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A Vision for Using All the Data and
Publications from Science on Web:
Mining this to
Study the Structure of Science
1 1 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu
Mark B Gerstein
Yale (Comp. Bio. & Bioinformatics)
NSF Workshop on Knowledge Management and Visualization Tools
2008.03.11, 09:30-10:00
Gerstein.info/talks (c) 2008
Slides downloadable from Lectures.GersteinLab.org
(Please read permissions statement.)
(Textmining talk, fits into ~30' w. interrupting questions)
Do not reproduce without permission
[Greenbaum & Gerstein, Nat. Biotech. ('03)]
langscape
changing the
DBs in science,
Rapid growth in
Do not reproduce without permission
Gerstein.info/talks (c) 2008
2 2 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu
Part of the Changing Landscape:
Situation Facing DBs and Journals
• Distinctions Blurring
Reading Journals via queries
3 3 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu
• Reading DB entries
Towards reading literature with computers
• Mining text and correlating papers
• Biology as a science of heterogeneous facts
Well-suited to database storage
Gerstein.info/talks (c) 2008
[Gerstein, Bioinformatics ('99); Gerstein & Junker. Nature Yearbook ('02)]
Do not reproduce without permission
Conventional Challenge
• Hard to keep up with volume and growth of
publications
• Missed opportunities in connections between fields
• Harness the power of technology to help scientists
share information?
New Vision
(c) 2008
• Discover new scientific relationships
• Study the Structure of Science itself
4 Gerstein.info/talks
4
Do not reproduce without permission
Overall
Process
of Web
Mining
.
5 5 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu
Fig. removed
until paper in press
Gerstein.info/talks (c) 2008
[Rzhetsky et al,
Cell ('08,
submitted)]
Do not reproduce without permission
Overall
Process
of Web
Mining
Digest Texts
into Simpler
6 6 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu
Machine
Understand-
able Form
and then
Synthesize
Gerstein.info/talks (c) 2008
[Rzhetsky et al,
Cell ('08,
submitted)]
Do not reproduce without permission
Overall
Process
of Web
Mining
Doing better science:
Finding new protein
relationships (e.g.
7 7 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu
protein interactions),
looking for inconsist-
encies in arguments,
assembling consen-
sus definitions
automatically
Krauthammer et al.
Molecular triangulation: bridging
Gerstein.info/talks (c) 2008
linkage and molecular-network
information for identifying candidate
genes in Alzheimer's disease. PNAS
('04); Iossifov et al. Probabilistic
inference of molecular networks from
noisy data sources.
Bioinformatics ('04)
[Rzhetsky et al,
Cell ('08,
submitted)]
Do not reproduce without permission
Overall
Process
of Web
Mining
Mapping
Science
8 8 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu
+
Studying its
Dynamics &
Evolution
Gerstein.info/talks (c) 2008
[Rzhetsky et al,
Cell ('08,
submitted)]
Do not reproduce without permission
Overall
Process
of Web
Mining
• Revealing
patterns of
collaboration
9 9 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu
• Understanding
basis of terms &
nomenclature
•Tracking the
evolution of ideas
• Models for the
evolution of
Gerstein.info/talks (c) 2008
science;
• Helping set policy
& research
directions
[Rzhetsky et al,
Cell ('08,
submitted)]
Do not reproduce without permission
Overall
Process
of Web
Mining
Making it
understand-
10 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
able (through
“mashup”)
10 (c) Mark Gerstein, 2008
[Rzhetsky et al,
Cell ('08,
submitted)]
Do not reproduce without permission
Examples Illuminating Current State of Affairs:
Mining Simple Term Occurrence
Statistics to Understand and Justify
Directions in Science
(c) 2008
11 Gerstein.info/talks
Do not reproduce without permission
Over-representation of crystallography
among the Nobel Prizes, highlighted
by the 2006 Nobels
(c) 2008
12 Gerstein.info/talks
[Seringhaus & Gerstein, Science (2007)
Do not reproduce without permission
The current state of mammalian gene annotation:
a rationale for data driven research
p53
TNF
VEGF
13 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
EGFR
ER
NFKB
TGFB
IL-6
COX2
BCL2
13 (c) Mark Gerstein, 2008
Adapted from Su and Hogenesch, Genome Biology, 2007 permission
Do not reproduce without
Gene Name Skew
14 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
