Hedging, Fact from claim, Science as mythology

Hedging erosion as a mechanism for fact creation: a small discourse analysis of

the Bourne corpus

Anita de Waard Elsevier Labs/U Utrecht/UToronto

Single paper: Implication is hedged

(1) Both seminomas and the EC component of nonseminomas share features with ES cells.

(2) b. the detection of miR-371-3 merely reflects its expression pattern in ES cells,




(2) a. To exclude that




(2) c. we tested by RPA miR-302a-d, another ES cells-specific miRNA cluster (Suh et al, 2004).

(3) a. In many of the miR-371-3 expressing seminomas and nonseminomas, miR-302a-d was undetectable (Figs S7 and S8),

(2) a. To exclude that






(2) a. To exclude that (3) b. suggesting


(3) c. miR-371-3 expression is a selective event during tumorigenesis.












Concepts (present)








Concepts (present)

Transitions(2) a. To exclude that (3) b. suggesting







Concepts (present)

Experiment (past)

Transitions(2) a. To exclude that (3) b. suggesting


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Goal These results suggest that....Transitions

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Overlap with KEfEd

More papers: citation erodes hedging

Hypothesis

To investigate the possibility that miR-372 and miR-373 suppress the

expression of LATS2, we...


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Therefore, these results point toLATS2 as a mediator of the miR-372 and miR-373 effects on cell proliferation and tumorigenicity,


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Voorhoeve, 2006More papers: citation erodes hedging

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two miRNAs, miRNA-372 and-373, function as potential novel oncogenes in testicular germ

cell tumors by inhibition of LATS2 expression, which suggests that Lats2 is an important

tumor suppressor (Voorhoeve et al., 2006).

Raver-Shapira et.al, JMolCell 2007


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miR-372 and miR-373 target the Lats2 tumor suppressor (Voorhoeve et al., 2006)

Yabuta, JBioChem 2007


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miR-372 and miR-373 target the Lats2 tumor suppressor (Voorhoeve et al., 2006)

Yabuta, JBioChem 2007

Voorhoeve, 2006

http://elsatglabs.com/labs/anita/demos/LATSDemo102007/


http://elsatglabs.com/labs/anita/demos/LATSDemo102007/index.html

http://elsatglabs.com/labs/anita/demos/LATSDemo102007/index.html

“[Y]ou can transform a fact into fiction or a fiction into fact just by adding or subtracting references [and data]”

– Bruno Latour, ‘Science in Action’,1987

Kinnings et al, 2010: Abstract hedged by editorsAuthorʼs version Final version Changes Edit1.!We have developed a computational

approach that integrates structural bioinformatics, molecular modeling and systems biology to construct the protein-drug network on a structural proteome scale.

1.!We report a computational approach that integrates structural bioinformatics, molecular modelling and systems biology to construct a drug-target network on a structural proteome-wide scale.

have developedʼ -> ʻreportʼʻthe protein-drugʼ -> ʻa drug-targetʼProteome scale -> proteome-wide scale

HedgeContent

English

9. The Drugome-TB reveals not only that around one-third existing drugs show the potential to be repositioned to treat tuberculosis, but also that many currently unexploited M.tb proteins may be highly druggable and could therefore serve as novel antitubercular targets.

1.!The TB-drugome reveals that approximately one-third of the drugs examined have the potential to be repositioned to treat tuberculosis and that many currently unexploited M.tb receptors may be chemically druggable and could serve as novel anti-tubercular targets.

not only that aroundʼ -> ʻapproximatelyʼʻof existing drugsʼ -> ʻof the drugs examinedʼʻbut alsoʼ -> ʻandʼʻhighlyʼ -> ʻchemicallyʼʻthereforeʼ -> …

English

Hedge

EnglishHedgeEnglish

6. It supports that the drug-target network is inherently modular.

6.#Indeed, our results support the idea that drug-target networks are inherently modular and further that any observed randomness is mainly caused by biased target coverage.

It supports thatʼ -> ʻIndeed, our results support the idea that ʻ

Hedge/English

7.!The randomness observed in the network is mainly due to the false drug-target connection and the biased target coverage.

