IPAM WorkshopJan 2006

I. Searching for human splice-regulatory motifs

II. Network analysis of synthetic-lethal interactions

IPAM Workshop Jan 2006

Fritz RothHarvard Medical School

Dept. of Biological Chemistry & Molecular Pharmacology

Outline

Human alternative-splicing motif search

Yeast synthetic-lethal network analysis

Outline

Human alternative-splicing motif search Review mRNA splicing Splice-junction expression data Sequence neighborhoods Clustering splice-junctions by usage Results

Adapted from Molecular Cell Biology, Lodish et al.

Brief review of canonical mRNA splicing

~100,000 genes

Antiquity 2001 2004

35,000

25,000 human genes5% alt. spliced

~70% of human multi-exon genes

alternatively spliced

Importance of alternative splicing

From correlation to regulatory mechanism

Roth et al , 1998, Hughes et al, 2000 Tavazoie et al 1999, Slides adapted from S. Tavazoie

Sequence neighborhood of a splice junction

(Sorek & Ast, Gen. Research, 2003)

Conservation: constitutive vs. alternative

3’ acceptorneighborhood

5’ donor neighborhood

Previous splicing motif searches

Canonical splicing enhancers/repressors not associated with specific tissues (e.g. Brudno et al., Burge et al, Chasin et al)

Based on small (curated literature) set of alternatively spliced genes (e.g. , Brudno et al, Stamm et al., Fedorov et al.)

Based on expressed sequence tag (EST) datasets (Xu et al), biased towards 3’ ends of genes, can contain artificial splice variants due to Unigene clustering (Modrek & Lee, 2002)

• Every splice junction for ~11K reference mRNAs for ~10K genes

• ~100K probe sequences on five arrays

• 49 tissues & cell lines run in fluor-reversed pairs

• full-length mRNA amplification

Johnson et al. Science. 2003 Dec 19;302(5653):2141-4.Castle et al. Genome Biology. 2003;4(10):R66.

Pre-mRNA

Alternative mature mRNAs 18 nt 18 nt+

Cassette exon

Exon-exon splice junction expression data

Tissue-specific probes & data

splice junction usage data

Flowchart

original intensities

probes5’ 3’

tissues

Tissue-specific splice junction expression

probes5’ 3’

tissues rescaled intensities

probes5’ 3’

tissues relative splicing

rescaled intensities

Synexin (ANXA7)

splice junction usage data cassette exons

alternative donors / acceptors

skipped cassette exons

Flowchart

Clustered splice junctions

allcassette exons

alternative

donors / acceptors

Probe set choice

Cassette exons skipped in heart & skeletal muscle

Results: heart & muscle

Flowchart

clustered splice junctions

allcassette exons

alternative

donors / acceptors

Probe set choice

all exonic intronicSequence neighborhoo

d choiceproximalto donor

proximal to

acceptor

sequence neighborhoods

Sequence neighborhood of a splice junction

1. Immediate neighborhood2. Neighborhood of junctions with shared splice site 3. Neighborhood of junctions of other competing junctions

Alternative splice junction neighborhoods

Flowchart

clustered splice junctions

allcassette exons

alternative

donors / acceptors

Probe set choice

all exonic intronicSequence neighborhoo

d choiceproximalto donor

proximal to

acceptor

sequence neighborhoods

Pattern findingword-based

motif-based

enriched sequence patterns

Cassette exons skipped in heart & skeletal muscle

ACTAAC @ end of intron- 8 out of 39 probes- 14-fold enrichment- strong position bias

(often nearby)

Results: heart & muscle

Cassette exons skipped in brain

26% of probes (9-fold enrichment)

Results: brain

76% (3-fold)

65% (15-fold)