14 (c) Mark Gerstein, 2008
[Seringhaus et al. GenomeBiology (2008)]
Do not reproduce without permission
Ex. Naming Issue: Starry Night
Starry night (P Adler, ’94)
15 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
15 (c) Mark Gerstein, 2008
[Seringhaus et al. GenomeBiology (2008)] Do not reproduce without permission
Naming
Pathologies:
Related to Single
Genes
16 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
(b) drop dead: flies with mutations in drop dead die
rapidly after their brain rapidly deteriorates. (c) malvolio:
gene needed for normal taste behaviour. Malvolio in
Shakespeare's Twelfth Night tasted "with distempered
appetite". (d) LOV: light, oxygen, or voltage (LOV) family
of blue-light photoreceptor domains. (e) yuri: this gene
16 (c) Mark Gerstein, 2008
was discovered on the anniversary of Yuri Gagarin's
space flight. Mutants have problems with gravitaxis and
cannot stay aloft. (f) tribbles: cells divide uncontrollably,
like the eponymous Star Trek characters. (g) kuzbanian:
mutants have uncontrollable bristle growth. Koozbanians
are alien Muppets with uncontrollable hair growth;
spelling was changed to avoid copyright infringement. (h)
ring: really interesting new gene. (i) yippee: a graduate
student’s reaction on cloning the gene
[Seringhaus et al. GenomeBiology (2008)]
Do not reproduce without permission
Naming
Pathologies:
Involving Multiple
Gene Names
17 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
(j) kryptonite and superman: the kryptonite mutation
suppresses the function of the SUPERMAN gene. (k)
arleekin, valient, tungus: mutations in arleekin, valient,
17 (c) Mark Gerstein, 2008
tungus and 29 other genes affect long-term memory.
Named after Pavlov's dogs. (l) PKD1 (human) and lov-1
(worm): these are homologs, although their names do not
suggest it. (m) MT-1: this label can refer to at least 11
different human genes. (n) BAF45 and BAF47: names for
the same gene, reflecting a revision of the molecular
weight of product.
[Seringhaus et al. GenomeBiology (2008)]
Do not reproduce without permission
Examples Illuminating Current State of Affairs:
Using Network Representations to
Make Maps of Science -- Studying
the Publication Patterns of
Genomics Consortia
18 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
18 (c) Mark Gerstein, 2008
[Douglas et al. GenomeBiol. ('05), pubnet.gersteinlab.org]
Do not reproduce without permission
Co-Authorship
Publication
Network of Struc.
Genomics
Consortia (NESG)
19 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
19 (c) Mark Gerstein, 2008
[Douglas et al. GenomeBiol. ('05), pubnet.gersteinlab.org]
Do not reproduce without permission
Co-
authorship
Networks
(45) comparing
the 9 NIH
20 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
Structural
Genomics
Centers
(19)
20 (c) Mark Gerstein, 2008
Average
Degree
[Douglas et al.
GenomeBiol. ('05),
(7) pubnet.gersteinlab.org]
Do not reproduce without permission
[Douglas et al. GenomeBiol. ('05), pubnet.gersteinlab.org]
Different Representations of
Publication Network of NESG
Do not reproduce without permission
21 (c) Mark Gerstein, 2008
21 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
Clustering
structures
determined
by struc.
genomics
consortia
according to
functional
22 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
similarity:
Is there a
functional
bias in
consortia
22 (c) Mark Gerstein, 2008
structures?
Avg. Avg. Path Clust. Diameter
Degree Coeff.
PSI 24 2.6 37% 7
PDB 6 3.9 31% 9 [Douglas et al. GenomeBiol. ('05), pubnet.gersteinlab.org]
Do not reproduce without permission
Making
Larger Maps:
Mapping the
whole field
of Melanoma
Research
(c) 2008
23 Gerstein.info/talks
[Boyack, Kevin W., Mane, Ketan and Börner, Katy. (2004). Mapping Medline Papers, Genes, and
Proteins Related to Melanoma Research. IV2004 Conference, London, UK, pp. 965-971. ]
Do not reproduce without permission
Backbone Map of Science & Soc. Sci.
(c) 2008
24 Gerstein.info/talks
[http://grants.nih.gov/grants/KM/OERRM/OER_KM_events/Borner.pdf
64(3), 351-374.40]
Boyack, Kevin W., Klavans, R. and Börner, Katy. (2005). Mapping the Backbone of Science. Scientometrics.Do not reproduce without permission
Ranking Journal Influence -
Eigenfactor.org
―Ranks journals much as Google ranks
websites.‖
Adjusts for citation differences among
disciplines
(c) 2008
25 Gerstein.info/talks
Ranking of journals in
computer science (top
of list).