6.#Indeed, our results support the idea that drug-target networks are inherently modular and further that any observed randomness is mainly caused by biased target coverage. …. -> ʻOur results support

the idea that ʻʻdue toʼ -> ʻmainly caused byʼ

Hedge

Hedge

10.With the advance of structural genomics, the methodology is ready for other pathogen genomes.

8. More generally the methodology may be applied to other pathogens of interest with results improving as more of their structural proteomes are determined through the continued efforts of structural biology/genomics.

the methodology is ready for other pathogen genomesʼ -> ʻMore generally the methodology may be applied...ʻWith the advance of structural genomicsʼ -> ʻwith results improving...ʻmore ...are determined...ʼ

Hedge

Content

10 sentences 8 sentences Edits:14 Hedges 11 English corrections4 Content corrections

Edits:14 Hedges 11 English corrections4 Content corrections

Xie et al 2009: Claim gets validated in citationsXie L, Li J, Xie L, Bourne PE, 2009: Drug Discovery Using Chemical Systems Biology: Identification of the Protein-Ligand Binding Network To Explain the Side Effects of CETP Inhibitors. PLoS Comput Biol 5(5): e1000387. doi:10.1371/journal.pcbi.1000387

[Goal]: To understand the origins of these adverse drug reactions from Torcetrapib and other related drugs undergoing clinical trials, [Method]: we introduce a systematic strategy [Goal] to identify off-targets in the human structural proteome and [Goal]: investigate the roles of these off-targets in impacting human physiology and pathology [Method]: using biochemical pathway analysis. [Reg-Imp]: Our findings suggest that [Implication]: potential side-effects of a new drug can be identified at an early stage of the development cycle and [Implication]: [these can] be minimized by fine-tuning multiple off-target interactions. [Reg-Hyp]: The hope is that [Hypothesis]: this can reduce both the cost of drug development and the mortality rates during clinical trials.

2010: 28 articles citing this paper; 19 full-text papers found.

2009: Paper enters knowledge space

2009: Paper enters knowledge space1. Network analyses in systems pharmacology SI Berger… - Bioinformatics, 2009 - Oxford Univ Press

“Whereas originally medications were thought to hit a specific target and modulate effects through pathways downstream of that target, we now know that many drugs hit multiple targets, each of which exist within a complex network. The effects of the drug, both therapeutic actions and adverse events, are therefore a result of perturbation of the complex network landscape. This was suggested in a study by Xie et al. (2009) where structural homology between protein drug binding sites was used to predict potential off targets of Cholesteryl Ester Transfer Protein Inhibitors.”

http://bioinformatics.oxfordjournals.org/content/25/19/2466.full%22%20%5Ct%20%22_blank




2. A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug … L Xie, L Xie… - Bioinformatics, 2009 - OUP“Moreover, recently we have shown that such ligand–binding-site mapping can contribute to the design of pharmaceuticals through the detection of off-targets (Kinnings et al., 2009; Xie et al., 2009; Xie et al., 2007).”“Owing to scalability and robustness of SOIPPA and the EVD model, we have successfully applied SMAP to drug discovery, including elucidation of the molecular mechanisms of drug side-effects and repurposing of safe pharmaceuticals to target different pathways (Kinnings et al., 2009; Xie et al., 2009; Xie et al., 2007).““Using SMAP, we have successfully identified off-targets for several pharmaceuticals either already on the market or in clinical trials. In one case we have revealed a complex off-target-binding network for cholesteryl ester transporter protein (CETP) inhibitors (Xie et al., 2009). “



http://bioinformatics.oxfordjournals.org/content/25/12/i305.full%22%20%5Cl%20%22ref-77












