Russell Hanson DFCI April 24, 2009
|
|
|
- Caroline Warren
- 5 years ago
- Views:
Transcription
1 DFCI Boston: Using the Weighted Histogram Analysis Method (WHAM) in cancer biology and the Yeast Protein Databank (YPD); Latent Dirichlet Analysis (LDA) for biological sequences and structures Russell Hanson DFCI April 24, 2009
2 Outline Part I Uri Alon s cell fate modeling Weighted histogram analysis method Part II Gene function in cell Weighted histogram analysis method Part III Corpora clustering Latent Dirichlet Analysis for topical clustering DFCI
3 Uri Alon s talk (p1) Question: Why do some cells live and some die? Procedure: Add chemotherapy drugs; monitor cell fate DFCI
4 Uri Alon s talk (p2) Superposition idea: P 1,2 (t) = a*p 1 (t)+b*p 2 (t) s.t. a+b=1. N.B. non-linearities; non-linear responses; non-linear systems; 5 coupled differential equations in biochemical processes; simulation methods in biochemical pathways (Plectix); system doesn t saturate to 1 upon additional drug addition DFCI
5 Some ideas from statistical mechanics - WHAM Superposition idea: P 1,2 (t) = a*p 1 (t)+b*p 2 (t) s.t. a+b=1. DFCI
6 Part II Yeast functional classes DFCI
7 YPD data Yeast Protein Data Bank has the following data: DFCI
8 Matrix representation for YPD classifications DFCI
9 Part III Corpora and thematic clustering Shift gears slightly: making recommendations for news websites, etc. will return shortly Want to have more fine-grained recommendations than connectivity in user network -- weight in a given thematic cluster. DFCI
10 Attributized Bayesian Choice Modeling Attributized content items, i, are stored as vectors in the choice-set database such that: Summary of tastes, T: Collaborative Filtering for text and news : Cold Start Problem (it isn t collaborative until it s collaborative) Past Experience: Some people want the most popular ( Dodgers make offer to Manny Ramirez - Boston.com ); some don t ( Non-Abelian Anyons and Topological Quantum Computation ) By weight in whole network; by weight in user s network; by weight in thematic cluster DFCI
11 Methods in corpora clustering (p1) DFCI
12 Methods in corpora clustering (p2) DFCI
13 Latent Dirichlet Allocation/Analysis (p1) DFCI
14 Latent Dirichlet Alloca/on/Analysis (p2) DFCI
15 Latent Dirichlet Allocation/Analysis (p3) DFCI
16 Latent Dirichlet Allocation/Analysis for proteins/genes/cancer targets/clinical data Just as in previous slide, topics were clustered around Arts, Budgets, Children, and Education. - Cluster around patient type - Cluster around drug type - Cluster around drug-interaction type - Cluster around phenotype/genotype - Cluster around structure/sequence types DFCI
17 Selected references: A. A. Cohen, et al. Response to a Drug Dynamic Proteomics of Individual Cancer Cells in DOI: /Science , 1511 (2008); 322 Ferrenberg AM, Swendsen RH. Optimized Monte-Carlo data-analysis Physical Review Letters volume: 63 issue: 12 pages: Sep Document Clustering in Large German Corpora Using Natural Language Processing, Richard Forster (2006), University of Zurich Latent Dirichlet Allocation, Blei, Ng, and Jordan, Journal of Machine Learning Research 3 (2003) LobeLink.com A. Ben-Hur and W.S. Noble "Kernel methods for predicting protein-protein interactions." Bioinformatics (Proceedings of the Intelligent Systems for Molecular Biology Conference). 21:38-46, D.-S. Huang, K. Li, and G.W. Irwin (Eds.): ICIC 2006, LNBI 4115, pp , Prediction of Protein Complexes Based on Protein Interaction Data and Functional Annotation Data Using Kernel Methods. Springer-Verlag Berlin Heidelberg B. Schwikowski, P. Uetz, S. Fields. A network of protein-protein in yeast. Nature Biotechnology 18 (12): Dec interactions K. Tsuda, H.J. Shin, B. Schölkopf. Fast protein classification with multiple networks. Bioinformatics (Suppl 2):ii59-ii65. J.-P. Vert, K. Tsuda and B. Schölkopf, A primer on kernel methods, in Kernel Methods in Computational Biology, B. Schölkopf, K. Tsuda and J.-P. Vert (Eds.), MIT Press, p.35-70, DFCI