25
Do not reproduce without permission
Examples Illuminating Current State of Affairs:
Analyzing the Dynamics of Science
(c) 2008
26 Gerstein.info/talks
Do not reproduce without permission
1st
Pub- Pub-
Google 'Omics terms over the years
Med Med
Hits
Hits Hit [Greenbaum et al. Gen. Res. ('01)]
Year
Genome ~1880000 66171 1932
Proteome ~63,000 703 1995
Transcriptome 3520 72 1997
Physiome 2980 15 1997
27 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
Metabolome 349 12 1998
Phenome 4980 6 1995
Proteome
Morphome 238 2 1996
PubMed Hits
Interactome 56 2 1999
Glycome 46 1 2000
Secretome 21 1 2000
Ribonome 1 1 2000
27 (c) Mark Gerstein, 2008
Orfeome 42 - -
Regulome 18 - -
Cellome 17 - -
Operome 8 - -
Transportome 1 - -
Functome 1 - - Do not reproduce without permission
Evolution of Science
Map based on ―bursty‖
words in life sciences
publications since 1980.
Older fundamental research
(center) led to four different
areas (subgraphs in
corners).
This and other domain
maps at
http://www.scimaps.org.
(c) 2008
28 Gerstein.info/talks
28
•K. Mane, K. Börner (2004). Mapping topics and topic bursts in PNAS. 101 (Supplement 1): 5287.
Do not reproduce without permission
RNAi:
Birth of a
Field in
the
Literature
Culmin-
ating in
29 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
the 2006
Nobel
29 (c) Mark Gerstein, 2008
Source:
Gerstein & Douglas.
PLoS Comp. Bio. 3:e80
(2007)
PubNet.GersteinLab.org
Do not reproduce without permission
The Social Dynamics of Innovation and
Scientific Discovery:
Making Models of Science
The Patterns of
Discovery and the
Spread of Ideas as
Epidemics.
The population dynamics of authors in an emerging field is well described by models
similar to those of epidemics, but that take into account contact processes and
intentionality characteristic of human social dynamics. Panel (a) shows a SEIRZ
model, (b) its best solution applied to the spread of Feynaman diagrams in the USA,
Japan and the Soviet Union, and (c) details the model parameter's interpretation. The
spread of ideas is characterized by relatively low contact rates (compared to infectious
(c) 2008
diseases), and very long lifetimes for the idea, as well as intentional strtuctures to
promote interaction between individuals during the learning process.
30 Gerstein.info/talks
30
[Bettencourt et al., Physica A (2006)] Do not reproduce without permission
Examples Illuminating Current State of Affairs:
Mashing up the Text from Scientific
Publications with other information
sources to make Science more
Understandable
• Mashing up scientific texts with streamed video,
genome annotation, protein structure & interactions
• SciVee
http://www.scivee.tv
Partnership: NSF, PloS, San Diego Supercomputing Center
(c) 2008
Pubcasts—video correlated with PLoS papers automatically displayed as video runs
Videos—scientists upload their own without papers
31 Gerstein.info/talks
• Journal of Visualized Experiments (JoVE)
http://www.jove.com
Monthly issues of theme-related videos
Procedure walk-throughs, interviews
High-quality video and sound
Do not reproduce without permission
SciVee
(c) 2008
32 Gerstein.info/talks
32
Do not reproduce without permission
JoVE
(c) 2008
33 Gerstein.info/talks
33
Do not reproduce without permission
Fusing Data & Papers
to Annotate the Genome
• Ideal project for 21st century is annotating every base
of the genome
Want to attach all publications and results to the genome
"Fly through Genome" as way to access and understand the
literature
• Problem of a good browser....
(c) 2008
34 Gerstein.info/talks
[Gerstein, Science ('00), Nature ('06)]
Do not reproduce without permission
We need a Google Earth for the
Genome; A Step in this Direction...