2. A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug … L Xie, L Xie… - Bioinformatics, 2009 - OUP“Moreover, recently we have shown that such ligand–binding-site mapping can contribute to the design of pharmaceuticals through the detection of off-targets (Kinnings et al., 2009; Xie et al., 2009; Xie et al., 2007).”“Owing to scalability and robustness of SOIPPA and the EVD model, we have successfully applied SMAP to drug discovery, including elucidation of the molecular mechanisms of drug side-effects and repurposing of safe pharmaceuticals to target different pathways (Kinnings et al., 2009; Xie et al., 2009; Xie et al., 2007).““Using SMAP, we have successfully identified off-targets for several pharmaceuticals either already on the market or in clinical trials. In one case we have revealed a complex off-target-binding network for cholesteryl ester transporter protein (CETP) inhibitors (Xie et al., 2009). “9. Predicting drug side-effects by chemical systems biology NP Tatonetti, T Liu… - Genome biology, 2009“The recent work of Xie et al. [7] is another excellent example of the use of networks combining proteins and drugs. They investigated the reasons for the serious side-effects of torcetrapib, an inhibitor of cholesteryl ester transfer protein (CETP) that was in clinical trials as a preventive treatment for cardiovascular disease. The aim of torcetrapib was to raise the levels of the desirable high-density lipoprotein cholesterol (HDL-C), but torcetrapib turned out to have the side-effect of raising blood pressure, with potentially fatal effects in high-risk patients, and was withdrawn from development in 2006.“Xie et al. [7] generated off-target binding networks by comparing the structure of ligand-binding sites in all known protein structures. The proteins identified as having similar binding domains were ranked by a normalized docking score and clustered by their structural and functional characteristics into a gene network that includes metabolic and regulation pathways. Using this analysis, the authors identified possible off-targets for torcetrapib even though the binding site of CETP itself is not fully described. Perhaps most interestingly, they incorporated biological pathways into their off-target networks and found a potential explanation for the poorly understood effects of torcetrapib on blood pressure. By combining a simple gene regulation model with the predicted binding affinities to activators and inhibitors of the renin-angiotensin-aldosterone system (RAAS), they showed that torcetrapib caused more severe effects since it has a higher affinity for more RAAS activators.”













http://www.biomedcentral.com/content/pdf/gb-2009-10-9-238.pdf%22%20%5Ct%20%22_blank

http://www.biomedcentral.com/content/pdf/gb-2009-10-9-238.pdf%22%20%5Ct%20%22_blank

2010: Paper is validated, contrasted and expanded

2010: Paper is validated, contrasted and expanded16. A Structure-Based Approach for Mapping Adverse Drug Reactions to the Perturbation of Underlying

Biological Pathways Wallach, N Jaitly, R Lilien… - PloS one, 2010 “Recently, Xie et al. [9] have used virtual docking to study ADRs related to the cholesteryl ester transfer protein (CETP) and identified possible off-target interactions for a set of CETP inhibitors. Their method uses a known 3D protein structure of the primary target to characterize the binding site of the drugs. Then, it identifies potential off-targets by searching for other proteins having similar binding sites. Possible interactions between the resulting set of proteins and the drugs are then studied using virtual docking.”“In this work, we developed a computational framework for proposing associations between the ADRs of clinically approved drugs and the modulation of underlying biological pathways. In contrast to the work of Xie et al. [9] and Yang et al. [12], our model exploits the assumption that drugs capable of modulating similar pathways may have similar ADR profiles.”

http://dx.plos.org/10.1371/journal.pone.0012063%22%20%5Ct%20%22_blank




http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012063%22%20%5Cl%20%22pone.0012063-Xie1




http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012063%22%20%5Cl%20%22pone.0012063-Yang1




19. The Mycobacterium tuberculosis Drugome and Its Polypharmacological Implications SL Kinnings, L Xie, KH Fung, RM Jackson… - PLoS Computational …, 2010 “This strategy has been applied to study several selected drug targets, and proven, both computationally and experimentally, to be a useful tool in drug repositioning [15], side effect prediction [16], [17], and polypharmacological target discovery [18]. In this paper, we extend this methodology to the construction of a proteome-wide drug-target network. “











http://dx.plos.org/10.1371/journal.pcbi.1000976%22%20%5Ct%20%22_blank


http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000976%22%20%5Cl%20%22pcbi.1000976-Kinnings1


http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000976%22%20%5Cl%20%22pcbi.1000976-Xie2