Text mining and natural language analysis. Jefrey Lijffijt
Text mining and natural language analysis Jefrey Lijffijt PART I: Introduction to Text Mining Why text mining The amount of text published on paper, on the web, and even within companies is inconceivably
Topic Models and Applications to Short Documents
Topic Models and Applications to Short Documents Dieu-Thu Le Email: dieuthu.le@unitn.it Trento University April 6, 2011 1 / 43 Outline Introduction Latent Dirichlet Allocation Gibbs Sampling Short Text
Collaborative Topic Modeling for Recommending Scientific Articles
Collaborative Topic Modeling for Recommending Scientific Articles Chong Wang and David M. Blei Best student paper award at KDD 2011 Computer Science Department, Princeton University Presented by Tian Cao
Dimension Reduction (PCA, ICA, CCA, FLD,
Dimension Reduction (PCA, ICA, CCA, FLD, Topic Models) Yi Zhang 10-701, Machine Learning, Spring 2011 April 6 th, 2011 Parts of the PCA slides are from previous 10-701 lectures 1 Outline Dimension reduction
Collaborative topic models: motivations cont
Collaborative topic models: motivations cont Two topics: machine learning social network analysis Two people: " boy Two articles: article A! girl article B Preferences: The boy likes A and B --- no problem.
Recent Advances in Bayesian Inference Techniques
Recent Advances in Bayesian Inference Techniques Christopher M. Bishop Microsoft Research, Cambridge, U.K. research.microsoft.com/~cmbishop SIAM Conference on Data Mining, April 2004 Abstract Bayesian
Bioinformatics. Dept. of Computational Biology & Bioinformatics
Bioinformatics Dept. of Computational Biology & Bioinformatics 3 Bioinformatics - play with sequences & structures Dept. of Computational Biology & Bioinformatics 4 ORGANIZATION OF LIFE ROLE OF BIOINFORMATICS
Topic Modelling and Latent Dirichlet Allocation
Topic Modelling and Latent Dirichlet Allocation Stephen Clark (with thanks to Mark Gales for some of the slides) Lent 2013 Machine Learning for Language Processing: Lecture 7 MPhil in Advanced Computer
Topic Modeling: Beyond Bag-of-Words
University of Cambridge hmw26@cam.ac.uk June 26, 2006 Generative Probabilistic Models of Text Used in text compression, predictive text entry, information retrieval Estimate probability of a word in a
Topic Modeling Using Latent Dirichlet Allocation (LDA)
Topic Modeling Using Latent Dirichlet Allocation (LDA) Porter Jenkins and Mimi Brinberg Penn State University prj3@psu.edu mjb6504@psu.edu October 23, 2017 Porter Jenkins and Mimi Brinberg (PSU) LDA October
Latent Dirichlet Allocation (LDA)
Latent Dirichlet Allocation (LDA) D. Blei, A. Ng, and M. Jordan. Journal of Machine Learning Research, 3:993-1022, January 2003. Following slides borrowed ant then heavily modified from: Jonathan Huang
Latent Dirichlet Allocation (LDA)
Latent Dirichlet Allocation (LDA) A review of topic modeling and customer interactions application 3/11/2015 1 Agenda Agenda Items 1 What is topic modeling? Intro Text Mining & Pre-Processing Natural Language
Latent Dirichlet Allocation
