Gaëtan Caillaut
commited on
Commit
•
76834b8
1
Parent(s):
85d463c
move word features in python code
Browse files- pubmed-word-features.txt +0 -501
- pubmed.py +5 -8
pubmed-word-features.txt
DELETED
@@ -1,501 +0,0 @@
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w-rat
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w-common
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w-use
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w-examin
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w-pathogenesi
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w-retinopathi
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w-mous
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w-studi
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w-anim
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w-model
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w-metabol
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w-abnorm
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w-contribut
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w-develop
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w-investig
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w-mice
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w-2
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w-month
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w-compar
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w-obtain
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w-method
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w-induc
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w-6
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w-inject
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w-experiment
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w-normal
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w-diet
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w-30
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w-hyperglycemia
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w-level
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w-lipid
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w-oxid
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w-activ
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w-protein
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w-kinas
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w-c
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w-measur
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w-result
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w-increas
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w-retin
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w-stress
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w-3
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w-similar
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w-observ
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w-conclus
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w-play
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w-import
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w-role
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w-present
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w-p
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w-m
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w-r
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w-muscl
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w-control
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w-chang
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w-dure
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w-lower
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w-higher
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w-mass
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w-correl
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w-decreas
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w-determin
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w-concentr
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w-stimul
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w-period
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w-caus
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w-mark
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w-group
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w-evid
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w-fast
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w-type
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w-signific
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w-differ
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w-ratio
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w-suggest
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w-degre
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w-occur
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w-vivo
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w-respect
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w-dysfunct
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w-region
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w-high
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w-appear
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w-sever
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w-affect
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w-cardiovascular
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w-complic
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w-primari
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w-death
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w-patient
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w-clinic
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w-suscept
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w-cardiac
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w-tissu
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w-specif
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w-function
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w-defect
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w-possibl
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w-indic
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w-state
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w-onli
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w-bodi
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w-weight
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w-loss
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w-valu
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w-howev
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w-4
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w-condit
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w-durat
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w-8
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w-week
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w-onset
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w-data
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w-direct
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w-report
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w-provid
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w-addit
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w-evalu
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w-sensit
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w-heart
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w-object
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w-mean
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w-blood
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w-glucos
