Mopping up the Flood of Data with Web Services

1
Mopping up the Flood of Data with Web Services

• Gary Wiggins
• Indiana University
• School of Informatics
• wiggins_at_indiana.edu

2
Overview of the Talk

• Data Mining and Knowledge Discovery
• DMKD in Bioinformatics
• DMKD in Chemistry
• Public Chemistry Databases for DMKD
• Overview of Web Services
• NIH-funded Projects Underway or Planned at
  Indiana University
• Educational Opportunities at IU

3
Data Mining and Knowledge Discovery (DMKD)

• Techniques began to be used around 1989
• Rapid growth in the mid 1990s, with DMKD field
  emerging around 1995
• Built on DM tools such as Machine Learning

4
Data Mining

• One of the steps in Knowledge Discovery
• Concerned with the actual extraction of knowledge
  from data
• Efficient and scalable methods for mining
  interesting patterns and knowledge and
  discovering hidden facts contained in large
  databases

5
Data Mining Techniques

• Efficient classification methods
• Clustering
• Outlier analysis
• Frequent, sequential, and structured pattern
  analysis
• Visualization and spatial/temporal analysis tools

6
Knowledge Discovery (KD)

• KD is a nontrivial process of identifying valid,
  novel, potentially useful, and ultimately
  understandable patterns from large collections of
  data.
• --Fayyad et al., as quoted by Cios and Kurgan
• The KD process involves
• Understanding and preparation of the data
• Data Mining (DM)
• Verification and application of the discovered
  knowledge

7
Framework for KD Process

• Steps range from very few, e.g.,
• Data collection and understanding
• Data mining
• Implementation
• To multi-step models, e.g., Cios and Kurgans
  six-step DMKD process model

8
Cios and Kurgans Six-Step DMKD Process Model

• Understanding the problem domain
• Understanding the data
• Preparation of the data
• 50 or more of effort spent on this step
• Data mining
• Evaluation of the discovered knowledge
• Using the discovered knowledge

9
General Data Mining/Data Analysis Systems

• SAS Enterprise Miner
• SPSS
• Insightful S-Plus
• IBM DB2 Intelligent Miner
• Microsoft SQLServer 2005
• SGI MLC and MineSet Tree Visualizer
• Inxight VizServer

10
Trends Major Conferences

• Knowledge Discovery and Data Mining (KDD) 2005
• http//www.informatik.uni-trier.de/ley/db/conf/kd
  d/kdd2005.html
• International Conference on Machine Learning
  (ICML) 2006
• http//www.icml2006.org/icml2006/technical/accepte
  d.html
• SIAM Conference on Data Mining 2006
• http//www.siam.org/meetings/sdm06/proceedings.htm
  

11
12th Annual SIGKDD International Conference
onKnowledge Discovery and Data Mining,
Philadelphia, August 20-23, 2006

• Areas of Interest on the Research Track
• Applications of data mining (biomedicine,
  business, e-commerce, defense)
• Data and result visualization
• Data warehousing
• Data mining for community generation, social
  network analysis and graph-structured data
• Foundations of data mining
• Interactive and online data mining
• KDD framework and process
• Mining data streams
• Mining high-dimensional data
• Mining sensor data
• Mining text and semi-structured data
• Mining multi-media data
• Novel data mining algorithms
• Privacy and data mining
• Robust and scalable statistical methods
• Pre-processing and post-processing for data
  mining
• Security issues
• Spatial and temporal data mining

12
Trends in DMKD

• OLAP (On-Line Analytical Processing)
• Data warehousing
• Association rules
• High Performance DMKD systems
• Visualization techniques
• Applications of DM
• More recently
• Database products that incorporate DM tools
• New developments in design and implementation of
  the DMKD process
• Information visualization products as end-user
  queries
• XML

