative Genetics I. Heather A Lawson Advanced Genetics Spring2018
An Introduction to Quantitative Genetics I Heather A Lawson Advanced Genetics Spring2018 Outline What is Quantitative Genetics? Genotypic Values and Genetic Effects Heritability Linkage Disequilibrium
More informationHybridized KNN and SVM for gene expression data classification
Mei, et al, Hybridized KNN and SVM for gene expression data classification Hybridized KNN and SVM for gene expression data classification Zhen Mei, Qi Shen *, Baoxian Ye Chemistry Department, Zhengzhou
More informationAssociation mapping (qualitative) Association scan, quantitative. Office hours Wednesday 3-4pm 304A Stanley Hall. Association scan, qualitative
Association mapping (qualitative) Office hours Wednesday 3-4pm 304A Stanley Hall Fig. 11.26 Association scan, qualitative Association scan, quantitative osteoarthritis controls χ 2 test C s G s 141 47
More informationReview: Genome assembly Reads
Assembly validation Review: Genome assembly Reads Contigs Scaffolds Chromosome Review: Mate pair data Overlap-Layout-Consensus AMOS project: A Modular Open Source assembler Importing data to an AMOS bank
More informationStatistical Evaluation of Sibling Relationship
The Korean Communications in Statistics Vol. 14 No. 3, 2007, pp. 541 549 Statistical Evaluation of Sibling Relationship Jae Won Lee 1), Hye-Seung Lee 2), Hyo Jung Lee 3) and Juck-Joon Hwang 4) Abstract
More informationMBios 478: Systems Biology and Bayesian Networks, 27 [Dr. Wyrick] Slide #1. Lecture 27: Systems Biology and Bayesian Networks
MBios 478: Systems Biology and Bayesian Networks, 27 [Dr. Wyrick] Slide #1 Lecture 27: Systems Biology and Bayesian Networks Systems Biology and Regulatory Networks o Definitions o Network motifs o Examples
More informationBreast cancer. Risk factors you cannot change include: Treatment Plan Selection. Inferring Transcriptional Module from Breast Cancer Profile Data
Breast cancer Inferring Transcriptional Module from Breast Cancer Profile Data Breast Cancer and Targeted Therapy Microarray Profile Data Inferring Transcriptional Module Methods CSC 177 Data Warehousing
More informationUniversity of Pittsburgh Cancer Institute UPMC CancerCenter. Uma Chandran, MSIS, PhD /21/13
University of Pittsburgh Cancer Institute UPMC CancerCenter Uma Chandran, MSIS, PhD chandran@pitt.edu 412-648-9326 2/21/13 University of Pittsburgh Cancer Institute Founded in 1985 Director Nancy Davidson,
More informationNature Genetics: doi: /ng Supplementary Figure 1. PCA for ancestry in SNV data.
Supplementary Figure 1 PCA for ancestry in SNV data. (a) EIGENSTRAT principal-component analysis (PCA) of SNV genotype data on all samples. (b) PCA of only proband SNV genotype data. (c) PCA of SNV genotype
More informationLung Tumour Detection by Applying Watershed Method
International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 13, Number 5 (2017), pp. 955-964 Research India Publications http://www.ripublication.com Lung Tumour Detection by Applying
More informationDharmesh A Sarvaiya 1, Prof. Mehul Barot 2
Detection of Lung Cancer using Sputum Image Segmentation. Dharmesh A Sarvaiya 1, Prof. Mehul Barot 2 1,2 Department of Computer Engineering, L.D.R.P Institute of Technology & Research, KSV University,
More informationNovel Fuzzy Technique for Cancer Detection in Noisy Breast Ultrasound Images
American Journal of Applied Sciences 9 (5): 779-783, 2012 ISSN 1546-9239 2012 Science Publications Novel Fuzzy Technique for Cancer Detection in Noisy Breast Ultrasound Images 1 Alamelumangai, N. and 2
More informationCOMPASS-TB Report Design Study: First Online Survey
COMPASS-TB Report Design Study: First Online Survey Geoff McKee, Ana Crisan, Jennifer Gardy, Tamara Mu nzner Summary Most respondents (15/17) from UK Majority (10/17) involved in clinical management, 8/17
More information