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w-strong
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w-hba
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w-1c
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w-a1c
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w-variabl
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w-independ
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w-assess
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w-relat
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w-trial
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w-research
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w-design
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w-profil
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w-sampl
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w-particip
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w-n
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w-1
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w-consist
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w-befor
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w-min
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w-predict
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w-adjust
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w-sex
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w-treatment
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w-7
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w-gt
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w-0
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w-larg
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w-influenc
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w-base
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w-standard
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w-14
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w-10
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w-wherea
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w-enhanc
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w-manag
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w-day
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w-secret
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w-cholesterol
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w-insulin
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w-24
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w-h
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w-low
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w-rate
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w-fatti
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w-acid
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w-effect
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w-hormon
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w-hepat
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w-contrast
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w-product
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w-major
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w-plasma
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w-current
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w-flow
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w-chronic
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w-mechan
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w-test
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w-therefor
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w-analys
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w-mrna
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w-streptozotocin
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w-did
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w-15
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w-g
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w-25
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w-mmol
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w-l
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w-5
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w-reduc
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w-number
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w-densiti
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w-posit
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w-cell
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w-17
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w-mm
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w-18
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w-induct
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w-associ
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w-express
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w-glycem
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w-respons
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w-therapi
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w-random
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w-initi
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w-ani
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w-singl
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w-new
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w-agent
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w-metformin
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w-medic
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w-glycosyl
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w-hemoglobin
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w-analysi
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w-baselin
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w-health
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w-factor
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w-process
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w-care
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w-9
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w-01
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w-95
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w-interv
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w-ci
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w-12
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w-reduct
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w-achiev
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w-target
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w-lt
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w-diseas
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w-class
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w-age
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w-obes
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w-renal
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w-improv
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w-progress
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w-noninsulindepend
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w-mellitus
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w-becaus
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w-s
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w-index
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w-hypertens
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w-need
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w-followup
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w-year
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w-mg
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w-dl
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w-remain
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w-subject
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w-treat