13
XML the Key to DM and KD?

• Or simply a data exchange protocol?
• Allows for the description and storage of
  structured or semi-structured data and their
  relationships
• Can be used to exchange data in a
  platform-independent way
• BUTonly one paper at the major conferences
  listed earlier that dealt with XML

14
XML helps

• Standardize communication between diverse DM
  tools and databases (I/O procedures)
• Build standard data repositories sharing data
  between different DM tools that work on different
  software platforms
• Implement communication protocols between DM
  tools
• Provide a framework for integration of and
  communication between different DMKD steps

15
Predictive Model Markup Language (PMML) and Other
Tools

• In conjunction with XML, PMML enables the
  automation of sharing of discovered knowledge
  between different domains and tools
• XML-RPC
• SOAP (Simple Object Access Protocol)
• UDDI
• OLAP
• OLE DB-DM

16
Discovery Informatics Definition

• "Discovery Informatics is the study and practice
  of employing the full spectrum of computing and
  analytical science and technology to the singular
  pursuit of discovering new information by
  identifying and validating patterns in data."
  --William W. Agresti in 2003

17
Discovery Informatics

• Discovery and Application of Information
• Data Mining and Machine Learning are two aspects
  of Discovery Informatics.

18
Overview of the Talk

• Data Mining and Knowledge Discovery
• DMKD in Bioinformatics
• DMKD in Chemistry
• Public Chemistry Databases for DMKD
• Overview of Web Services
• NIH-funded Projects Underway or Planned at
  Indiana University
• Educational Opportunities at IU

19
Trends Bioinformatics Conferences

• International Conference on Intelligent Systems
  for Molecular Biology (ISMB) 2006
• http//ismb2006.cbi.cnptia.embrapa.br/papers.html
• Research in Computational Molecular Biology
  (RECOMB) 2006
• http//www.informatik.uni-trier.de/ley/db/conf/re
  comb/recomb2006.html
• Pacific Symposium on Biocomputing (PSB) 2006
• http//helix-web.stanford.edu/psb06/

20
Main Areas of Research in Bioinformatics

• Sequence alignment
• Alternative splicing
• Microarray analysis
• Functional analysis
• Analysis of single nucleotide polymorphisms
  (SNPs)
• Natural language text analysis

21
DMKD Sessions at Major Bioinformatics Conferences

• Databases and Data Integration
• Text Mining and Information Extraction
• Semantic Webs

22
Data Mining in Bioinformatics (Bajcsy)

• Data cleaning, data preprocessing, and semantic
  integration of heterogeneous, distributed
  biomedical databases
• Existing data mining tools for biodata analysis
• Development of advanced, effective, and scalable
  data mining methods in biodata analysis

23
Preprocessing of Biodata

• Integration of multiple microarray gene
  experiments must resolve inconsistent labels of
  genes to form a coherent data store.
• Focus on quantitative quality metrics based on
  analytical and statistical data descriptors and
  on relationships among variables.

24
Semantic Integration of Heterogeneous Biomedical
Databases

• Combine multiple sources into a coherent data
  store
• Find semantically equivalent real-world entities
  from several biomedical sources
• Problems
• Different labels for the same concept gene_id
  vs. g_id
• Time asynchronization same gene analyzed at
  multiple development stages

25
Approaches for Semantic Integration of Biodata

• Construction of integrated biodata warehouses or
  biodatabases
• Construction of a federation of heterogeneous
  distributed biodatabases
• Must build up mapping rules or semantic ambiguity
  resolution rules across multiple databases

26
Existing Data Mining Tools for Biodata Analysis-I

• Sequence Analysis, e.g.,
• NCBI/BLAST, ClustalW, HMMER, PHYLIP, MEME,
  TRANSFAC, MDScan, Vector NTI, Sequencher,
  MacVector
• Structure Prediction and Visualization, e.g.,
• RasMol, Raster3D, Swiss-Model, Scope, MolScript,
  Cn3D