(c) 2008
35 Gerstein.info/talks
[Herr, Holloway, Börner, Emergent Mosaic of Wikipedian Activity, 2007] Do not reproduce without permission
Vision
Impediments to the
Do not reproduce without permission
36 (c) Mark Gerstein, 2008
36 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
• Need to perform a
DB
“distributed query” over Interoperation
many sites & Federated
Conventional web links Information
More complex interfaces Architecture
• Annotation of the human
37 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
genome involves a massive
federation of interoperating
servers
"Administered" by many
37 (c) Mark Gerstein, 2008
disparate people and groups
[Smith et al., BMC Bioinfo. ('07)]
Do not reproduce without permission
Impediment #1:
Structuring the
Information
38 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
Correctly for Large-
scale Query
38 (c) Mark Gerstein, 2008
Do not reproduce without permission
Structuring the Information
EF2_YEAST Curated DB entries in Uniprot
vs Unstructured scientific text
Structured Semantic Web [Berners-Lee et al, Sci.
Am. (2001)] vs purely unstructured text mining
Descriptive Name:
Elongation Factor 2
39 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
Lots of references
to papers
Summary sentence
39 (c) Mark Gerstein, 2008
describing function:
This protein promotes the
GTP-dependent
translocation of the
nascent protein chain from
the A-site to the P-site of
the ribosome. [Smith & Gerstein, Science ('06), Tech Rev. (Jul. '07)]
Do not reproduce without permission
Other Issues with the Current
Situtation between DBs & Journals
• Not always a clear linkage between papers & DBs
Keeping entries in DB and paper in sync
40 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
• Data aliquot
Huge datasets are handled but what of isolated facts
• How to connect key attributes of Journals with DBs
Attribution for credit & accountability
Time stamping of unchanging entries
40 (c) Mark Gerstein, 2008
Citation and history
Well worked out process of QC via refereeing and editing
• Readability of Papers
Detailed data embedded into papers, making text hard to read
[Gerstein, Bioinformatics ('99); Gerstein & Junker. Nature Yearbook ('02)]
Do not reproduce without permission
Structured Abstract Proposal as a
Compromise
• Storing information in papers in machine interpretable
fashion
for automatic deposition into DBs
Abstract + standardized view of all tables
• Cross-referencing it with a specific part of the global
41 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
genome, proteome, and interactome
Article written as annotation from the start
• Done in parallel to submission & revision of normal
journal article
Refereed & edited normally
41 (c) Mark Gerstein, 2008
Capitalizes on peer review & incentives to publish
• Curators vs editors
Author is in control and this process
But it’s officiated by referees and editors
[Seringhaus & Gerstein, FEBS ('08); Gerstein et al., Nature ('07)]
Do not reproduce without permission
[Seringhaus & Gerstein, BMC Bioinformatics (2007)] Ex. Structured Abstract
Do not reproduce without permission
42 (c) Mark Gerstein, 2008
42 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
• K.lactis (species)
KlSTE4 (gene)
• KlSte4p (protein) Ex. Structured
– CLONED
» Available at … Abstract
[Seringhaus & Gerstein, BMC Bioinformatics (2007)]
– SEQUENCED
» Sequence
ATGTACGCTATAGGC….
– MUTANTS KlGPA1 (gene)
» DELETION • KlGpa1p (protein)
» FUNCTIONAL ASSAYS – INTERACTIONS
» Sterile in both MATa and » TWO-HYBRID
43 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
MATα » KlSte4 = XXX
» No defect in vegetative • KlGpa1p* (protein)
growth – INTERACTIONS
» STRAIN INFORMATION » TWO-HYBRID
» Available at…. » KlSte4 = XXX
– INTERACTIONS KlGPA2 (gene)
» TWO-HYBRID • KlGpa2p (protein)
» KlGpa1p (10x stronger) = – INTERACTIONS
XXX
» TWO-HYBRID
43 (c) Mark Gerstein, 2008
» Control (no partner) = XXX
» KlSte4 = XXX
» KlGpa1p* = XXX
» KlGpa2p = XXX
• S.cerevisiae (species)
SCGPA1 (gene)
» ScGpa1p = XXX (S.
cerevisiae) • ScGpa1p (protein)
– COMMENTS – INTERACTIONS
» Both KlSte4p and KlGpa1p » TWO-HYBRID
required to induce mating in » KlSte4 = XXX
K.lactis
Do not reproduce without permission
Unsupervised Textmining
vs Manually Curated and Structured
Documents: Not necessarily a conflict
• Structured abs.