24. PROMISCUOUS: a database for network-based drug-repositioning J von Eichborn, MS Murgueitio… - Nucleic Acids …, 2010 - Oxford Univ PressAs is the case with the aforementioned drug, until now most cases of drug repositioning are the result of serendipitous observations. […] Another example was that of Xie et al. (16) in explaining the off-target effects of the CETP inhibitor Torcetrapib that was taken out of phase III clinical trials after it was discovered to have significant side-effects. Their work revealed a complex network of interactions with up to twelve putative off-targets.












http://nar.oxfordjournals.org/content/early/2010/11/10/nar.gkq1037.full%22%20%5Ct%20%22_blank


http://nar.oxfordjournals.org/content/early/2010/11/10/nar.gkq1037.full%22%20%5Cl%20%22ref-16










24. PROMISCUOUS: a database for network-based drug-repositioning J von Eichborn, MS Murgueitio… - Nucleic Acids …, 2010 - Oxford Univ PressAs is the case with the aforementioned drug, until now most cases of drug repositioning are the result of serendipitous observations. […] Another example was that of Xie et al. (16) in explaining the off-target effects of the CETP inhibitor Torcetrapib that was taken out of phase III clinical trials after it was discovered to have significant side-effects. Their work revealed a complex network of interactions with up to twelve putative off-targets.

27. Cause-effect relationships in medicine: a protein network perspective AF Fliri, WT Loging… - Trends in Pharmacological Sciences, 2010 – Elsevier “Xie and coworkers used the similarity of cholesteryl ester transfer protein (CETP) ligand binding to identify off-target proteins, and used these proteins to generate protein networks in an attempt to rationalize side-effect differences among the CETP inhibitors torcetrapib, anacetrapib, and JTT-705 [89 L. Xie et al.], Drug discovery using chemical systems biology: Identification of the protein-ligand binding network to explain the side effects of CETP inhibitors, The latter, the most ‘promiscuous’ of the three, did not cause hypertension as a side effect whereas the more selective torcetrapib did. This result suggested that the adverse events of medicines may not necessarily be reduced by increasing the target selectivity of a drug but instead by ‘fine-tuning’ the pertinent cross-pathway communication. “
















http://linkinghub.elsevier.com/retrieve/pii/S0165614710001409%22%20%5Ct%20%22_blank

http://linkinghub.elsevier.com/retrieve/pii/S0165614710001409%22%20%5Ct%20%22_blank

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T1K-50X8PXY-1&_user=10&_coverDate=11/30/2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=34e2b0809c7f801201c5a0c6c69553ca&searchtype=a%22%20%5Cl%20%22bbib0445

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T1K-50X8PXY-1&_user=10&_coverDate=11/30/2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=34e2b0809c7f801201c5a0c6c69553ca&searchtype=a%22%20%5Cl%20%22bbib0445







Hedging rules fact creation

- Small case study, but nonetheless clear:

- Authors need to downplay claims to be acceptable in the literature

- After publication, hedging ‘erodes’, modality is dropped: claim becomes fact.

- Danger: experimental context is not taken into account when citing - claim gets extended without experimental validation

- So how do we develop systems that allow the context to stick to the claim?

A closer look at citations

time = t+1

Model of phenomenon

!

Model of phenomenon

time = t

?

Domain Model

(C) Ed Hovy


time = t+1

Model of phenomenon

!

Model of phenomenon

time = t

?

Domain Model

Citation: • ‘Lifts’ claim from previous paper and add it to current paper

• We need ‘rules for legal lifting’:• Express author’s claim truly• Maintain (experimental) content• Apply appropriately

(C) Ed Hovy


time = t+1

Model of phenomenon

!

Model of phenomenon

time = t

?

Domain Model

Citation: • ‘Lifts’ claim from previous paper and add it to current paper

• We need ‘rules for legal lifting’:• Express author’s claim truly• Maintain (experimental) content• Apply appropriately

What does this mean?