27
Existing Data Mining Tools for Biodata Analysis-II

• Genome Analysis, e.g.,
• CAP3, Paracel GenomeAssembler, GenomeScan,
  GeneMark, GenScan, X-Grail, ORF Finder,
  GeneBuilder
• Pathway Analysis and Visualization, e.g.,
• KEGG, EcoCyc/MetaCyc, GenMapp
• Microarray Analysis, e.g.,
• ScanAlyze/Cluster/TreeView, Scanalytics
  MicroArray Suite, Profiler, Silicon Genetics

28
Biospecific Data Analysis Software Systems

• Agilent GeneSpring
• Spotfire
• Invitrogen VectorNTI

29
Text Mining in Bioinformatics

• Techniques have progressed from simple
  recognition of terms to extraction of interaction
  relationships in complex sentences.
• Search objectives have broadened to a range of
  problems, e.g.,
• Improving homology search
• Identifying cellular location
• Deriving genetic network technologies

30
Current Work in Biomedical Text Mining (Cohen and
Hersh)

• Text mining operates at a finer level of
  granularity than information retrieval and text
  summarization.
• TM examines relationships between specific kinds
  of information contained within and between
  documents.
• Areas of active research
• Named entity recognition (genes, proteins, etc.)
• Text classification
• Synonym and abbreviation extraction
• Relationship extraction
• Hypothesis generation
• Integrated frameworks

31
Systems Biology

• Requires a shift in focus from genes and proteins
  to the systems structure and dynamics
• Four key properties
• System structures
• System dynamics
• Control method
• Design method
• Systems Biology Markup Language (SBML) and CellML

32
iSpecies.org
33
Overview of the Talk

• Data Mining and Knowledge Discovery
• DMKD in Bioinformatics
• DMKD in Chemistry
• Public Chemistry Databases for DMKD
• Overview of Web Services
• NIH-funded Projects Underway or Planned at
  Indiana University
• Educational Opportunities at IU

34
Data Mining in Chemistry

• Modern experimentation (whether classical or
  high-throughput) should be based on the
  productive interplay of statistical techniques
  (design-of-experiments), molecular modeling as
  well as cheminformatics.
• --Ulrich S. Schubert

35
Session on Integration of Informatics and
Knowledge Management Informatics

• Integration of Informatics at the Systems Level
  and at the Data LevelChris L. Waller, Ph.D.,
  Director, World Wide Chemistry Informatics,
  Pfizer Global Research Development 
• Integrated Knowledge Management at Bayer
  HealthCare Pharmacophore Informatics William J.
  Scott, Ph.D., Team Leader, Department for
  Chemistry Research, Bayer Pharmaceuticals
  Corporation
• Building a Knowledge Enabled OrganizationCory R.
  Brouwer, Ph.D., Associate Director, Knowledge
  Management Informatics, Pfizer Global Research
  Development
• Knowledge Management Building a Knowledge
  Enabled OrganizationVictor Lobanov, Ph.D.,
  Principal Scientist, MDI, Johnson Johnson
  Pharmaceutical RD
• 10th Annual Cheminformatics Conference, May
  23-16, 2006, Philadelphia

36
Impact of HTS and Combinatorial Chemistry Research

• Most impact in
• the pharmaceutical industry
• medical research
• catalyst research
• More recently
• polymer and materials research.

37
Diversity of Data Mining in Chemistry

• On 5/7/2006 there were 4072 references to either
  datamining or data mining in Chemical
  Abstracts.
• 3416 different index terms were assigned to those
  records.
• 2772 used 1-5 times (81)
• 298 used 6-10 times (9)
• 103 used 11-15 times (3)
• 71 used 16-20 times (2)
• 38 used 21-25 times (1)
• 24 used 26-30 times (1)
• 110 for 31-480 times (3)
• Most frequent co-term bioinformatics with 480
  hits or 12 of the occurrences

38
SFS graph
39
Components of the Semantic Web for Chemistry

• XML eXtensible Markup Language
• RDF Resource Description Framework
• RSS Rich Site Summary
• Dublin Core allows metadata-based newsfeeds
• OWL for ontologies
• BPEL4WS for workflow and web services
• Murray-Rust et al. Org. Biomol. Chem. 2004, 2,
  3192-3203.