44 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
might be good
training sets
for mining
• Also, gateway
to mining
44 (c) Mark Gerstein, 2008
[Smith et al., Bioinformatics ('07)] Do not reproduce without permission
Impediment #2:
Access Restrictions
45 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
Inhibit Large-scale
Query
45 (c) Mark Gerstein, 2008
Do not reproduce without permission
Absence of social framework for
protecting "data" on the web
• Researchers unclear on framework
The ambiguity of the present copyright laws governing the
46 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
protection of databases creates a situation where researchers
are (practically) unclear about their rights to extract and
combine data
• Putting articles up on sites, "quoting" annotation
Likewise, researchers are unsure how to get "credit" for
combined data ("Mash ups")
46 (c) Mark Gerstein, 2008
• Disincentive to data integration
• Information owners, unsure of how laws safeguards
their information, overprotect their data with licenses
and technological mechanisms that impede
interoperation.
[Greenbaum & Gerstein, Nat. Biotech. ('03)]
Do not reproduce without permission
Technological safeguards
to "protect" data
• Limits on Bulk Downloads & • Databases can be stored in
Global Analysis propriety formats
Passwords and IP filtering Extreme is encryption
• allow the database owner • Watermarking adds overt or
47 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
to limit access to specific hidden digital fingerprints
users and computers
Slightly corrupting the data.
• selectively cut off access
Not that common in bio-DBs
to researchers performing
(but found in British Library).
bulk calculations.
Data can also be presented
piecemeal, in response to a
47 (c) Mark Gerstein, 2008
specific user query
Examples
• Incyte Proteome database
• Cellzome database of
interactions.
[Greenbaum & Gerstein, Nat. Biotech. ('03)]
Do not reproduce without permission
Free text Issue is Part
of this Larger Context
• Different traditions in academic publishing vs DB world
Genome sequence is free
but have to pay for article about it!
• Many free text initiatives
48 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
PubMedCentral.NIH.gov & arXiv.org
• Tricky economics of free text
potentially efficient
but redistributes dollars in world of academic publishing
who pays: readers or writers
48 (c) Mark Gerstein, 2008
[Greenbaum et al. (2003) Interdiscip Sci Rev 28: 293-302.] Do not reproduce without permission
Impediment #3:
Security
Considerations
49 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
Inhibit Large-scale
Query
49 (c) Mark Gerstein, 2008
Do not reproduce without permission
[Greenbaum et al., Nat. Biotech. ('04); Smith et al., GenomeBiol. ('05)
West" Internet
Vast Computer Security Costs in the "Wild
Do not reproduce without permission
50 (c) Mark Gerstein, 2008
50 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
Vast difficulty in securing information
servers in academia
• Mundane administration — patches
[Greenbaum et al., Nat. Biotech. ('04); Smith et al., GenomeBiol. ('05)
• Make building intricate systems for interoperation
51 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
difficult, as researchers have to continually check their
interfaces for "holes"
• Unique impact on research (vs business)
Free and broad dissemination of ideas between labs and
public is hallmark of research.
Preserving openness precludes standard security practices
51 (c) Mark Gerstein, 2008
often employed in a corporate or military environment -- e.g.
private networks
Academic computer users exhibit great variability, making
effective security procedures more difficult
Do not reproduce without permission
A Vision for Harnessing the Volume of Information
on the Web to Study the Structure of Science
• Main Applications of Large-scale • Impediments Large-
Mining scale Mining
New Scientific Discoveries (as Distributed Query)
(not disc. here) (Semi) Structuring
Understanding Areas of Study through the Information in
Simple Zipf Stats Journals
• Crystallography Nobel, Genomics, Overcoming access
Gene Naming restrictions
Maps of Science Security
(c) 2008
• Studying a genomics consortia, Considerations
Bigger Map, Ranking Journals
52 Gerstein.info/talks
Dynamics of Science
• Watching and modeling the
appearance of new terms, RNAi
ex.
Do not reproduce without permission
MS
53 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
MG
53 (c) Mark Gerstein, 2008
Acknowledgements
Acknowledgements
TopNet.GersteinLab.org
Do not reproduce without permission
Acknowledgements
TopNet.GersteinLab.org
MS
MG
Do not reproduce without permission
54 (c) Mark Gerstein, 2008
54 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
Acknowledgements
TopNet.GersteinLab.org
MS
MG
Do not reproduce without permission
55 (c) Mark Gerstein, 2008
55 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
NIH, NSF, Keck
MS
M Seringhaus
56 Gerstein.info/talks (c)2002, Yale, bioinfo.mbb.yale.edu
Job opportunities
currently for MG
postdocs & K Cheung D Greenbaum
students
M Schultz
56 (c) Mark Gerstein, 2008
G Montelione
S Douglas A Smith
Acknowledgements K Yip
TopNet.GersteinLab.org
P Cayting
Do not reproduce without permission
