首页下载资源网络技术用 VisualBasic 编写的知识图谱数据库引擎

ZIP用 VisualBasic 编写的知识图谱数据库引擎

2301_8048821412.75MB需要积分:1

资源文件列表:

graphQL-main.zip 大约有735个文件
  1. graphQL-main/
  2. graphQL-main/.Rbuildignore 28B
  3. graphQL-main/.Rhistory
  4. graphQL-main/.github/
  5. graphQL-main/.github/ISSUE_TEMPLATE/
  6. graphQL-main/.github/ISSUE_TEMPLATE/bug_report.md 834B
  7. graphQL-main/.github/ISSUE_TEMPLATE/custom.md 126B
  8. graphQL-main/.github/ISSUE_TEMPLATE/feature_request.md 595B
  9. graphQL-main/.github/gpl3.xml 1.05KB
  10. graphQL-main/.github/sync_multiplegit.cmd 155B
  11. graphQL-main/.github/update_banner.cmd 275B
  12. graphQL-main/.gitignore 5.95KB
  13. graphQL-main/.gitmodules 219B
  14. graphQL-main/.pkg/
  15. graphQL-main/.pkg/package.cmd 254B
  16. graphQL-main/.pkg/package_net48.cmd 247B
  17. graphQL-main/.vscode/
  18. graphQL-main/.vscode/settings.json 43B
  19. graphQL-main/@export/
  20. graphQL-main/@export/KnowledgeGraph.d.ts 3.23KB
  21. graphQL-main/@export/MsgFile.d.ts 1.95KB
  22. graphQL-main/@export/Query.d.ts 1.81KB
  23. graphQL-main/@export/graph_mysql.d.ts 2.58KB
  24. graphQL-main/@export/index.d.ts 2.09KB
  25. graphQL-main/@export/mysql.d.ts 5.69KB
  26. graphQL-main/CODE_OF_CONDUCT.md 5.08KB
  27. graphQL-main/DESCRIPTION 369B
  28. graphQL-main/LICENSE 1.06KB
  29. graphQL-main/NAMESPACE 31B
  30. graphQL-main/R/
  31. graphQL-main/R/definition.R 1.44KB
  32. graphQL-main/R/graphKbWeb.R 2.64KB
  33. graphQL-main/R/imports_stopwords.R 5.18KB
  34. graphQL-main/R/text_graph.R 2.81KB
  35. graphQL-main/R/zzz.R 331B
  36. graphQL-main/README.md 1.77KB
  37. graphQL-main/_config.yml 26B
  38. graphQL-main/data/
  39. graphQL-main/data/verbs.txt 2.57KB
  40. graphQL-main/docs/
  41. graphQL-main/docs/1752-0509-8-S4-S11.pdf 837.99KB
  42. graphQL-main/graphQL.Rproj 356B
  43. graphQL-main/man/
  44. graphQL-main/man/.onLoad.1 248B
  45. graphQL-main/man/__cosine.1 338B
  46. graphQL-main/man/__jaccard.1 344B
  47. graphQL-main/man/context_cosine.1 664B
  48. graphQL-main/man/definition.1 773B
  49. graphQL-main/man/get_prompt.1 618B
  50. graphQL-main/man/graphR/
  51. graphQL-main/man/graphR/KnowledgeGraph/
  52. graphQL-main/man/graphR/KnowledgeGraph/KnowledgeGraph.md 756B
  53. graphQL-main/man/graphR/KnowledgeGraph/KnowledgeGraph/
  54. graphQL-main/man/graphR/KnowledgeGraph/KnowledgeGraph/Kosaraju.SCCs.1 257B
  55. graphQL-main/man/graphR/KnowledgeGraph/KnowledgeGraph/as.knowledge.1 422B
  56. graphQL-main/man/graphR/KnowledgeGraph/KnowledgeGraph/correctKnowledges.1 347B
  57. graphQL-main/man/graphR/KnowledgeGraph/KnowledgeGraph/extractKnowledgeTerms.1 325B
  58. graphQL-main/man/graphR/KnowledgeGraph/KnowledgeGraph/graphUMAP.1 269B
  59. graphQL-main/man/graphR/KnowledgeGraph/KnowledgeGraph/knowledgeCommunity.1 1.34KB
  60. graphQL-main/man/graphR/KnowledgeGraph/KnowledgeGraph/knowledgeIslands.1 353B
  61. graphQL-main/man/graphR/KnowledgeGraph/KnowledgeGraph/networkGraph.1 512B
  62. graphQL-main/man/graphR/KnowledgeGraph/KnowledgeGraph/niceTerms.1 833B
  63. graphQL-main/man/graphR/KnowledgeGraph/KnowledgeGraph/removeLinkByWeight.1 297B
  64. graphQL-main/man/graphR/MsgFile/