40
Chemical Markup Language (CML)

• Much of the semantics in a chemical article can
  be supported by CML
• Molecules
• Structures
• Reactions and reaction schemes
• Spectra (including annotations)
• Physicochemical data
• XML dictionaries and lexicons provide linguistic
  and semantic support for markup
• Will lead to quicker authoring and higher quality
  of embedded structures and data through machine
  validation

41
Key Factors in the Success of the Chemical
Semantic Web

• Institutional Repositories services deployed and
  supported at an institutional level to offer
  dissemination management, stewardship, and where
  appropriate, long-term preservation of both the
  intellectual work created by an institutional
  community and the records of the intellectual and
  cultural life of the institutional community
• Open Access Movement

42
Knowledge-Driven Bioinformatics Enhanced with
Chemistry
43
Text Mining (Banville)

• In the pharmaceutical field, it is ideally the
  marriage of biological and chemical information
  that needs to be the ultimate focus of text data
  mining applications.
• Problems
• Lack of universal publication standards for
  identifying each unique chemical entity
• Selective indexing policies of AI services
• Need to understand how chemical structures link
  to biological processes

44
OSCAR3 Service

• Open Java source application under development by
  Peter Murray-Rust group at Cambridge (Not
  published yet)
• Extracts chemical information from either a
  paragraph of experimental data or a full paper
  (e.g. melting points, infra-red and NMR data, and
  mass spectral information)
• Produces an XML instance highlighting the
  chemical information with an Extensible
  Stylesheet Language (XSL) file
• At IU, we are attaching SOAP input/output engine
  for a web service based on OSCAR3.

45
OSCAR at Work in the Future
46
Semantic Scholars Grid I
Local MDStore
Local HarvestStore
Fetch MD and Documents
Gatherer
Query and Get list
Indexer
Analyzer
Index all Local MD
Run filter such asOSCAR onharvested MDand
documents Store new MD
47
Semantic Scholars Grid II
Local MDStore
Plug-in
SynchronizeSSG andforeign MD
Updater
CommunityTools
SSGViewer
Instant Citation Index etc.
Update local MD Control foreign interactions View
all MD Access Community Tools
Update and viewforeign MD
48
Chemical Datamining Software

• SureChem
• http//surechem.reeltwo.com/
• CLiDE
• Recognizes structures, reactions, and text
• http//www.simbiosys.ca/clide/
• OSCAR
• OSCAR1 to check experimental data
• http//www.ch.cam.ac.uk/magnus/checker.html
• http//www.rsc.org/Publishing/ReSourCe/AuthorGuide
  lines/AuthoringTools/ExperimentalDataChecker/
• CSR (Chemical Structure Reconstruction)
• http//www.scai.fraunhofer.de/uploads/media/MZ-ERC
  IM05_04.pdf
• MDL DocSearchcombines MDLs Isentris platform
  and EMCs Documentum

49
Overview of the Talk

• Data Mining and Knowledge Discovery
• DMKD in Bioinformatics
• DMKD in Chemistry
• Public Chemistry Databases for DMKD
• Overview of Web Services
• NIH-funded Projects Underway or Planned at
  Indiana University
• Educational Opportunities at IU

50
ChemDB http//cdb.ics.uci.edu/CHEM/Web/
51
ChEBI, Chemical Entities of Biological Interest

• Dictionary of molecular entities focused on small
  chemical compounds
• Features an ontological classification, showing
  the relationships between molecular entities or
  classes of entities and their parents and/or
  children

