A solution to the Curse of Dimensionality Problem in Pairwise Scoring Techniques

Transcription

1 A soluton to the Curse of Dmensonalty Problem n Parwse orng Tehnques Man Wa MAK Dept. of Eletron and Informaton Engneerng The Hong Kong Polytehn Unversty un Yuan KUNG Dept. of Eletral Engneerng Prneton Unversty ICONIP'06 1

2 Outlne Proten equenes and ubellular Loalzaton Parwse orng Kernels Feature eleton Results and Conlusons ICONIP'06 2

3 Cells The human body ontans many dfferent organs wth eah organ performng a dfferent funton. Cells also have a set of "lttle organs" alled organelles that are adapted and/or spealzed for arryng out one or more vtal funtons. Pture from 1 Nuleolus 2 Nuleus 3 Rbosome 4 Vesle 5 Rough endoplasm retulum ER 6 Golg apparatus 7 Cytoskeleton 8 mooth ER 9 Mtohondra 10 Vauole 11 Cytoplasm 12 Lysosome 13 Centroles ICONIP'06 3

4 Cell and Proten equenes A proten onssts of a sequene of amno ads Amno ad sequene of a proten ontans nformaton about ts subellular loaton Pture was extrated from ICONIP'06 4

5 Proten equenes Protens are represented by sequenes of 20 alphabets amno ad. The funton and subellular loatons of protens an be predted by lookng at ther orrespondng sequenes. MITILEKIAIEEMARTQ KNKATAHLGLLKANVA KLRRELIPKGGGGGTG EAGFEVAKTGDARVGF VIEHVLNDEDVVQIVKKV. Proten Funton/ ubellular Loaton Predtor ICONIP'06 5

6 Feature Extraton Beause most lassfers work on numbers nstead of strngs we need to onvert sequenes to numbers or vetors. Ths an be solved by kernel methods j j 123 trng spae KNKATAHLGLLKANK KAKATLHLGLLKANK KNKATAHLALLKANK ICONIP'06 6 K Parwse smlarty sores

7 Feature Extraton by equene Algnment Idea: Gven a query sequene we algn t aganst a set of sequenes wth known subellular loatons to nfer ts loaton. j gves the algnment sore of sequenes seq and j j K K N N K K A A T A A A H H L L G H L L L L K K A NK NK seq j Penalty Appled ICONIP'06 7

8 Feature Extraton by Profle Algnment The senstvty of detetng remote homolog an be mproved by replang sequene algnment omparng amno-ad resdues wth profle algnment. Tranng equenes KNKATK j KAKATK WIPROT Database Query Algned sequenes PI-BLAT Ψ Ψ : P P 20 j P 20 Profle Profle Algnment Profle Ψ Ψ ICONIP'06 8 pro j

9 K pro Feature Extraton by Profle Algnment Profle Algnment kernel: r r j j Ψ Ψ < T t 1 pro pro pro > Ψ Ψ t pro Ψ T s the number of tranng sequenes wth known subellular loaton j r T pro pro t Ψ Ψ 1 j Ψ j pro j T ore matrx Ψ Ψ ICONIP'06 9 pro T j

10 ICONIP'06 10 Tranng 1-vs-Rest VM Classfer T T j 0 and 0 α α y Quadrat Programmng C b y 1 V K α α α α α j j j j K y y max subjet to: } { 2 1 T D K Parwse equene/profle Algnment Computng Kernel Matrx j K b α

11 Classfaton by 1-vs-Rest VM Gven an unknown sequene the sore of the -th VM s gven by f V Predton s based on y seq α K C y arg max 1 f MAXNET y + b f 1 f 2 f C ICONIP'06 11

12 Classfaton by 1-vs-Rest VM Gven an unknown sequene the sore of the -th VM s gven by f V Predton s based on y pro α K Ψ Ψ + b C y arg max 1 f MAXNET y f 1 f 2 f C ICONIP'06 12

13 ICONIP'06 13 > + < α + α + α V V T t t t V b y b y b K y f r r 1 seq 1 V {24} T T {24} > < r r j K r r needs to be algned wth all tranng sequenes > Lots of omputaton T Feature eleton Major ause of omputaton burden

14 Feature eleton Feature Compute Densty Funtons of Postve and Negatve Classes Class Vetor r j m m p n ICONIP' Compute ymmetr Dvergene D γ γ

15 Feature eleton D γ Hstogram of for 4 lasses ICONIP'06 15 γ m m p n

16 Feature eleton equene Profle ICONIP'06 16

17 Experments We appled the sequene algnment VM and profle algnment VM to a eukaryot proten dataset Renhardt and Hubbard The dataset omprses 2427 annotated sequenes extrated from WIPORT 33.0 whh amounts to 684 ytoplasm 325 extraellular 321 mtohondral and 1097 nulear protens. 5-Fold ross valdaton was used to obtan the auray. ICONIP'06 17

18 Results Full feature k 0 k 3 k 2 k 1 ICONIP'06 18

19 Results ICONIP'06 19

20 Results Optmal pont ICONIP'06 20

21 Conlusons Expermental evaluaton on a benhmark proten sequene dataset shows that FDA-based seleton shemes an redue the feature dmenson from thousands to hundreds makng subsequent lassfaton muh easer Wth just a small reduton n reognton auray a substantal speed up n reognton tme an be aheved. ICONIP'06 21

22 Further Informaton ICONIP'06 22

23 Feature Extraton by Profle Algnment n j } 20 j P p j 1 p j 2 L j p v L j j p n j 1p n j }20 q 1 q 2 M u v M n q u u M q n 1 q pro Ψ Ψ j n Q v ICONIP'06 23

24 Feature Extraton by Profle Algnment ore for best path equene pro Ψ Ψ j equene 1 0 ICONIP'06 24

25 ICONIP'06 25 equene Algnment kernel: > < T t j t t j j K 1 seq seq seq seq seq r r T s the number of tranng sequenes wth known subellular loaton Feature Extraton by equene Algnment seq j r T T seq j ore matrx 1 seq j seq j T

26 Classfaton by 1-vs-Rest VM Gven an unknown sequene the sore of the -th VM s gven by f V Predton s based on y seq α K C y arg max 1 f MAXNET y + b f 1 f 2 f C ICONIP'06 26