Outlines Advanced Artificial Intelligence October 1, 2009 Outlines Part I: Theoretical Background Part II: Application and Results 1 Motive Previous Research Exchangeability 2 Notation and Terminology
HYPERGRAPH BASED SEMI-SUPERVISED LEARNING ALGORITHMS APPLIED TO SPEECH RECOGNITION PROBLEM: A NOVEL APPROACH
HYPERGRAPH BASED SEMI-SUPERVISED LEARNING ALGORITHMS APPLIED TO SPEECH RECOGNITION PROBLEM: A NOVEL APPROACH Hoang Trang 1, Tran Hoang Loc 1 1 Ho Chi Minh City University of Technology-VNU HCM, Ho Chi
Latent Dirichlet Allocation Introduction/Overview
Latent Dirichlet Allocation Introduction/Overview David Meyer 03.10.2016 David Meyer http://www.1-4-5.net/~dmm/ml/lda_intro.pdf 03.10.2016 Agenda What is Topic Modeling? Parametric vs. Non-Parametric Models
Generative Clustering, Topic Modeling, & Bayesian Inference
Generative Clustering, Topic Modeling, & Bayesian Inference INFO-4604, Applied Machine Learning University of Colorado Boulder December 12-14, 2017 Prof. Michael Paul Unsupervised Naïve Bayes Last week
* Matrix Factorization and Recommendation Systems
Matrix Factorization and Recommendation Systems Originally presented at HLF Workshop on Matrix Factorization with Loren Anderson (University of Minnesota Twin Cities) on 25 th September, 2017 15 th March,
IPSJ SIG Technical Report Vol.2014-MPS-100 No /9/25 1,a) 1 1 SNS / / / / / / Time Series Topic Model Considering Dependence to Multiple Topics S
1,a) 1 1 SNS /// / // Time Series Topic Model Considering Dependence to Multiple Topics Sasaki Kentaro 1,a) Yoshikawa Tomohiro 1 Furuhashi Takeshi 1 Abstract: This pater proposes a topic model that considers
Distributed ML for DOSNs: giving power back to users
Distributed ML for DOSNs: giving power back to users Amira Soliman KTH isocial Marie Curie Initial Training Networks Part1 Agenda DOSNs and Machine Learning DIVa: Decentralized Identity Validation for
Bayesian Data Fusion with Gaussian Process Priors : An Application to Protein Fold Recognition
Bayesian Data Fusion with Gaussian Process Priors : An Application to Protein Fold Recognition Mar Girolami 1 Department of Computing Science University of Glasgow girolami@dcs.gla.ac.u 1 Introduction
Computational methods for predicting protein-protein interactions
Computational methods for predicting protein-protein interactions Tomi Peltola T-61.6070 Special course in bioinformatics I 3.4.2008 Outline Biological background Protein-protein interactions Computational
Introduction to Bioinformatics. Shifra Ben-Dor Irit Orr
Introduction to Bioinformatics Shifra Ben-Dor Irit Orr Lecture Outline: Technical Course Items Introduction to Bioinformatics Introduction to Databases This week and next week What is bioinformatics? A
Bayesian Nonparametrics for Speech and Signal Processing
Bayesian Nonparametrics for Speech and Signal Processing Michael I. Jordan University of California, Berkeley June 28, 2011 Acknowledgments: Emily Fox, Erik Sudderth, Yee Whye Teh, and Romain Thibaux Computer
Motif Extraction and Protein Classification
Motif Extraction and Protein Classification Vered Kunik 1 Zach Solan 2 Shimon Edelman 3 Eytan Ruppin 1 David Horn 2 1 School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel {kunikver,ruppin}@tau.ac.il
CISC 636 Computational Biology & Bioinformatics (Fall 2016)
CISC 636 Computational Biology & Bioinformatics (Fall 2016) Predicting Protein-Protein Interactions CISC636, F16, Lec22, Liao 1 Background Proteins do not function as isolated entities. Protein-Protein
Dept. of Physics 2018 Summer Internship Opportunities For undergraduate students
Learning from the smallest the dynamical behaviour at the nanoscale Dr Francesca Baletto Type of project: Computational Type of Activity: Writing and using a piece of code in Python The student should