52
Vioxx Entry in ChEBI
53
The IUPAC International Chemical Identifier
(InChI)

• Open source, non-proprietary, public-domain
  identifier for chemicals
• String of characters that uniquely represent a
  molecular substance
• Independent of the way the chemical structure is
  drawn
• Enables reliable structure recognition and easy
  linking of diverse data compilations
• Accepts as input MOLfiles (or SDfiles) and CML
  files
• Download the program to your computer at
• http//www.iupac.org/inchi/license.html

54
Generation of InChI for Vioxx with wInChI
55
Vioxx Entry in PubChem Compounds Found with InChI
56
Vioxx Bioassay Data in PubChem
57
Vioxx PubChem Link to External Sources of
Information
58
PubChem Link to Elsevier MDL

• DiscoveryGate www.discoverygate.com
• provides access to integrated scientific content
  from databases, journal articles, patent
  publications and reference works
• information providers include Elsevier,
  Thomson-Derwent, FIZ CHEMIE, the U.S. FDA, Prous
  Science and Thieme
• MDL Compound Index (the master list of substances
  included in DiscoveryGate data sources) now
  exceeds 14 million unique chemical structures
  with the addition of 5 million chemical
  structures from the PubChem database.

59
The Elsevier MDL/NIH Link via PubChem and
DiscoveryGate

• Cross-indexes PubChem to the Compound Index
  hosted on Elsevier MDLs DiscoveryGate platform
• MDL added 5 million structures from PubChem to
  their index, resulting in over 14 million unique
  chemical structures
• Links go both ways
• Can move from biological data in PubChem to
  bioactivity, chemical sourcing, synthetic
  methodology, and EHS data in DiscoveryGate
  sources

60
Elsevier MDLs xPharm

• Comprehensive set of records linking
• Agents (compounds) (2300)
• Targets (600)
• Disorders (450)
• Principles that govern their interactions (180)
• Answers questions such as
• What targets are associated with control of blood
  pressure?
• What adverse effects are associated with
  monoamine oxidase inhibitors?

61
Web Guide for Essential Cheminformatics Resources

• http//www.chembiogrid.org
• http//www.indiana.edu/cheminfo/cicc/

62
ChemBioGrid Chemical Databases
63
Overview of the Talk

• Data Mining and Knowledge Discovery
• DMKD in Bioinformatics
• DMKD in Chemistry
• Public Chemistry Databases for DMKD
• Overview of Web Services
• NIH-funded Projects Underway or Planned at
  Indiana University
• Educational Opportunities at IU

64
Web Services Overview

• What are Web Services?
• A distributed invocation system built on Grid
  computing
• Independent of platform and programming language
• Built on existing Web standards
• A service oriented architecture with
• Interfaces based on Internet protocols
• Messages in XML (except for binary data
  attachments)

65
Web Services for Chemistry Problems

• Performance and scalability
• Proprietary data
• Competition from high-performance desktop
  applications
• -- Geoff Hutchison, its a puzzle blog,
  2005-01-05
• ALSO
• Lack of a substantial body of trustworthy Open
  Access databases
• Non-standard chemical data formats (over 40 in
  regular use and requiring normalization to one
  another)

66
DM Internet Toolbox Architecture
67
Overview of the Talk

• Data Mining and Knowledge Discovery
• DMKD in Bioinformatics
• DMKD in Chemistry
• Public Chemistry Databases for DMKD
• Overview of Web Services
• NIH-funded Projects Underway or Planned at
  Indiana University
• Educational Opportunities at IU

68
Indiana University Planned Projectshttp//www.ch
embiogrid.org

• Application of a Grid-based distributed data
  architecture to chemistry
• Development of tools for HTS data analysis and
  virtual screening
• Database for quantum mechanical simulation data
• Chemical prototype projects
• Novel routes to enzymatic reaction mechanisms
• Mechanism-based drug design
• Data-inquiry-based development of new methods in
  natural product synthesis