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w-oral
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w-requir
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w-0001
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w-mortal
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w-includ
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w-vs
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w-background
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w-poor
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w-drug
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w-13
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w-rang
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w-combin
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w-intervent
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w-daili
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w-dose
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w-100
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w-toler
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w-receiv
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w-11
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w-postprandi
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w-kg
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w-hypoglycemia
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w-frequent
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w-event
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w-versus
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w-symptom
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w-incid
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w-parent
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w-complex
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w-longterm
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w-inhibitor
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w-peripher
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w-nerv
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w-stz
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w-conduct
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w-demonstr
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w-frequenc
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w-inhibit
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w-neuropathi
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w-pathway
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w-shown
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w-time
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w-ii
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w-individu
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w-adult
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w-50
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w-60
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w-diagnosi
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w-healthi
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w-follow
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w-young
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w-seen
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w-alter
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w-gene
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w-e
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w-identifi
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w-previous
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w-mediat
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w-vascular
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w-lipoprotein
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w-involv
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w-phenotyp
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w-confirm
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w-variant
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w-endotheli
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w-potenti
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w-disord
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w-popul
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w-nonobes
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w-aim
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w-serum
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w-hba1c
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w-hypoglycaemia
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w-continu
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w-case
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w-impair
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w-risk
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w-known
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w-men
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w-women
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w-40
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w-complet
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w-estim
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w-like
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w-particular
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w-human
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w-character
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w-elev
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w-synthesi
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w-greater
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w-small
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w-reveal
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w-liver
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w-niddm
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w-genet
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w-receptor
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w-growth
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w-pancreat
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w-betacel
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w-molecul
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w-enzym
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w-regul
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w-polymorph
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w-total
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w-allel
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w-02
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w-resist
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w-cpeptid
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w-hypothesi
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w-perform
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w-score
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w-001
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w-05
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w-histori
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w-action
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w-approxim
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w-suppress
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w-glucagon
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w-ml
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w-x
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w-free
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w-peopl
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w-uptak
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w-intens
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w-relationship
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w-prevent
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w-autoimmun