MOLECULAR MODELING IN BIOLOGY (BIO 3356) SYLLABUS
New York City College of Technology School of Arts and Sciences Department of Biological Sciences MOLECULAR MODELING IN BIOLOGY (BIO 3356) SYLLABUS Course Information Course Title: Molecular Modeling in
Intelligent Systems I
Intelligent Systems I 00 INTRODUCTION Stefan Harmeling & Philipp Hennig 24. October 2013 Max Planck Institute for Intelligent Systems Dptmt. of Empirical Inference Which Card? Opening Experiment Which
Department of Computer Science, Guiyang University, Guiyang , GuiZhou, China
doi:10.21311/002.31.12.01 A Hybrid Recommendation Algorithm with LDA and SVD++ Considering the News Timeliness Junsong Luo 1*, Can Jiang 2, Peng Tian 2 and Wei Huang 2, 3 1 College of Information Science
Information retrieval LSI, plsi and LDA. Jian-Yun Nie
Information retrieval LSI, plsi and LDA Jian-Yun Nie Basics: Eigenvector, Eigenvalue Ref: http://en.wikipedia.org/wiki/eigenvector For a square matrix A: Ax = λx where x is a vector (eigenvector), and
A Continuous-Time Model of Topic Co-occurrence Trends
A Continuous-Time Model of Topic Co-occurrence Trends Wei Li, Xuerui Wang and Andrew McCallum Department of Computer Science University of Massachusetts 140 Governors Drive Amherst, MA 01003-9264 Abstract
Bayesian Networks BY: MOHAMAD ALSABBAGH
Bayesian Networks BY: MOHAMAD ALSABBAGH Outlines Introduction Bayes Rule Bayesian Networks (BN) Representation Size of a Bayesian Network Inference via BN BN Learning Dynamic BN Introduction Conditional
EVALUATING SYMMETRIC INFORMATION GAP BETWEEN DYNAMICAL SYSTEMS USING PARTICLE FILTER
EVALUATING SYMMETRIC INFORMATION GAP BETWEEN DYNAMICAL SYSTEMS USING PARTICLE FILTER Zhen Zhen 1, Jun Young Lee 2, and Abdus Saboor 3 1 Mingde College, Guizhou University, China zhenz2000@21cn.com 2 Department
Networks & pathways. Hedi Peterson MTAT Bioinformatics
Networks & pathways Hedi Peterson (peterson@quretec.com) MTAT.03.239 Bioinformatics 03.11.2010 Networks are graphs Nodes Edges Edges Directed, undirected, weighted Nodes Genes Proteins Metabolites Enzymes
16 : Approximate Inference: Markov Chain Monte Carlo
10-708: Probabilistic Graphical Models 10-708, Spring 2017 16 : Approximate Inference: Markov Chain Monte Carlo Lecturer: Eric P. Xing Scribes: Yuan Yang, Chao-Ming Yen 1 Introduction As the target distribution
Term Filtering with Bounded Error
Term Filtering with Bounded Error Zi Yang, Wei Li, Jie Tang, and Juanzi Li Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University, China {yangzi, tangjie, ljz}@keg.cs.tsinghua.edu.cn
Topic Models. Advanced Machine Learning for NLP Jordan Boyd-Graber OVERVIEW. Advanced Machine Learning for NLP Boyd-Graber Topic Models 1 of 1
Topic Models Advanced Machine Learning for NLP Jordan Boyd-Graber OVERVIEW Advanced Machine Learning for NLP Boyd-Graber Topic Models 1 of 1 Low-Dimensional Space for Documents Last time: embedding space
Machine Learning 2007: Slides 1. Instructor: Tim van Erven Website: erven/teaching/0708/ml/
Machine 2007: Slides 1 Instructor: Tim van Erven (Tim.van.Erven@cwi.nl) Website: www.cwi.nl/ erven/teaching/0708/ml/ September 6, 2007, updated: September 13, 2007 1 / 37 Overview The Most Important Supervised
On the use of Long-Short Term Memory neural networks for time series prediction
On the use of Long-Short Term Memory neural networks for time series prediction Pilar Gómez-Gil National Institute of Astrophysics, Optics and Electronics ccc.inaoep.mx/~pgomez In collaboration with: J.