69
Web Services for Chemistry at IU
70
NCI Developmental Therapeutics Program (DTP)

• Downloadable data
• In vitro 60 cell line results
• in vitro anti-HIV results
• Yeast assay
• 200,000 chemical structures
• molecular targets
• microarray data
• Or search the database at
• http//dtp.nci.nih.gov/docs/dtp_search.html

71
IU Database of NIH DTP Data

• Contains over 200,000 chemical structures tested
  in 60 cellular assays from different human tumor
  cell lines
• Also includes microarray assay profiles for the
  untreated cell lines (14,000 datapoints)
• A local PostgreSQL database containing the data
  that is exposed as a web service
• Using workflows and complex SQL queries, we can
  do advanced data mining that exploits the
  chemical, biological and genomic information for
  particular audiences (chemists, biologists, etc)

72
Mining the NIH DTP database
14,000 gene expression values
60 cell lines
Cell lines can be clustered based on gene
expression similarity
200,000 compounds
Compounds can be clustered based on similarity of
profile across cell lines, or by chemical
structure fingerprint similarity
73
Use of Taverna at IU

• A protein implicated in tumor growth is supplied
  to the docking program (in this case HSP90 taken
  from the PDB 1Y4 complex)
• The workflow employs our local NIH DTP database
  service to search 200,000 compounds tested in
  human tumor cellular assays for similar
  structures to the ligand.
• Client portlets are used to browse these
  structures
• Once docking is complete, the user visualizes the
  high-scoring docked structures in a portlet using
  the JMOL applet.
• Similar structures are filtered for drugability,
  and are automatically passed to the OpenEye FRED
  docking program for docking into the target
  protein.
• A 2D structure is supplied for input into the
  similarity search (in this case, the extracted
  bound ligand from the PDB IY4 complex)
• Correlation of docking results and biological
  fingerprints across the human tumor cell lines
  can help identify potential mechanisms of action
  of DTP compounds

74
Taverna Workflow
Workflow definition
Available web services (WSDL)
Visual depiction of workflow
75
Taverna in Action
76
CGL Contributions to CICC

• Build Web/Grid services for connecting
• Data sources
• Applications (simulation, data mining, data
  assimilation, imaging, etc).
• Computing resources
• Information services.
• Third party tool evaluation
• Workflow (Taverna)
• Grid tools Globus and Condor (for interacting
  with TeraGrid)
• Building standards-based Web portal environments.
• OGCE grid portal project
• JSR 168 Java standards.
• This activity will begin in earnest over the
  summer.

77
Digital Chemistry (BCI) Clustering Service Methods
78
Local Web Service Methods for WWMM of PMRs Group
79
More Services
80
ToxTree

• An in silico toxicology prediction suite
• Based on the CDK toolkit
• Built on CML
• Released as OpenSource under the GPL
• Standalone PC software
• User Manual http//ecb.jrc.it/DOCUMENTS/QSAR/TOXT
  REE/toxTree_user_manual.pdf

81
ToxTree Service

• An open Java source application by Nina
  Jeliazkova
• Estimates toxic hazard by applying a decision
  tree approach.
• Encodes the Cramer scheme
• (Cramer G. M., R. A. Ford, R. L. Hall,
  Estimation of Toxic Hazard - A Decision Tree
  Approach, J. Cosmet. Toxicol., Vol.16, pp.
  255-276, Pergamon Press, 1978)
• Could be applied to datasets from various
  compatible file types.
• We are converting this GUI application to a
  text-based web service

82
Overview of the Talk

• Data Mining and Knowledge Discovery
• DMKD in Bioinformatics
• DMKD in Chemistry
• Public Chemistry Databases for DMKD
• Overview of Web Services
• NIH-funded Projects Underway or Planned at
  Indiana University
• Educational Opportunities at IU

83
Chemoinformatics Education at IU

• School of Informatics degree programs
• BS, MS, PhD
• Programs offered at both the Indianapolis (IUPUI)
  and Bloomington (IUB) campuses

