Summary of RoBiC Data/Results - Prognostic Datasets

See also Diagnostic datasets.
Data taken from > Kent Ridge Bio-medical Data Set Repository.

Prognostic Datasets

P₁: BreastCancer ⁽¹⁾ P₂: AML
(Outcome) ⁽²⁾ P₃: Central Nervous System P₄: Prostate
(Outcome) ⁽³⁾

Data Ptr Data Ptr Data Ptr Data Ptr Data

Number of Samples 76 15 60 21

Number of Genes/Attributes 23625 7129 7129 12600

Class Distribution relapse (32) /
non-relapse (44) fail (8) /
success (7) fail (39) /
survive (21) recurrent (8) /
non-recurrent (13)

Base % 57.9% 53.3% 65% 61.9%

Original Result 73%
78% 90%

Naive Bayes 63.18% 46.67% 63.33 47.62%

SVM 67.11% 53.33% 65% 47.62%

PAM ⁽⁴⁾
δ = 0 67.11% 53.33% 65% 47.62%

BiC(Plaid ⁽⁵⁾ ,NB)
# of biclusters 63.16%
1 73.33%
19 65%
1 71.43%
6

BiC(Plaid,SVM)
# of biclusters 63.16%
1 60%
10 65%
1 61.9%
2

BiC(RoBiC, NB)
# of biclusters
88.16 ±10.11 %
3 80 ±18.2 %
30 95 ±7.5 %
2 76.19 ±22.8 %
1

BiC(RoBiC,SVM)
# of biclusters⁽⁶⁾
90.79 ±7.6 %
2 80 ±18.2 %
16 95 ±7.5 %
2 85.71 ±12.0 %
13

Permutation Test (1000)

Average

# above Bic(RobiC,SVM) value
54.08%

0

1000

51.66%

27

1000

60.5%

0

1000

56.77%

22

1000

⁽¹⁾ We removed patients #9 and #10 (which had lots of missing genes: 12,632), and also removed the genes whose values were missing for at least one of the remaining patients (after removing #9 and #10). This meant removing 856 of the 24,481 genes.

⁽²⁾ This dataset is for predicting the clinical outcome for AML samples.

⁽³⁾ This dataset is for predicting the clinical outcome (recurrent/non-recurrent).

⁽⁴⁾ PAM is one of the standard algorithms for learning classifiers for microarray data.
We empirically found setting δ = 0 worked best for the Breast Cancer dataset; we continued to use this setting for the other data.

⁽⁵⁾ Plaid is a biclustering algorithm for microarray data.

⁽⁶⁾ RoBiC bicluster characteristics can be found here.

P₁: L. van't Veer, et al., Gene expression profiling predicts clinical outcome of breast cancer, Nature, 415, 530--536, January, 2002.
P₂: T. Golub, et al., Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring, Science, 286, 531-537, October 1999.
P₃: S. Pomeroy et al., Prediction of Central Nervous System Embryonal Tumour Outcome Based on Gene Expression, Letters to Nature, 415, 436-442, January, 2002.
P₄: D. Singh, et al., Gene Expression Correlates of Clinical Prostate Cancer Behavior, Cancer Cell, 1, 203--209, March, 2002.

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