Results: ileum

Results: jejunum, liver, pancreas Cluster

HNRPLClusterResults: jejunum, liver, pancreas

RRM1 RRM2 RRM3 RRM4

Results: jejunum, liver, pancreasHNRPLCluster

RRM1 RRM2 RRM3 RRM4

HNRPLProtein interaction

RRM1 RRM2 RRM3 RRM4

HNRPLProtein interaction

tissue-specificalternative

splicing

candidatecis-regulatory

motifs

Summary, Part I

validatedcis-regulatory

motifs

Map to trans-actingsplicing factors

candidatecis-regulatory

secondary strcture

Acknowledgments, Part I

Adnan Derti

George Church & Lab

Roth Lab

Rosetta/Merck Jason Johnson John Castle Lee Lim

Adrian Krainer

Outline

Yeast synthetic-lethal network analysis Background

Overlap with other biological relationships

Network motifs

Predicting synthetic lethality

Role of transcription compensation in mutational robustness

SSL vs. protein interaction in predicting function

What is Synthetic Lethality?

Cells live

Cells die

Gene XGene Y

What is Synthetic Sickness/Lethality (SSL)?

Cells live

Cells dieor grow slowly

Gene XGene Y

An engine can run without one cylinder

(from http://www.cs.unc.edu/~geom/collide/videos.shtml)

Scenarios resulting in synthetic genetic interaction

2 partially redundant pathways

Partially redundant

3 compensatory pathways,

2 required

Protein complex

tolerating 1 but not

2 mutations

A known sub-network of SSL interactions

A Canadian consortium (Boone et al.) has made many double mutants

As of 2001:

8 query genes

4500 nonessential “array” genes

≈ 36,000 tested pairs

(Tong et al., Science, 2001)

The known sub-network circa 2001

The known SSL sub-network circa 2004160 query x 4500 nonessential ≈ 700,000 tested pairs (≈4% pairs)

~3800 interactions

Outline

Network motifs

Query Characteristic S+C S only C only Neither P(C|S) Odds P(S|C) P-valueSimilar GO annotation 779 1746 27256 698373 0.31 8 0.03 3E-460Same MIPS phenotype 389 2136 9453 716176 0.15 12 0.04 1E-273Same GO annotation 385 2140 8361 717268 0.15 13 0.04 6E-288Same subcellular localization 113 2412 3500 722129 0.04 9 0.03 7E-68Same MIPS complex 82 2443 1445 724184 0.03 16 0.05 1E-67Physical interaction 96 2429 2586 723043 0.04 11 0.04 5E-63Sequence homology (BLAST Eval < 10E-3) 47 2478 1769 723860 0.02 8 0.03 1E-25Same MIPS complex (with no subcomplexes) 20 2505 166 725463 0.01 35 0.11 1E-23Same MIPS protein class 29 2496 1023 724606 0.01 8 0.03 4E-17Same MCODE complex 26 2499 906 724723 0.01 8 0.03 1E-15Common physical interaction partner 33 2492 2911 722718 0.01 3 0.01 1E-08Correlated mRNA expr (Cho, CC >0.7) 46 2479 6171 719458 0.02 2 7E-03 3E-06Physical interaction: APMS 14 2511 1032 724597 0.01 4 0.01 3E-05Physical interaction: APMS: HMS-PCI 9 2516 699 724930 4E-03 4 0.01 1E-03Phys interaction: Y2H U APMS "spoke" 5 2520 290 725339 2E-03 5 0.02 4E-03

Similar GO annotation 779 1746 27256 698373 0.31 8 0.03 3E-460Same MIPS phenotype 389 2136 9453 716176 0.15 12 0.04 1E-273Same GO annotation 385 2140 8361 717268 0.15 13 0.04 6E-288Same subcellular localization 113 2412 3500 722129 0.04 9 0.03 7E-68Same MIPS complex 82 2443 1445 724184 0.03 16 0.05 1E-67Physical interaction 96 2429 2586 723043 0.04 11 0.04 5E-63Sequence homology (BLAST Eval < 10E-3) 47 2478 1769 723860 0.02 8 0.03 1E-25Same MIPS complex (with no subcomplexes) 20 2505 166 725463 0.01 35 0.11 1E-23Same MIPS protein class 29 2496 1023 724606 0.01 8 0.03 4E-17Same MCODE complex 26 2499 906 724723 0.01 8 0.03 1E-15Common physical interaction partner 33 2492 2911 722718 0.01 3 0.01 1E-08Correlated mRNA expr (Cho, CC >0.7) 46 2479 6171 719458 0.02 2 7E-03 3E-06Physical interaction: APMS 14 2511 1032 724597 0.01 4 0.01 3E-05Physical interaction: APMS: HMS-PCI 9 2516 699 724930 4E-03 4 0.01 1E-03Phys interaction: Y2H U APMS "spoke" 5 2520 290 725339 2E-03 5 0.02 4E-03