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w-recent
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w-preval
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w-nondiabet
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w-genotyp
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w-conclud
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w-linkag
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w-islet
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w-peptid
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w-form
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w-membran
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w-transgen
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w-failur
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w-isol
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w-negat
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w-earli
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w-famili
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w-chromosom
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w-immun
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w-support
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w-16
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w-cohort
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w-insulindepend
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w-outcom
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w-screen
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w-approach
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w-infus
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w-multipl
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w-depend
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w-physic
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w-transport
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w-acut
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w-releas
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w-presenc
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w-glycaem
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w-male
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w-antibodi
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w-femal
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w-pattern
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w-t2dm
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w-promot
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w-fat
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w-d
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w-bmi
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w-haplotyp
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w-triglycerid
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w-interact
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w-marker
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w-describ
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w-area
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w-20
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w-cytokin
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w-bind
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w-bb
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w-alpha
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w-beta
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w-cd4
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w-spontan
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w-given
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w-vitro
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w-basal
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w-protect
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w-pressur
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w-detect
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w-exercis
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w-children
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w-adolesc
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w-life
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w-b
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w-antigen
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w-iddm
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w-american
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w-hla
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w-arteri
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w-nephropathi
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w-review
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w-destruct
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w-content
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w-autoantibodi
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w-dm
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w-select
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w-infect
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w-recipi
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w-intak
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w-placebo
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w-db
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w-pancrea
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w-diagnos
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w-glomerular
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w-albumin
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w-excret
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w-syndrom
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w-t
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w-lymphocyt
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w-produc
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w-coronari
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w-status
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w-microalbuminuria
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w-nod
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w-mhc
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w-insul
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w-administr
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w-revers
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w-transplant
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w-graft
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w-t1d
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w-lead
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w-v
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w-dietari
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w-general
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w-macrophag
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w-kidney
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w-urinari
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w-myocardi
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w-meal
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w-ica
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w-locus
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w-tcell
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w-depress
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w-bone
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w-mutat
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pubmed.py
CHANGED
@@ -52,6 +52,9 @@ _CLASS_LABELS = [
|
|
52 |
"Diabetes Mellitus Type 2"
|
53 |
]
|
54 |
|
|
|
|
|
|
|
55 |
|
56 |
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
|
57 |
class PubmedDataset(datasets.GeneratorBasedBuilder):
|
@@ -80,12 +83,9 @@ class PubmedDataset(datasets.GeneratorBasedBuilder):
|
|
80 |
|
81 |
def _info(self):
|
82 |
if self.config.name == "nodes":
