cancer detection.pptx

8/20/2019 cancer detection.pptx

http://slidepdf.com/reader/full/cancer-detectionpptx 1/38

BY : GIERMIN SAHAGUN11337710



This example demons!aes"sin# a ne"!al ne$o!% odee& &an&e! '!om mass

spe&!ome!( daa on p!oien



+ha is &an&e! ,

• Is a e!m "sed 'o!diseases in $hi&ha-no!mal &ells di.ide

$iho" &on!ol anda!e a-le o in.adeohe! iss"es*

• /an&e! &ells &an

sp!ead o ohe! pa!so' he -od( h!o"#hhe -loodand l(mph s(sems



The Impo!an&e o' /an&e!ee&ion

• Cancer is one of the leading cause of deathworldwide. As of 2013 about It causedabout 8.2 million deaths or 14.6 of allhuman deaths. And detecting cancer in anearlier state can lead to a higher chance of

being cured and increase of once life s!an .



Random 'a&s a-o"&an&e!

• "he ma#orit$ of cancer sur%i%ors &64' werediagnosed ( or more $ears ago.

• )earl$ half &46' of cancer sur%i%ors are *0$ears of age or older

• "obacco use is the cause of about 22 ofcancer deaths.

• Another 10 is due to obesit$+ a !oor diet+ lac,

of !h$sical acti%it$+ and drin,ing alcohol.• -ther factors include certain infections+

e!osure to ioni/ing radiation+ anden%ironmental !ollutants.



In!od"&io

n

• Se!"m p!oeomi& pae!n dia#nosi&s&an -e "sed o di2e!eniae samples'!om paiens $ih and $iho"

disease* !o)le pae!ns a!e#ene!aed "sin# s"!'a&e4enhan&edlase! deso!pion and ioni5aion6SEI8 p!oein mass spe&!ome!(*

• This e&hnolo#( has he poenial oimp!o.e &lini&al dia#nosi&s ess 'o!&an&e! paholo#ies*



The !o-lem: /an&e! ee&ion

• "he goal is to build a classier that candistinguish between cancer and control!atients from the mass s!ectrometr$ data.

• "he methodolog$ followed in this eam!leis to select a reduced set of measurementsor features that can be used todistinguish between cancer and control

!atients using a classier.• "hese features will be ion intensit$ le%els at

s!ecic masscharge %alues.



9o!mai

n# he

aa



• "o recreate the datain o.a!iandaase*ma used in this eam!le+download and uncom!ress the raw masss!ectrometr$ data from the 5A)CI web site.

Create the datale ;.a!ian/an&e!<A</daase*ma b$either running scri!t msse=p!o&essin# inioinformatics "oolbo &"7' or b$ following theste!s in the eam!le-iodis&ompdemo &atch

!rocessing with !arallel com!uting'. "he new lecontains %ariables + 79 and gr!.



• Ea&h &ol"mn in Y !ep!esensmeas"!emens a%en '!om apaien* The!e

a!e >1? &ol"mnsin Y !ep!esenin# >1?paiens@ o" o' $hi&h 1>1 a!e

o.a!ian &an&e! paiens



• Ea&h !o$ in Y !ep!esens he ioninensi( le.el a a spe&i)& mass4&ha!#e .al"e indi&aed in MC* The!e

a!e 1000mass4&ha!#e .al"esin MC and ea&h !o$ in Y !ep!esenshe ion4inesi( le.els o' he paiensa ha pa!i&"la! mass4&ha!#e .al"e*



• The .a!ia-le #!p holds he

index in'o!maion as o $hi&ho' hese samples !ep!esen&an&e! paiens and $hi&h

ones !ep!esen no!mal



Ran%in#

De(9ea"!es



Ran%in# De(9ea"!es

• "his is a t$!ical classication!roblem in which the number offeatures is much larger than the

number of obser%ations+ but in whichno single feature achie%es a correctclassication+ therefore we need to

nd a classier which a!!ro!riatel$learns how to weight multi!lefeatures and at the same time

!roduce a generali/ed ma!!ing



• A sim!le a!!roach for nding signicantfeatures is to assume that each 79 %alueis inde!endent and com!ute a twowa$ t

test. !an%'ea"!es returns an inde tothe most signicant 79 %alues+ forinstance 100 indices ran,ed b$ the

absolute %alue of the test statistic.



• "o nish recreating the datafrom o.a!iandaase*ma+ loadthe ;.a!ian/an&e!<A</daase*ma

and!an%'ea"!es from ioinformatics "oolbo to choose 100 highest ran,edmeasurements as in!uts .

ind :ran,features&+gr!+;C<I"=<I-);+;ttest;+;)>7=<;+100'? : &ind+@'?

e)ne he a!#es 'o! he $o &lassesas 'ollo$s:

t : double&strcm!&;Cancer;+gr!''? t : t? 1tB?