Overlap between synthetic lethality & other “interactions”

Scenarios resulting in synthetic interaction

2 partially redundant pathways

Partially redundant

3 partially redundant

pathways, 2 required

Protein complex

tolerating 1 but not

2 destabilizi

ng mutations

< 2% < 4% *

Outline

Network motifs

“Network Motifs: Simple Building Blocks of Complex Networks”

R. Milo, S. Shen-Orr, , , U. Alon, Science (2003).

The synthetic lethal network has many triangles

Xiaofeng Xin, Boone Lab

Motifs in an integrated S. cerevisiae network

S: Synthetic sickness or lethality

X: Correlated expression

P: Stable physical interaction

R: Transcriptional regulation

H: Sequence homology

(2.6+0.5) ×102

4.7×102

Nrand:

Nreal:

(2.6+0.5) ×102

4.7×102

Nrand:

Nreal:

Mcm1 Sw i4

Gin4 Cdc6

Yor315w

Motif Set A

a network motif a network theme

Hap2 Hap3

B2B1 B2B1

Nrand:

Nreal:

(2.3+1.1) ×1017.0+6.7

1.0×1022.0×102

Nrand:

Nreal:

(2.3+1.1) ×1017.0+6.7

1.0×1022.0×102RR

P/X/Ha b

Qcr10 Isa1

Ypl207w

Qcr9 Prp3

Hap3Hap2

Motif Set B

Hhf1 Hht1

C2C1 C2C1

Nrand:

Nreal:

(3.6+0.2) ×103(4.3+0.5)×102

1.5×1045.6×103

Nrand:

Nreal:

(3.6+0.2) ×103(4.3+0.5)×102

1.5×1045.6×103

Hir1 Hir2

Hhf2 Hht2

Htb2Hta2

Hta1Motif Set C

a network motif a network themeH H

S,HS,H

Cla4 Slt2S

D4D3D2D1 D4D3D2D1

(1.0+0.2) ×105

5.6×105

(1.3+0.1)×103

3.2×103

(1.7+0.1) ×103

2.7×103

Nrand:

Nreal:

(3.8+0.4) ×102

9.8×102

(1.0+0.2) ×105

5.6×105

(1.3+0.1)×103

3.2×103

(1.7+0.1) ×103

2.7×103

Nrand:

Nreal:

(3.8+0.4) ×102

9.8×102

HS/HS/H

b c Cla4 Slt2

Motif Set D

P,XP,X

Atp14 Atp3

E4E3E2E1 E4E3E2E1

(2.7+0.4) ×105

1.5 ×106

(6.4+0.2) ×104

4.5×105

Nrand:

Nreal:

(1.7+0.1) ×106(2.6+0.5) ×102

9.5×1064.7×102

(2.7+0.4) ×105

1.5 ×106

(6.4+0.2) ×104

4.5×105

Nrand:

Nreal:

(1.7+0.1) ×106(2.6+0.5) ×102

9.5×1064.7×102

P/XP/X

X X Atp2

Atp14 Atp3

Motif Set E

F4 F5F3F2F1 F4 F5F3F2F1

(3.9+0.2) ×103

7.5×103

(1.4+0.1)×103

1.8×104

(8.4+0.2)×103

1.2×104

Nrand:

Nreal:

(1.3+0.1)×103(1.8+0.3)×102

3.2×1034.4×102

(3.9+0.2) ×103

7.5×103

(1.4+0.1)×103

1.8×104

(8.4+0.2)×103

1.2×104

Nrand:

Nreal:

(1.3+0.1)×103(1.8+0.3)×102

3.2×1034.4×102

P/XP/X

Ssn8 Cdc73

Rpb5 Rpo21

Rpb4Rpb2

Motif Set F

a network motif a network themeSec72

Yke2 Gim5

G5 G6G4G3G2G1 G5 G6G4G3G2G1

(8.3+0.6) ×102

1.2×103

Nrand:

Nreal:

(3.1+0.1) ×104(6.1+0.5) ×102(1.7+0.1)×103(7.2+1.2) ×101(9.0+1.6)×101

6.7×1041.5×1038.6×1032.9×1021.7×103

(8.3+0.6) ×102

1.2×103

Nrand:

Nreal:

(3.1+0.1) ×104(6.1+0.5) ×102(1.7+0.1)×103(7.2+1.2) ×101(9.0+1.6)×101

6.7×1041.5×1038.6×1032.9×1021.7×103

S S H H

S/HS/H

Yke2 Gim5

Pac10Gim3

Motif Set G

R X R X

H6H4H3H2 H5

H6H4H3H2

(5.3+0.5)×102

1.9×103

(8.0+2.3)×101

6.1×102

(1.8+0.6) ×101

5.8×101

(4.0+0.4) ×102

8.2×102

(6.6+1.0) ×102

1.1×103

Nrand:

Nreal:

(2.5+0.2)×103

3.2×103

(5.3+0.5)×102

1.9×103

(8.0+2.3)×101

6.1×102

(1.8+0.6) ×101

5.8×101

(4.0+0.4) ×102

8.2×102

(6.6+1.0) ×102

1.1×103

Nrand:

Nreal:

(2.5+0.2)×103

3.2×103

Motif Set H

(2.6+0.5) ×102

4.7×102

Nrand:

Nreal:

(2.6+0.5) ×102

4.7×102

Nrand:

Nreal:

Mcm1 Sw i4

Gin4 Cdc6Rax2

Yor315w

Motif Set A

Hap2 Hap3

B2B1 B2B1

Nrand:

Nreal:

(2.3+1.1) ×1017.0+6.7

1.0×1022.0×102

Nrand:

Nreal:

(2.3+1.1) ×1017.0+6.7

1.0×1022.0×102RR

P/X/Ha b

Qcr10 Isa1

Ypl207w

Qcr9 Prp3

Hap3Hap2

Motif Set B

Hhf1 Hht1

C2C1 C2C1

Nrand:

Nreal:

(3.6+0.2) ×103(4.3+0.5)×102

1.5×1045.6×103

Nrand:

Nreal:

(3.6+0.2) ×103(4.3+0.5)×102

1.5×1045.6×103

Hir1 Hir2

Hhf2 Hht2

Htb2Hta2

Hta1Motif Set C

a network motif a network themeH H

S,HS,H

Cla4 Slt2S

D4D3D2D1 D4D3D2D1

(1.0+0.2) ×105

5.6×105

(1.3+0.1)×103

3.2×103

(1.7+0.1) ×103

2.7×103

Nrand:

Nreal:

(3.8+0.4) ×102

9.8×102

(1.0+0.2) ×105

5.6×105

(1.3+0.1)×103

3.2×103

(1.7+0.1) ×103

2.7×103

Nrand:

Nreal:

(3.8+0.4) ×102

9.8×102

HS/HS/H

b c Cla4 Slt2

Motif Set D

P,XP,X

Atp14 Atp3

E4E3E2E1 E4E3E2E1

(2.7+0.4) ×105

1.5 ×106

(6.4+0.2) ×104

4.5×105

Nrand:

Nreal:

(1.7+0.1) ×106(2.6+0.5) ×102

9.5×1064.7×102

(2.7+0.4) ×105

1.5 ×106

(6.4+0.2) ×104

4.5×105

Nrand:

Nreal:

(1.7+0.1) ×106(2.6+0.5) ×102

9.5×1064.7×102

P/XP/X

X X Atp2

Atp14 Atp3

Motif Set E

F4 F5F3F2F1 F4 F5F3F2F1

(3.9+0.2) ×103

7.5×103

(1.4+0.1)×103

1.8×104

(8.4+0.2)×103

1.2×104

Nrand:

Nreal:

(1.3+0.1)×103(1.8+0.3)×102

3.2×1034.4×102

(3.9+0.2) ×103

7.5×103

(1.4+0.1)×103

1.8×104

(8.4+0.2)×103

1.2×104

Nrand:

Nreal:

(1.3+0.1)×103(1.8+0.3)×102

3.2×1034.4×102

P/XP/X

Ssn8 Cdc73

Rpb5 Rpo21

Rpb4Rpb2

Motif Set F

a network motif a network themeSec72

Yke2 Gim5

(8.3+0.6) ×102

1.2×103

Nrand:

Nreal:

(3.1+0.1) ×104(6.1+0.5) ×102(1.7+0.1)×103(7.2+1.2) ×101(9.0+1.6)×101

6.7×1041.5×1038.6×1032.9×1021.7×103

(8.3+0.6) ×102

1.2×103

Nrand:

Nreal:

(3.1+0.1) ×104(6.1+0.5) ×102(1.7+0.1)×103(7.2+1.2) ×101(9.0+1.6)×101

6.7×1041.5×1038.6×1032.9×1021.7×103

S S H H

S/HS/H

Yke2 Gim5

Pac10Gim3

Motif Set G

R X R X

H6H4H3H2 H5

H6H4H3H2

(5.3+0.5)×102

1.9×103

(8.0+2.3)×101

6.1×102

(1.8+0.6) ×101

5.8×101

(4.0+0.4) ×102

8.2×102

(6.6+1.0) ×102

1.1×103

Nrand:

Nreal:

(2.5+0.2)×103

3.2×103

(5.3+0.5)×102

1.9×103

(8.0+2.3)×101

6.1×102

(1.8+0.6) ×101

5.8×101

(4.0+0.4) ×102

8.2×102

(6.6+1.0) ×102

1.1×103

Nrand:

Nreal:

(2.5+0.2)×103

3.2×103

Motif Set H

Motifs Themes

one gene synthetic lethal with a

complex?

Yke2 Gim5

(8.3+0.6) ×102

1.2×103

Nrand:

Nreal:

(3.1+0.1) ×104(6.1+0.5) ×102(1.7+0.1)×103(7.2+1.2) ×101(9.0+1.6)×101

6.7×1041.5×1038.6×1032.9×1021.7×103

(8.3+0.6) ×102

1.2×103

Nrand:

Nreal:

(3.1+0.1) ×104(6.1+0.5) ×102(1.7+0.1)×103(7.2+1.2) ×101(9.0+1.6)×101

6.7×1041.5×1038.6×1032.9×1021.7×103

S S H H

S/HS/H

Yke2 Gim5

Pac10Gim3

Motif Set G

Yke2 Gim5

(8.3+0.6) ×102

1.2×103

Nrand:

Nreal:

(3.1+0.1) ×104(6.1+0.5) ×102(1.7+0.1)×103(7.2+1.2) ×101(9.0+1.6)×101

6.7×1041.5×1038.6×1032.9×1021.7×103

(8.3+0.6) ×102

1.2×103

Nrand:

Nreal:

(3.1+0.1) ×104(6.1+0.5) ×102(1.7+0.1)×103(7.2+1.2) ×101(9.0+1.6)×101

6.7×1041.5×1038.6×1032.9×1021.7×103

S S H H

S/HS/H

Yke2 Gim5

Pac10Gim3

Motif Set G

Yke2 Gim5

(8.3+0.6) ×102

1.2×103

Nrand:

Nreal:

(3.1+0.1) ×104(6.1+0.5) ×102(1.7+0.1)×103(7.2+1.2) ×101(9.0+1.6)×101

6.7×1041.5×1038.6×1032.9×1021.7×103

(8.3+0.6) ×102

1.2×103

Nrand:

Nreal:

(3.1+0.1) ×104(6.1+0.5) ×102(1.7+0.1)×103(7.2+1.2) ×101(9.0+1.6)×101

6.7×1041.5×1038.6×1032.9×1021.7×103

S S H H

S/HS/H

Yke2 Gim5

Pac10Gim3

Motif Set G

Yke2 Gim5

(8.3+0.6) ×102

1.2×103

Nrand:

Nreal:

(3.1+0.1) ×104(6.1+0.5) ×102(1.7+0.1)×103(7.2+1.2) ×101(9.0+1.6)×101

6.7×1041.5×1038.6×1032.9×1021.7×103

(8.3+0.6) ×102

1.2×103

Nrand:

Nreal:

(3.1+0.1) ×104(6.1+0.5) ×102(1.7+0.1)×103(7.2+1.2) ×101(9.0+1.6)×101

6.7×1041.5×1038.6×1032.9×1021.7×103

S S H H

S/HS/H

Yke2 Gim5

Pac10Gim3

Motif Set G

Motifs Themes

pair of synthetic lethal complexes?

… …… …S/H S/H S/HS/H

Sec72 Sec66

Gim5Yke2

P Sec72

Yke2 Gim5

Sec63Sec62

Sec72 Sec66

Gim5Yke2

P Sec72

Yke2 Gim5

Sec63Sec62

Sec72 Sec66

Gim5Yke2

P Sec72

Yke2 Gim5

Sec63Sec62

Sec72 Sec66

Gim5Yke2

P Sec72

Yke2 Gim5

Sec63Sec62

Thematic map ofsynthetic-lethal complexes

Mapping pairs ofsynthetic-lethal complexes

Outline

Network motifs

Predicting Synthetic Lethality: Why?

Especially if the consortia led by Boone and Boeke

are testing all yeast gene pairs

Simple Answer:

Even finished, this project is one strain, one organism, one phenotype (growth), and one growth condition.

Predicting Synthetic Lethality: Many weak predictors

Predicting Synthetic Lethality: Probabilistic decision trees

0 0.2 0.4 0.6 0.8 1

False Positive Rate

tivity

xval: all characteristics

xval: no 2hop characteristics

random

Predicting Synthetic Lethality: Cross-validation success

~80% sensitivity by testing ~20% of gene pairs (80/20 Rule!)

Outline

Network motifs

Upregulation of compensatory genes?

Gene MSSL

Gene G

Anecdotally, this is rare (Lesage et al, 2004)

Upregulation of compensatory genes:Data sets examined

mRNA expression, mutant vs. wild type

(Rosetta compendium, Hughes et al, 2000)

SSL genetic interactions (Tong et al.)

-2 -1 0 1 2

non-SSL

Log ratios

116,863935

Upregulation of compensatory genes:Distribution of log (Gmutant_M / Gwt)]

Upregulation of compensatory genes:How common is it?

Of 935 SSL M:G pairs examined, only thirteen went up significantly

That is only four more than expected given the fraction that went up in 120,000 non-SSL pairs

Six previously observed examplesFKS1 RLM1 Zhao, 1998FKS1 SLT2 Lesage, 2004GAS1 CHS3 Lesage, 2004GAS1 KRE11 Lesage, 2004GAS1 SLT2 Lesage, 2004GAS1 YALO53W Lesage, 2004

Seven new examplesBNI1 SLT2CDC42 GIC2 (translational compensation; Jacquenoud, 1998)FKS1 KAI1FKS1 PAL2GAS1 YMR316C-ASHE4 ARC40SHE4 CHS7

Thirteen examples of compensatory upregulation

Transcriptional compensation for gene loss.

exists for a few SSL pairs is extremely rare

Rationalization

Regulatory apparatus to detect gene loss… Provides only a weak benefit if gene loss is rare Large mutational target

Outline

Network motifs

Tested forProtein intxnTested

for genetic intxn

Tested for both

data sets: 1 genetic, 5 protein

SSL vs protein interaction: The arena

phys data set (overlapping w/

genetic) gene pairs

predictive interaction GO characteristic accuracy sensitivity

TAP spoke 104409 physical 0.42 0.006genetic shared Process 0.19 0.041physical 0.31 0.008genetic shared Function 0.15 0.061physical 0.29 0.012genetic shared Component 0.08 0.048

TAP matrix 104409

HMS-PCI spoke 168114

HMS-PCI matrix 168114

Y2H 566423

SSL vs protein interaction

Protein intxns are more accurate.SSL intxns are more sensitive.