Understanding Comments Submitted to FCC on Net Neutrality. Kevin (Junhui) Mao, Jing Xia, Dennis (Woncheol) Jeong December 12, 2014
Understanding Comments Submitted to FCC on Net Neutrality Kevin (Junhui) Mao, Jing Xia, Dennis (Woncheol) Jeong December 12, 2014 Abstract We aim to understand and summarize themes in the 1.65 million
hsnim: Hyper Scalable Network Inference Machine for Scale-Free Protein-Protein Interaction Networks Inference
CS 229 Project Report (TR# MSB2010) Submitted 12/10/2010 hsnim: Hyper Scalable Network Inference Machine for Scale-Free Protein-Protein Interaction Networks Inference Muhammad Shoaib Sehgal Computer Science
Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations
: DAG-Structured Mixture Models of Topic Correlations Wei Li and Andrew McCallum University of Massachusetts, Dept. of Computer Science {weili,mccallum}@cs.umass.edu Abstract Latent Dirichlet allocation
STRUCTURAL BIOINFORMATICS I. Fall 2015
STRUCTURAL BIOINFORMATICS I Fall 2015 Info Course Number - Classification: Biology 5411 Class Schedule: Monday 5:30-7:50 PM, SERC Room 456 (4 th floor) Instructors: Vincenzo Carnevale - SERC, Room 704C;
Content-based Recommendation
Content-based Recommendation Suthee Chaidaroon June 13, 2016 Contents 1 Introduction 1 1.1 Matrix Factorization......................... 2 2 slda 2 2.1 Model................................. 3 3 flda 3
Mixtures of Multinomials
Mixtures of Multinomials Jason D. M. Rennie jrennie@gmail.com September, 25 Abstract We consider two different types of multinomial mixtures, () a wordlevel mixture, and (2) a document-level mixture. We
2 GENE FUNCTIONAL SIMILARITY. 2.1 Semantic values of GO terms
Bioinformatics Advance Access published March 7, 2007 The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
BSc MATHEMATICAL SCIENCE
Overview College of Science Modules Electives May 2018 (2) BSc MATHEMATICAL SCIENCE BSc Mathematical Science Degree 2018 1 College of Science, NUI Galway Fullscreen Next page Overview [60 Credits] [60
Integrating Ontological Prior Knowledge into Relational Learning
Stefan Reckow reckow@mpipsykl.mpg.de Max Planck Institute of Psychiatry, Proteomics and Biomarkers, 80804 Munich, Germany Volker Tresp volker.tresp@siemens.com Siemens AG, Corporate Research & Technology,
Introduction to Bioinformatics
CSCI8980: Applied Machine Learning in Computational Biology Introduction to Bioinformatics Rui Kuang Department of Computer Science and Engineering University of Minnesota kuang@cs.umn.edu History of Bioinformatics
Non-parametric Clustering with Dirichlet Processes
Non-parametric Clustering with Dirichlet Processes Timothy Burns SUNY at Buffalo Mar. 31 2009 T. Burns (SUNY at Buffalo) Non-parametric Clustering with Dirichlet Processes Mar. 31 2009 1 / 24 Introduction
Replicated Softmax: an Undirected Topic Model. Stephen Turner
Replicated Softmax: an Undirected Topic Model Stephen Turner 1. Introduction 2. Replicated Softmax: A Generative Model of Word Counts 3. Evaluating Replicated Softmax as a Generative Model 4. Experimental
Topic Models. Brandon Malone. February 20, Latent Dirichlet Allocation Success Stories Wrap-up
Much of this material is adapted from Blei 2003. Many of the images were taken from the Internet February 20, 2014 Suppose we have a large number of books. Each is about several unknown topics. How can
Graph Wavelets to Analyze Genomic Data with Biological Networks
Graph Wavelets to Analyze Genomic Data with Biological Networks Yunlong Jiao and Jean-Philippe Vert "Emerging Topics in Biological Networks and Systems Biology" symposium, Swedish Collegium for Advanced
Differential Modeling for Cancer Microarray Data
Differential Modeling for Cancer Microarray Data Omar Odibat Department of Computer Science Feb, 01, 2011 1 Outline Introduction Cancer Microarray data Problem Definition Differential analysis Existing