84
Other Educational Activities

• Graduate Certificate Program in Chemical
  Informatics (4 courses by Distance Education)
• I571 Chemical Information Technology (3 cr.)
• I572 Computational Chemistry and Molecular
  Modeling (3 cr.)
• I573 Programming Techniques for Chemical and Life
  Science Informatics (3 cr.)
• I553 Independent Study in Chemical Informatics (3
  cr.)
• I571 as CIC Courseshare offering w. Michigan
• Experiments with teleconferencing as a distance
  education tool

85
PhD in Informatics

• Began in August 2005
• Tracks
• bioinformatics chemical informatics health
  informatics human-computer interaction design
  social and organizational informatics
• Under development
• complex systems, networks, modeling and
  simulation cybersecurity discovery and
  application of information logical and
  mathematical foundations music informatics

86
Graduate Enrollment Chemo-, Laboratory, Bio-,
Health Informatics
87
Software/DBs Used in the Program

• Company Products and/or (Target
  Area)
• ArrgusLab (Molecular modeling)
• Digital Chemistry Toolkit (Clustering)
• Cambridge Cryst Data Ctr Cambridge Structrual DB
  GOLD
• CambridgeSoft ChemDraw Ultra
• Chemical Abstracts Service SciFinder Scholar
• Chemaxon Marvin (and other software)
• Daylight Chemical Info System Toolkit
• FIZ Karlsruhe Inorganic Crystal Structure DB
• IO-Informatics Sentient
• MDLCrossFire Beilstein and Gmelin
• OpenEye Toolkit (and other software)
• Sage Informatics ChemTK
• Serena Software PCMODEL
• Spotfire DecisionSite
• STN International STN Express with Discover
  (Anal Ed)
• Wavefunction Spartan

88
Closing quote

• The future of chemistry depends on the
  automated analysis of chemical knowledge,
  combining disparate data sources in a single
  resource, . . . which can be analysed using
  computational techniques to assess and build on
  these data.
• Townsend et al. Org. Biomol. Chem. 2004, 2, 3299.

89
We all need help when overloaded!
90
Bibliography

• Agresti, William W. Discovery informatics.
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  United States, March 26-30, 2006, CINF-055.

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  Lee Jae K., Tanabe Lorraine, Kohn Kurt W.,
  Reinhold William C., Myers Timothy G., Andrews
  Darren T., Scudiero Dominic A., Eisen Michael B.,
  Sausville Edward A., Pommier Yves, Botstein
  David, Brown Patrick O., Weinstein John N. A
  gene expression database for the molecular
  pharmacology of cancer. Nature Genetics 2000,
  24, 236-244.
• Schubert, Ulrich S. "Materials informatics from
  data to knowledge towards integrated escience
  approaches." QSAR Combinatorial Science 2005,
  24(1), 5. (NB Entire issue is devoted to this
  topic.)
• SIAM International Conference on Data Mining
  (5th 2005 Newport Beach, CA) Data Mining
  Proceedings. Kargupta, Hillol et al., eds. SIAM,
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• Torr-Brown, Sheryl. Advances in knowledge
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Web 2.0

• Social Software allows group interactions
• Enables groups to form and organize themselves
• Examples
• Wikis
• Blogs
• RSS (now found on chemistry.org)
• Podcasting/Coursecasting
• Webcasting/Webinars
• Flickr
• Jybe
• FURL

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FURL (Frame Uniform Resource Locater)

• For archiving and sharing of web pages
• Furler can capture the pages for a discussion
  group
• Tracks useful pages for a discussion
• http//www.furl.net/home.jsp

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Jybe (Join Your Browser with Everyone)

• Collaboration and communication in real time with
  IE and Firefox
• Screen-sharing AND editing
• Privacy protected must be invited
• Upload documents to convert to html
• http//www.jybe.com