|
83 |
-
with open("pubmed-word-features.txt", "rt", encoding="UTF-8") as f:
|
84 |
-
word_features = f.read().split("\n")
|
85 |
-
|
86 |
features_dict = {
|
87 |
w: datasets.Value("float32")
|
88 |
-
for w in
|
89 |
}
|
90 |
features_dict["node"] = datasets.Value("string")
|
91 |
features_dict["label"] = datasets.ClassLabel(names=_CLASS_LABELS)
|
@@ -165,9 +165,6 @@ class PubmedDataset(datasets.GeneratorBasedBuilder):
|
|
165 |
neighbors[n] = []
|
166 |
neighbors[src].append(target)
|
167 |
|
168 |
-
with open("pubmed-word-features.txt", "rt", encoding="UTF-8") as f:
|
169 |
-
word_features = f.read().split("\n")
|
170 |
-
|
171 |
def _word_feature_tuple(x):
|
172 |
w, v = x.split("=")
|
173 |
return (w, float(v))
|
@@ -184,7 +181,7 @@ class PubmedDataset(datasets.GeneratorBasedBuilder):
|
|
184 |
w_features = dict(map(_word_feature_tuple, row[2:-1]))
|
185 |
features = {"node": node, "label": label,
|
186 |
"neighbors": neighbors[node]}
|
187 |
-
for x in
|
188 |
features[x] = w_features.get(x, 0.0)
|
189 |
yield id, features
|
190 |
|
|
|
52 |
"Diabetes Mellitus Type 2"
|
53 |
]
|
54 |
|
55 |
+
_WORD_FEATURES = ["w-rat", "w-common", "w-use", "w-examin", "w-pathogenesi", "w-retinopathi", "w-mous", "w-studi", "w-anim", "w-model", "w-metabol", "w-abnorm", "w-contribut", "w-develop", "w-investig", "w-mice", "w-2", "w-month", "w-compar", "w-obtain", "w-method", "w-induc", "w-6", "w-inject", "w-experiment", "w-normal", "w-diet", "w-30", "w-hyperglycemia", "w-level", "w-lipid", "w-oxid", "w-activ", "w-protein", "w-kinas", "w-c", "w-measur", "w-result", "w-increas", "w-retin", "w-stress", "w-3", "w-similar", "w-observ", "w-conclus", "w-play", "w-import", "w-role", "w-present", "w-p", "w-m", "w-r", "w-muscl", "w-control", "w-chang", "w-dure", "w-lower", "w-higher", "w-mass", "w-correl", "w-decreas", "w-determin", "w-concentr", "w-stimul", "w-period", "w-caus", "w-mark", "w-group", "w-evid", "w-fast", "w-type", "w-signific", "w-differ", "w-ratio", "w-suggest", "w-degre", "w-occur", "w-vivo", "w-respect", "w-dysfunct", "w-region", "w-high", "w-appear", "w-sever", "w-affect", "w-cardiovascular", "w-complic", "w-primari", "w-death", "w-patient", "w-clinic", "w-suscept", "w-cardiac", "w-tissu", "w-specif", "w-function", "w-defect", "w-possibl", "w-indic", "w-state", "w-onli", "w-bodi", "w-weight", "w-loss", "w-valu", "w-howev", "w-4", "w-condit", "w-durat", "w-8", "w-week", "w-onset", "w-data", "w-direct", "w-report", "w-provid", "w-addit", "w-evalu", "w-sensit", "w-heart", "w-object", "w-mean", "w-blood", "w-glucos", "w-strong", "w-hba", "w-1c", "w-a1c", "w-variabl", "w-independ", "w-assess", "w-relat", "w-trial", "w-research", "w-design", "w-profil", "w-sampl", "w-particip", "w-n", "w-1", "w-consist", "w-befor", "w-min", "w-predict", "w-adjust", "w-sex", "w-treatment", "w-7", "w-gt", "w-0", "w-larg", "w-influenc", "w-base", "w-standard", "w-14", "w-10", "w-wherea", "w-enhanc", "w-manag", "w-day", "w-secret", "w-cholesterol", "w-insulin", "w-24", "w-h", "w-low", "w-rate", "w-fatti", "w-acid", "w-effect", "w-hormon", "w-hepat", "w-contrast", "w-product", "w-major", "w-plasma", "w-current", "w-flow", "w-chronic", "w-mechan", "w-test", "w-therefor", "w-analys", "w-mrna", "w-streptozotocin", "w-did", "w-15", "w-g", "w-25", "w-mmol", "w-l", "w-5", "w-reduc", "w-number", "w-densiti", "w-posit", "w-cell", "w-17", "w-mm", "w-18", "w-induct", "w-associ", "w-express", "w-glycem", "w-respons", "w-therapi", "w-random", "w-initi", "w-ani", "w-singl", "w-new", "w-agent", "w-metformin", "w-medic", "w-glycosyl", "w-hemoglobin", "w-analysi", "w-baselin", "w-health", "w-factor", "w-process", "w-care", "w-9", "w-01", "w-95", "w-interv", "w-ci", "w-12", "w-reduct", "w-achiev", "w-target", "w-lt", "w-diseas", "w-class", "w-age", "w-obes", "w-renal", "w-improv", "w-progress", "w-noninsulindepend", "w-mellitus", "w-becaus", "w-s", "w-index", "w-hypertens", "w-need", "w-followup", "w-year", "w-mg", "w-dl", "w-remain", "w-subject", "w-treat", "w-oral", "w-requir", "w-0001", "w-mortal", "w-includ", "w-vs", "w-background", "w-poor", "w-drug", "w-13", "w-rang", "w-combin", "w-intervent", "w-daili", "w-dose", "w-100", "w-toler", "w-receiv", "w-11", "w-postprandi", "w-kg", "w-hypoglycemia", "w-frequent", "w-event", "w-versus", "w-symptom", "w-incid", "w-parent", "w-complex", "w-longterm", "w-inhibitor", "w-peripher", "w-nerv", "w-stz", "w-conduct", "w-demonstr", "w-frequenc", "w-inhibit", "w-neuropathi", "w-pathway", "w-shown", "w-time", "w-ii", "w-individu", "w-adult", "w-50", "w-60", "w-diagnosi", "w-healthi", "w-follow", "w-young", "w-seen", "w-alter", "w-gene", "w-e", "w-identifi", "w-previous", "w-mediat", "w-vascular", "w-lipoprotein", "w-involv", "w-phenotyp", "w-confirm", "w-variant", "w-endotheli", "w-potenti", "w-disord", "w-popul", "w-nonobes", "w-aim", "w-serum", "w-hba1c", "w-hypoglycaemia", "w-continu", "w-case", "w-impair", "w-risk", "w-known", "w-men", "w-women", "w-40", "w-complet", "w-estim", "w-like", "w-particular", "w-human", "w-character", "w-elev", "w-synthesi", "w-greater", "w-small", "w-reveal", "w-liver", "w-niddm", "w-genet", "w-receptor", "w-growth", "w-pancreat", "w-betacel", "w-molecul", "w-enzym", "w-regul", "w-polymorph", "w-total", "w-allel", "w-02", "w-resist", "w-cpeptid", "w-hypothesi", "w-perform", "w-score", "w-001", "w-05", "w-histori", "w-action", "w-approxim", "w-suppress", "w-glucagon", "w-ml", "w-x", "w-free", "w-peopl", "w-uptak", "w-intens", "w-relationship", "w-prevent", "w-autoimmun", "w-recent", "w-preval", "w-nondiabet", "w-genotyp", "w-conclud", "w-linkag", "w-islet", "w-peptid", "w-form", "w-membran", "w-transgen", "w-failur", "w-isol", "w-negat", "w-earli", "w-famili", "w-chromosom", "w-immun", "w-support", "w-16", "w-cohort", "w-insulindepend", "w-outcom", "w-screen", "w-approach", "w-infus", "w-multipl", "w-depend", "w-physic", "w-transport", "w-acut", "w-releas", "w-presenc", "w-glycaem", "w-male", "w-antibodi", "w-femal", "w-pattern", "w-t2dm", "w-promot", "w-fat", "w-d", "w-bmi", "w-haplotyp", "w-triglycerid", "w-interact", "w-marker", "w-describ", "w-area", "w-20", "w-cytokin", "w-bind", "w-bb", "w-alpha", "w-beta", "w-cd4", "w-spontan", "w-given", "w-vitro", "w-basal", "w-protect", "w-pressur", "w-detect", "w-exercis", "w-children", "w-adolesc", "w-life", "w-b", "w-antigen", "w-iddm", "w-american", "w-hla", "w-arteri", "w-nephropathi", "w-review", "w-destruct", "w-content", "w-autoantibodi", "w-dm", "w-select", "w-infect", "w-recipi", "w-intak", "w-placebo", "w-db", "w-pancrea", "w-diagnos", "w-glomerular", "w-albumin", "w-excret", "w-syndrom", "w-t", "w-lymphocyt", "w-produc", "w-coronari", "w-status", "w-microalbuminuria", "w-nod", "w-mhc", "w-insul", "w-administr", "w-revers", "w-transplant", "w-graft", "w-t1d", "w-lead", "w-v", "w-dietari", "w-general", "w-macrophag", "w-kidney", "w-urinari", "w-myocardi", "w-meal", "w-ica", "w-locus", "w-tcell", "w-depress", "w-bone", "w-mutat"
|
56 |
+
]
|
57 |
+
|
58 |
|
59 |
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
|
60 |
class PubmedDataset(datasets.GeneratorBasedBuilder):
|
|
|
83 |
|
84 |
def _info(self):
|
85 |
if self.config.name == "nodes":
|
|
|
|
|
|
|
86 |
features_dict = {
|
87 |
w: datasets.Value("float32")
|
88 |
+
for w in _WORD_FEATURES
|
89 |
}
|
90 |
features_dict["node"] = datasets.Value("string")
|
91 |
features_dict["label"] = datasets.ClassLabel(names=_CLASS_LABELS)
|
|
|
165 |
neighbors[n] = []
|
166 |
neighbors[src].append(target)
|
167 |
|
|
|
|
|
|
|
168 |
def _word_feature_tuple(x):
|
169 |
w, v = x.split("=")
|
170 |
return (w, float(v))
|
|
|
181 |
w_features = dict(map(_word_feature_tuple, row[2:-1]))
|
182 |
features = {"node": node, "label": label,
|
183 |
"neighbors": neighbors[node]}
|
184 |
+
for x in _WORD_FEATURES:
|
185 |
features[x] = w_features.get(x, 0.0)
|
186 |
yield id, features
|
187 |
|