Deep Learning for NLP
Deep Learning for NLP CS224N Christopher Manning (Many slides borrowed from ACL 2012/NAACL 2013 Tutorials by me, Richard Socher and Yoshua Bengio) Machine Learning and NLP NER WordNet Usually machine learning
Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text
Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text Yi Zhang Machine Learning Department Carnegie Mellon University yizhang1@cs.cmu.edu Jeff Schneider The Robotics Institute
Correlated Protein Function Prediction via Maximization of Data-Knowledge Consistency
Correlated Protein Function Prediction via Maximization of Data-Knowledge Consistency Hua Wang 1, Heng Huang 2,, and Chris Ding 2 1 Department of Electrical Engineering and Computer Science Colorado School
11/3/15. Deep Learning for NLP. Deep Learning and its Architectures. What is Deep Learning? Advantages of Deep Learning (Part 1)
11/3/15 Machine Learning and NLP Deep Learning for NLP Usually machine learning works well because of human-designed representations and input features CS224N WordNet SRL Parser Machine learning becomes
Computational Systems Biology
Computational Systems Biology Vasant Honavar Artificial Intelligence Research Laboratory Bioinformatics and Computational Biology Graduate Program Center for Computational Intelligence, Learning, & Discovery
CS Lecture 18. Topic Models and LDA
CS 6347 Lecture 18 Topic Models and LDA (some slides by David Blei) Generative vs. Discriminative Models Recall that, in Bayesian networks, there could be many different, but equivalent models of the same
人工知能学会インタラクティブ情報アクセスと可視化マイニング研究会 ( 第 3 回 ) SIG-AM Pseudo Labled Latent Dirichlet Allocation 1 2 Satoko Suzuki 1 Ichiro Kobayashi Departmen
Pseudo Labled Latent Dirichlet Allocation 1 2 Satoko Suzuki 1 Ichiro Kobayashi 2 1 1 Department of Information Science, Faculty of Science, Ochanomizu University 2 2 Advanced Science, Graduate School of
The Role of Network Science in Biology and Medicine. Tiffany J. Callahan Computational Bioscience Program Hunter/Kahn Labs
The Role of Network Science in Biology and Medicine Tiffany J. Callahan Computational Bioscience Program Hunter/Kahn Labs Network Analysis Working Group 09.28.2017 Network-Enabled Wisdom (NEW) empirically
Modeling User Rating Profiles For Collaborative Filtering
Modeling User Rating Profiles For Collaborative Filtering Benjamin Marlin Department of Computer Science University of Toronto Toronto, ON, M5S 3H5, CANADA marlin@cs.toronto.edu Abstract In this paper
Bioinformatics Chapter 1. Introduction
Bioinformatics Chapter 1. Introduction Outline! Biological Data in Digital Symbol Sequences! Genomes Diversity, Size, and Structure! Proteins and Proteomes! On the Information Content of Biological Sequences!
Recent Advances in Structured Sparse Models
Recent Advances in Structured Sparse Models Julien Mairal Willow group - INRIA - ENS - Paris 21 September 2010 LEAR seminar At Grenoble, September 21 st, 2010 Julien Mairal Recent Advances in Structured
Approximate inference for stochastic dynamics in large biological networks
MID-TERM REVIEW Institut Henri Poincaré, Paris 23-24 January 2014 Approximate inference for stochastic dynamics in large biological networks Ludovica Bachschmid Romano Supervisor: Prof. Manfred Opper Artificial
Finding Your Literature Match
a recommender system Edwin Henneken Smithsonian Astrophysical Observatory The Literature Universe Total records: Astronomy: 1,725,719 Physics: 5,437,973 The Literature Universe The Literature Universe
Applying hlda to Practical Topic Modeling
Joseph Heng lengerfulluse@gmail.com CIST Lab of BUPT March 17, 2013 Outline 1 HLDA Discussion 2 the nested CRP GEM Distribution Dirichlet Distribution Posterior Inference Outline 1 HLDA Discussion 2 the