The p!ep!o&essin# seps '!om hes&!ip and example lised a-o.ea!e inended o demons!ae a

!ep!esenai.e se o' possi-lep!e4p!o&essin# and 'ea"!esele&ion p!o&ed"!es* Usin#

di2e!en seps o! pa!amee!sma( lead o di2e!en and

possi-l( imp!o.ed !es"ls o' his

example*



/lassi)&aion Usin# a

9eed 9o!$a!d Ne"!alNe$o!%

• )ow that $ou ha%e identied some

signicant features+ $ou can use thisinformation to classif$ the cancerand normal sam!les.



setdemorandstream&6*2880(1'

• Dince the neural networ, is initiali/edwith random initial weights+ the

results after training the networ,%ar$ slightl$ e%er$ time the eam!leis run. "o a%oid this randomness+ the

random seed is set to re!roduce thesame results e%er$ time. Eowe%erthis is not necessar$ for $our owna!!lications.



• A 1hidden la$er feed forward neuralnetwor, with ( hidden la$er neurons is

created and trained.• "he in!ut and target sam!les are

automaticall$ di%ided into training+%alidation and test sets. "he training set isused to teach the networ,.

• "raining continues as long as the networ,continues im!ro%ing on the %alidation set.

• "he test set !ro%ides a com!letel$inde!endent measure of networ, accurac$.



ne pae!nne68F

.ie$6ne8

• The inp" and o"p" ha.e si5eso' 0 -e&a"se he ne$o!% has no(e -een &on)#"!ed o ma&h o"!

inp" and a!#e daa* This $illhappen $hen he ne$o!% is!ained*



• No$ he ne$o!% is !ead( o -e !ained*The samples a!e a"omai&all( di.ided ino!ainin#@ .alidaion and es ses*

• The !ainin# se is "sed o ea&h hene$o!%* T!ainin# &onin"es as lon# as hene$o!% &onin"es imp!o.in# on he.alidaion se*

• The es se p!o.ides a &ompleel(independen meas"!e o' ne$o!% a&&"!a&(*

• The NN T!ainin# Tool sho$s he ne$o!%-ein# !ained and he al#o!ihms "sed o!ain i*

• I also displa(s he !ainin# sae d"!in#!ainin# and he &!ie!ia $hi&h sopped!ainin# $ill -e hi#hli#hed in #!een*



net+trB : train&net++t'?

The -"ons a he -oom open"se'"l plos $hi&h &an -e openedd"!in# and a'e! !ainin#* in%s

nex o he al#o!ihm names andplo -"ons open

do&"menaion on hoses"-e&s*



!lot!erform&tr'

• "o see how the networ,;s !erformanceim!ro%ed during training+ either clic, theFerformance button in the training tool+ or

call FG-"F=<-<7.• Ferformance is measured in terms of mean

sHuared error+ and shown in log scale. It ra!idl$decreased as the networ, was trained.

• Ferformance is shown for each of the training+%alidation and test sets. "he %ersion of thenetwor, that did best on the %alidation set iswas after training.



• "he trained neural networ, can now betested with the testing sam!les we!artitioned from the main dataset.

• "he testing data was not used in training inan$ wa$ and hence !ro%ides an outof

sam!le dataset to test the networ, on.• "his will gi%e us a sense of how well the

networ, will do when tested with data fromthe real world.



test : &@+tr.testInd'? test" :

t&@+tr.testInd'? test : net&test'?testClasses : test J 0.(

The ne$o!% o"p"s $ill -e in

he !an#e 0 o 1@ so $eh!eshold hem o #e 1s and

0s indi&ain# &an&e! o!

no!mal paiens !espe&i.el(*



• -ne measure of how well the neural networ, has t thedata is the confusion !lot. Eere the confusion matri is!lotted across all sam!les.

• "he confusion matri shows the !ercentages of correct andincorrect classications. Correct classications are thegreen sHuares on the matrices diagonal.

• Incorrect classications form the red sHuares.

• If the networ, has learned to classif$ !ro!erl$+ the!ercentages in the red sHuares should be %er$ small+indicating few misclassications.

• If this is not the case then further training+ or training anetwor, with more hidden neurons+ would be ad%isable.

!lotconfusion&test"+test'



• Another measure of how well the neural

network has fit data is the receiver operating

characteristic plot.• This shows how the false positive and true

positive rates relate as the thresholding of

outputs is varied from 0 to 1.• The farther left and up the line is, the fewer

false positives need to be accepted in order to

get a high true positive rate.

• The best classifiers will have a line going

from the bottom left corner, to the top left

corner, to the top right corner, or close to that.



• This example ill"s!aed ho$ne"!al ne$o!%s &an -e "sedas &lassi)e!s 'o! &an&e!

dee&ion*• ;ne &an also expe!imen

"sin# e&hni="es li%e

p!in&ipal &omponen anal(siso !ed"&e he dimensionali(o' he daa o -e "sed 'o!

-"ildin# ne"!al ne$o!%s o



Re'e!en&es

• 1B ".F. Conrads+ et al.+ Eighresolutionserum !roteomic features for o%ariandetection+ =ndocrine<elated Cancer+ 11+

2004+ !!. 1631*8.• 2B =.. Fetricoin+ et al.+ >se of !roteomic

!atterns in serum to identif$ o%ariancancer+ Gancet+ 3(&306'+ 2002+ !!. (*2

(**.

cancer detection.pptx

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