Common regulatorGene co-occurenceGene fusionGene neighborhoodHomologymRNA coexpressionChromosomal distanceSame localizationSame phenotypeMany ‘2-hop’ relationships

SSL vs protein interaction: combining with other relationships

0 0.2 0.4 0.6 0.8 1

all characteristicsno Pno Gno P, G

P onlyG onlyP, G onlyrandom

False Positive Rate (1 – Specificity)

0 0.01 0.02

SSL vs protein interaction:combining with other relationships

Summary, Part II

SSL other biological relationships

Map of compensatory complexes

Transcriptional compensation plays a minor role in robustness to gene loss

SSL vs. protein interaction in predicting function: SSL wins but is complementary

Acknowledgments, Part II

Roth Lab Sharyl Wong Lan Zhang Oliver King Debra Goldberg Gabriel Berriz Frank Gibbons Others

Data Sources SGD MIPS YPD

Canadian Synthetic Lethal Team

Charlie Boone Amy Tong Guillaume Lesage Howard Bussey Brenda Andrews Xiaofeng Xin Gary Bader Zhijian Li Others

Marc Vidal

Probabilistic Decision Trees

Models conditional probability of one variable given a combination of others.

Capable of integrating many variables (built-in feature selection).

Provides intuition about why predictions are made.

IPAM WorkshopJan 2006

Documents

Transcript of IPAM WorkshopJan 2006

IPAM Research in Industrial Projects for Students (RIPS ...math.ewha.ac.kr/~chohong/publications/rips_final_report.pdf · IPAM Research in Industrial Projects for Students (RIPS)

2014 IPAM IMO DAY

Controlled Algebras and GII’s Ronald L. Rivest MIT CSAIL IPAM Workshop October 9, 2006.

IPAM in University Environments

Changing of the Guard at IPAM

IPAM Microsoft Documentation

IPAM Products and Services - Calleva Networks Ltd. · IPAM Products and Services from BT Diamond IP BT Diamond IP IPAM products and services automate these three key elements of IPAM:

GestióIP IPAM - brucknet.debrucknet.de/gestioip/docu/Documentacion_GestioIP_34_en.pdf · Documentation GestióIP IPAM v3.4 Click over the corresponding network to list all of it's

Security Capabilities of IPAM

22 jump start DHCP and IPAM ppt

IPAM- APPL Entry Workbook€¦ · IPAM- APPL Entry Workbook IPAM Series – IPAM APPL Entry Workbook 01/08/14 7 Logging On To complete the exercises in this workbook you need to log

Implementing and securing a resilient The IPAM Specialists ... · Implementing and securing a resilient The IPAM Specialists network services infrastructure

NSF Annual Report 2006 UPDATED 11-15-06 - IPAM … · IPAM Annual Progress Report, updated October 2006 4 For IPAM’s new grant (year 1), the figures are as follows: Expense Category

COURSE OUTLINE · 4.1.5 Practice Questions 4.2 IPAM DNS and DHCP 4.2.1 IPAM and DHCP Overview (1:32) 4.2.2 Manage the DHCP Server with IPAM (2:47) 4.2.3 Configure DHCP Scopes and

DNS, DHCP & IPAM with IPv6

IPAM 31 March 2009 Multiscale Methods for Coulomb Collisions in Plasmas Russel Caflisch IPAM Mathematics Department, UCLA.

Injury Prevention and Management (IPaM) program · Title: IPaM Infographic 2016 Author: Injury Prevention and Management program Subject: A flyer highlighting IPaM infographics Keywords:

IPv6 0x09 Gestion d’adresses IPv6 (IPAM)

IPAM Newsletter

SolarWinds Orion IPAM AdministratorGuide