Latent Dirichlet Allocation Based Multi-Document Summarization
Latent Dirichlet Allocation Based Multi-Document Summarization Rachit Arora Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai - 600 036, India. rachitar@cse.iitm.ernet.in
Lecture 13 : Variational Inference: Mean Field Approximation
10-708: Probabilistic Graphical Models 10-708, Spring 2017 Lecture 13 : Variational Inference: Mean Field Approximation Lecturer: Willie Neiswanger Scribes: Xupeng Tong, Minxing Liu 1 Problem Setup 1.1
Computational Biology From The Perspective Of A Physical Scientist
Computational Biology From The Perspective Of A Physical Scientist Dr. Arthur Dong PP1@TUM 26 November 2013 Bioinformatics Education Curriculum Math, Physics, Computer Science (Statistics and Programming)
Analysis of N-terminal Acetylation data with Kernel-Based Clustering
Analysis of N-terminal Acetylation data with Kernel-Based Clustering Ying Liu Department of Computational Biology, School of Medicine University of Pittsburgh yil43@pitt.edu 1 Introduction N-terminal acetylation
Staffing formula and enrollment limits X Other REMOVAL of GWAR attribute
CALIFORNIA STATE UNIVERSITY CHANNEL ISLANDS COURSE MODIFICATION PROPOSAL Courses must be submitted by October 15, 2013, and finalized by the end of the fall semester to make the next catalog (2014-15)
Comparison of Shannon, Renyi and Tsallis Entropy used in Decision Trees
Comparison of Shannon, Renyi and Tsallis Entropy used in Decision Trees Tomasz Maszczyk and W lodzis law Duch Department of Informatics, Nicolaus Copernicus University Grudzi adzka 5, 87-100 Toruń, Poland
Predicting Protein Functions and Domain Interactions from Protein Interactions
Predicting Protein Functions and Domain Interactions from Protein Interactions Fengzhu Sun, PhD Center for Computational and Experimental Genomics University of Southern California Outline High-throughput
Collaborative Hotel Recommendation based on Topic and Sentiment of Review Comments
DEIM Forum 2017 P6-2 Collaborative Hotel Recommendation based on Topic and Sentiment of Abstract Review Comments Zhan ZHANG and Yasuhiko MORIMOTO Graduate School of Engineering, Hiroshima University Hiroshima
Nonparametric Bayesian Methods (Gaussian Processes)
[70240413 Statistical Machine Learning, Spring, 2015] Nonparametric Bayesian Methods (Gaussian Processes) Jun Zhu dcszj@mail.tsinghua.edu.cn http://bigml.cs.tsinghua.edu.cn/~jun State Key Lab of Intelligent
IBM Q: building the first universal quantum computers for business and science. Federico Mattei Banking and Insurance Technical Leader, IBM Italy
IBM Q: building the first universal quantum computers for business and science Federico Mattei Banking and Insurance Technical Leader, IBM Italy Agenda Which problems can not be solved with classical computers?
Collaborative Filtering
Collaborative Filtering Nicholas Ruozzi University of Texas at Dallas based on the slides of Alex Smola & Narges Razavian Collaborative Filtering Combining information among collaborating entities to make
Comparative Summarization via Latent Dirichlet Allocation
Comparative Summarization via Latent Dirichlet Allocation Michal Campr and Karel Jezek Department of Computer Science and Engineering, FAV, University of West Bohemia, 11 February 2013, 301 00, Plzen,
Unsupervised Classification via Convex Absolute Value Inequalities
Unsupervised Classification via Convex Absolute Value Inequalities Olvi L. Mangasarian Abstract We consider the problem of classifying completely unlabeled data by using convex inequalities that contain
Preliminaries. Data Mining. The art of extracting knowledge from large bodies of structured data. Let s put it to use!
