1. Data integration
The STITCH database of protein–small molecule interactions
Lars Juhl Jensen

2. guilt by association

4. functional associations

5. Kuhn et al., Nucleic Acids Research, 2010

6. parts lists

7. >2.5 million proteins

8. 630 genomes

9. many databases

10. different formats

11. model organism databases

12. Ensembl

13. RefSeq

14. PubChem compounds

15. >74,000 small molecules

16. genomic context

17. gene fusion

18. Korbel et al., Nature Biotechnology, 2004

19. conserved neighborhood

20. operons

21. Korbel et al., Nature Biotechnology, 2004

22. bidirectional promoters

23. Korbel et al., Nature Biotechnology, 2004

24. phylogenetic profiles

25. Korbel et al., Nature Biotechnology, 2004

26. interaction data

27. protein–small molecule

28. in vitro binding assays

29. protein–protein

30. yeast two-hybrid

31. affinity purification

32. fragment complementation

33. Jensen & Bork, Science, 2008

34. genetic interactions

35. Beyer et al., Nature Reviews Genetics, 2007

36. gene coexpression

38. many databases

39. BindingDB

40. CTD
Comparative Toxicogenomics Database

41. DrugBank

42. GLIDA
GPCR-Ligand Database

43. PDSP Ki
Psycoactive Drug Screening Program

44. PharmGKB
Pharmacogenomics Knowledge Base

45. BIND
Biomolecular Interaction Network Database

46. BioGRID
General Repository for Interaction Datasets

47. DIP
Database of Interacting Proteins

48. IntAct

49. MINT
Molecular Interactions Database

50. HPRD
Human Protein Reference Database

51. PDB
Protein Data Bank

52. GEO
Gene Expression Omnibus

53. different formats

54. different identifiers

55. partially redundant

56. curated knowledge

57. complexes

58. pathways

59. Letunic & Bork, Trends in Biochemical Sciences, 2008

60. high confidence

61. many databases

62. MIPS
Munich Information center
for Protein Sequences

63. Gene Ontology

64. KEGG
Kyoto Encyclopedia of Genes and Genomes

65. MetaCyc

66. PID
NCI-Nature Pathway Interaction Database

67. Reactome

68. different formats

69. different identifiers

70. partially redundant

71. text mining

72. >10 km

73. human readable

74. not computer readable

75. different names

76. Reflect

77. dictionary

78. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009

79. text corpus

80. MEDLINE

81. SGD
Saccharomyces Genome Database

82. The Interactive Fly

83. OMIM
Online Mendelian Inheritance in Man

84. co-mentioning

85. NLP
Natural Language Processing

86. integration

87. many data types

88. not comparable

89. variable quality

90. spread over 630 genomes

91. quality scores

92. reproducibility

93. von Mering et al., Nucleic Acids Research, 2005

94. intergenic distances

95. Korbel et al., Nature Biotechnology, 2004

96. benchmarking

97. calibrate vs. gold standard

98. von Mering et al., Nucleic Acids Research, 2005

99. raw quality scores

100. probabilistic scores

101. orthology transfer

102. von Mering et al., Nucleic Acids Research, 2005

103. combine all evidence

105. Acknowledgments
Damian Szklarczyk
Andrea Franceschini
Michael Kuhn
Sune Frankild
Heiko Horn
Evangelos Pafilis
Milan Simonovic
Alexander Roth
Pablo Minguez
Tobias Doerks
Jean Muller
Manuel Stark
Samuel Chaffron
Chris Creevey
Philippe Julien
Jan Korbel
Berend Snel
Martijn Huynen
Reinhardt Schneider
Sean O’Donoghue
Christian von Mering
Peer Bork

106. Predicting novel targets for existing
drugs using side effect information
Lars Juhl Jensen

107. the problem

108. new uses for old drugs

109. drug–drug network

110. shared target(s)

111. chemical similarity

112. Campillos & Kuhn et al., Science, 2008

113. Campillos & Kuhn et al., Science, 2008

114. similar drugs share targets

115. only trivial predictions

116. the idea

117. chemical perturbations

118. phenotypic readouts

119. drug treatment

120. side effects

121. the hard work

122. information on side effects

123. no database

124. package inserts

125. Campillos & Kuhn et al., Science, 2008

126. text mining

127. side-effect ontology

128. backtracking

129. Campillos & Kuhn et al., Science, 2008

130. manual validation

131. SIDER
Kuhn et al., Molecular Systems Biology, 2010

132. side-effect correlations

133. Campillos & Kuhn et al., Science, 2008

134. GSC weighting

135. side-effect frequencies

136. Campillos & Kuhn et al., Science, 2008

137. raw similarity score

138. Campillos & Kuhn et al., Science, 2008

139. p-values

140. Campillos & Kuhn et al., Science, 2008

141. side-effect similarity

142. chemical similarity

143. Campillos & Kuhn et al., Science, 2008

144. confidence scores

145. reference set

146. incomplete databases

147. text mining

148. manual validation

149. MATADOR
Günther et al., Nucleic Acids Research, 2008

150. Campillos & Kuhn et al., Science, 2008

151. the results

152. drug–drug network

153. Campillos & Kuhn et al., Science, 2008

154. categorization

155. Campillos & Kuhn et al., Science, 2008

156. 20 drug–drug pairs

157. in vitro binding assays

158. Ki<10 µM for 11 of 20

159. cell assays

160. 9 of 9 showed activity

161. the future

162. link side-effects to targets

163. direct target prediction

164. Acknowledgments
Monica Campillos
Michael Kuhn
Anne-Claude Gavin
Peer Bork

165. larsjuhljensen