(C) Ed Hovy

The logical structure of a citation

(C) Ed Hovy


- The original author states - a Proposition P : about entities, relations, events, actions…

- an Interpretation of P IA(P):

- Holder = author A

- Topic = P

- Context = conditions in area/domain for which P

- Epistemic valence = {true, hypothesized, untrue, ...}

- Strength/certainty/intensity/probability = ...

- Provenance: date, paper, journal ...

(C) Ed Hovy


- The original author states - a Proposition P : about entities, relations, events, actions…

- an Interpretation of P IA(P):

- Holder = author A

- Topic = P

- Context = conditions in area/domain for which P

- Epistemic valence = {true, hypothesized, untrue, ...}

- Strength/certainty/intensity/probability = ...

- Provenance: date, paper, journal ...

- Citation: The citer’s interpretation of P IC(P) or of IA(P) IC(IA(P)):

- Holder = citer C

- Topic = P or IA(P) ... etc. (C) Ed Hovy

Rules for legal lifting

(C) Ed Hovy


- Citer must include Holder, Provenance, Topic, Valence,

“De Waard (1862) showed/believed that P”

- Citer may include own interpretation IC(IA(P)) too:

(C) Ed Hovy





“…but was [I believe] sadly mistaken”

(C) Ed Hovy






- Citer may include Context, but must make certain that it applies in the context described in the current paper … This is often not done

(C) Ed Hovy







- Citer should include the most canonical reference

(C) Ed Hovy








- Citer really should include all relevant references

(C) Ed Hovy








- Citer really should include all relevant references

Citation manage-

ment tools can

assist with all of this

(C) Ed Hovy

‘Referring Use of Tense’

Argumentation:

Instantaneous present/To-inf

Experiment Event past

Concepts‘Gnomic’ present

Discourse progressionInstantaneous

present

Other/Past WorkPresent

Future WorkFuture

Anecdotal excursion into mythological text:

Facts in the eternal present:

Endogenous small RNAs (miRNAs) regulate gene expression by mechanisms conserved across metazoans.

I sing of golden-throned Hera whom Rhea bare. Queen of the immortals is she, surpassing all in beauty: she is the sister and the wife of loud-thundering Zeus, --the glorious one whom all the blessed throughout high Olympus reverence and honor.

Events in the simple past:

Vehicle-treated animals spent equivalent time investigating a juvenile in the first and second sessions in experiments conducted in the NAC and the striatum: T1 values were 122 ± 6 s and 114 ± 5 s.

Now the wooers turned to the dance and to gladsome song, and made them merry, and waited till evening should come; and as they made merry dark evening came upon them.

Events with embedded facts:

We also generated BJ/ET cells expressing the RASV12-ERTAM chimera gene, which is only active when tamoxifen is added (De Vita et al, 2005).

And she took her mighty spear, tipped with sharp bronze, heavy and huge and strong, wherewith she vanquishes the ranks of men-of warriors, with whom she is wroth, she, the daughter of the mighty sire.

Attribution in the present perfect:

miRNAs have emerged as important regulators of development and control processes such as cell fate determination and cell death (Abrahante et al., 2003, Brennecke et al., 2003, Chang et al., 2004, Chen et al., 2004, Johnston and Hobert, 2003, Lee et al., 1993, ...

In this book I have had old stories written down, as I have heard them told by intelligent people, concerning chiefs who have held dominion in the northern countries, and who spoke the Danish tongue; and also concerning some of their family branches, according to what has been told me.

Implications are hedged, & present tense + that:

These results indicate that although miR-372&3 confer complete protection to oncogene-induced senescence in a manner similar to p53 inactivation, the cellular response to DNA damage remains intact.

Now it is said that ever since then whenever the camel sees a place where ashes have been scattered, he wants to get revenge with his enemy the rat and stomps and rolls in the ashes hoping to get the rat.

Anecdotal excursion into mythological text:

Trees of representation

Argumentation:

present

Experiment Past

Concepts‘Present

Discourse

Present

Other/Past

Present

Future WorkFuture

Trees of representation

Hedging, Fact from claim, Science as mythology

Technology

Transcript of Hedging, Fact from claim, Science as mythology