Data Mining The art of extracting knowledge from large bodies of structured data. Let s put it to use! 1 Recommendations 2 Basic Recommendations with Collaborative Filtering Making Recommendations 4 The
CS612 - Algorithms in Bioinformatics
Fall 2017 Databases and Protein Structure Representation October 2, 2017 Molecular Biology as Information Science > 12, 000 genomes sequenced, mostly bacterial (2013) > 5x10 6 unique sequences available
Welcome to CAMCOS Reports Day Fall 2011
Welcome s, Welcome to CAMCOS Reports Day Fall 2011 s, CAMCOS: Text Mining and Damien Adams, Neeti Mittal, Joanna Spencer, Huan Trinh, Annie Vu, Orvin Weng, Rachel Zadok December 9, 2011 Outline 1 s, 2
Gene Ontology and overrepresentation analysis
Gene Ontology and overrepresentation analysis Kjell Petersen J Express Microarray analysis course Oslo December 2009 Presentation adapted from Endre Anderssen and Vidar Beisvåg NMC Trondheim Overview How
Applying Latent Dirichlet Allocation to Group Discovery in Large Graphs
Lawrence Livermore National Laboratory Applying Latent Dirichlet Allocation to Group Discovery in Large Graphs Keith Henderson and Tina Eliassi-Rad keith@llnl.gov and eliassi@llnl.gov This work was performed
Latent variable models for discrete data
Latent variable models for discrete data Jianfei Chen Department of Computer Science and Technology Tsinghua University, Beijing 100084 chris.jianfei.chen@gmail.com Janurary 13, 2014 Murphy, Kevin P. Machine
Research topics and trends over the past decade ( ) of Baltic Coleopterology using text mining methods
Baltic J. Coleopterol. 14(1) 2014 ISSN 1407-8619 Research topics and trends over the past decade (2001-2013) of Baltic Coleopterology using text mining methods Yuno Do, Jarosław Skłodowski Do Y., Skłodowski
Kernel Density Topic Models: Visual Topics Without Visual Words
Kernel Density Topic Models: Visual Topics Without Visual Words Konstantinos Rematas K.U. Leuven ESAT-iMinds krematas@esat.kuleuven.be Mario Fritz Max Planck Institute for Informatics mfrtiz@mpi-inf.mpg.de
Simon Trebst. Quantitative Modeling of Complex Systems
Quantitative Modeling of Complex Systems Machine learning In computer science, machine learning is concerned with algorithms that allow for data analytics, most prominently dimensional reduction and feature
Efficient Tree-Based Topic Modeling
Efficient Tree-Based Topic Modeling Yuening Hu Department of Computer Science University of Maryland, College Park ynhu@cs.umd.edu Abstract Topic modeling with a tree-based prior has been used for a variety
Distributed Estimation, Information Loss and Exponential Families. Qiang Liu Department of Computer Science Dartmouth College
Distributed Estimation, Information Loss and Exponential Families Qiang Liu Department of Computer Science Dartmouth College Statistical Learning / Estimation Learning generative models from data Topic
Analyzing Burst of Topics in News Stream
1 1 1 2 2 Kleinberg LDA (latent Dirichlet allocation) DTM (dynamic topic model) DTM Analyzing Burst of Topics in News Stream Yusuke Takahashi, 1 Daisuke Yokomoto, 1 Takehito Utsuro 1 and Masaharu Yoshioka
Learning Gaussian Process Kernels via Hierarchical Bayes
Learning Gaussian Process Kernels via Hierarchical Bayes Anton Schwaighofer Fraunhofer FIRST Intelligent Data Analysis (IDA) Kekuléstrasse 7, 12489 Berlin anton@first.fhg.de Volker Tresp, Kai Yu Siemens
Machine learning methods for gene/protein function prediction
Machine learning methods for gene/protein function prediction Giorgio Valentini valentini@di.unimi.it DI - Dipartimento di Informatica Università degli Studi di Milano G.Valentini, DI - Univ. Milano 1
Text Mining for Economics and Finance Latent Dirichlet Allocation
Text Mining for Economics and Finance Latent Dirichlet Allocation Stephen Hansen Text Mining Lecture 5 1 / 45 Introduction Recall we are interested in mixed-membership modeling, but that the plsi model
Bioinformatics I. CPBS 7711 October 29, 2015 Protein interaction networks. Debra Goldberg
Bioinformatics I CPBS 7711 October 29, 2015 Protein interaction networks Debra Goldberg debra@colorado.edu Overview Networks, protein interaction networks (PINs) Network models What can we learn from PINs
Overview. Overview. Social networks. What is a network? 10/29/14. Bioinformatics I. Networks are everywhere! Introduction to Networks
Bioinformatics I Overview CPBS 7711 October 29, 2014 Protein interaction networks Debra Goldberg debra@colorado.edu Networks, protein interaction networks (PINs) Network models What can we learn from PINs
Text Mining: Basic Models and Applications
Introduction Basics Latent Dirichlet Allocation (LDA) Markov Chain Based Models Public Policy Applications Text Mining: Basic Models and Applications Alvaro J. Riascos Villegas University of los Andes
Clustering and Network
Clustering and Network Jing-Dong Jackie Han jdhan@picb.ac.cn http://www.picb.ac.cn/~jdhan Copy Right: Jing-Dong Jackie Han What is clustering? A way of grouping together data samples that are similar in