  65. graphQL-main/man/graphR/MsgFile/MsgFile.md 500B
  66. graphQL-main/man/graphR/MsgFile/MsgFile/
  67. graphQL-main/man/graphR/MsgFile/MsgFile/edgeSource.1 239B
  68. graphQL-main/man/graphR/MsgFile/MsgFile/getTerms.1 224B
  69. graphQL-main/man/graphR/MsgFile/MsgFile/open.1 569B
  70. graphQL-main/man/graphR/MsgFile/MsgFile/read.graph.1 433B
  71. graphQL-main/man/graphR/MsgFile/MsgFile/read.knowledge_table.1 495B
  72. graphQL-main/man/graphR/MsgFile/MsgFile/save.1 513B
  73. graphQL-main/man/graphR/MsgFile/MsgFile/seekTerm.1 247B
  74. graphQL-main/man/graphR/Query/
  75. graphQL-main/man/graphR/Query/Query.md 408B
  76. graphQL-main/man/graphR/Query/Query/
  77. graphQL-main/man/graphR/Query/Query/ignore.evidenceLink.1 488B
  78. graphQL-main/man/graphR/Query/Query/insert.1 690B
  79. graphQL-main/man/graphR/Query/Query/join.1 239B
  80. graphQL-main/man/graphR/Query/Query/query.1 480B
  81. graphQL-main/man/graphR/Query/Query/similarity.1 633B
  82. graphQL-main/man/graphR/graph_mysql/
  83. graphQL-main/man/graphR/graph_mysql/graph_mysql.md 575B
  84. graphQL-main/man/graphR/graph_mysql/graph_mysql/
  85. graphQL-main/man/graphR/graph_mysql/graph_mysql/add_term.1 327B
  86. graphQL-main/man/graphR/graph_mysql/graph_mysql/as.knowledge_builder.1 560B
  87. graphQL-main/man/graphR/graph_mysql/graph_mysql/assign_graph.1 543B
  88. graphQL-main/man/graphR/graph_mysql/graph_mysql/fetch_json.1 271B
  89. graphQL-main/man/graphR/graph_mysql/graph_mysql/fetch_table.1 365B
  90. graphQL-main/man/graphR/graph_mysql/graph_mysql/open.graphdb.1 371B
  91. graphQL-main/man/graphR/graph_mysql/graph_mysql/pull_nextGraph.1 582B
  92. graphQL-main/man/graphR/graph_mysql/graph_mysql/save.knowledge.1 766B
  93. graphQL-main/man/graphR/mysql/
  94. graphQL-main/man/graphR/mysql/mysql.md 1.66KB
  95. graphQL-main/man/graphR/mysql/mysql/
  96. graphQL-main/man/graphR/mysql/mysql/add.1 226B
  97. graphQL-main/man/graphR/mysql/mysql/check.1 496B
  98. graphQL-main/man/graphR/mysql/mysql/close_ssh.1 190B
  99. graphQL-main/man/graphR/mysql/mysql/count.1 204B
  100. graphQL-main/man/graphR/mysql/mysql/create_filedump.1 257B
  101. graphQL-main/man/graphR/mysql/mysql/dump_inserts.1 527B
  102. graphQL-main/man/graphR/mysql/mysql/exec.1 455B
  103. graphQL-main/man/graphR/mysql/mysql/find.1 230B
  104. graphQL-main/man/graphR/mysql/mysql/get_last_sql.1 232B
  105. graphQL-main/man/graphR/mysql/mysql/limit.1 269B
  106. graphQL-main/man/graphR/mysql/mysql/open.1 1.13KB
  107. graphQL-main/man/graphR/mysql/mysql/order_by.1 265B
  108. graphQL-main/man/graphR/mysql/mysql/performance_counter.1 1.22KB
  109. graphQL-main/man/graphR/mysql/mysql/project.1 260B
  110. graphQL-main/man/graphR/mysql/mysql/save.1 230B
  111. graphQL-main/man/graphR/mysql/mysql/select.1 238B
  112. graphQL-main/man/graphR/mysql/mysql/table.1 231B
  113. graphQL-main/man/graphR/mysql/mysql/where.1 234B
  114. graphQL-main/man/graphR/mysql/mysql/write_dumps.1 280B
  115. graphQL-main/man/graph_link.1 488B
  116. graphQL-main/man/graph_tokens.1 346B
  117. graphQL-main/man/import_stopwords.1 482B
  118. graphQL-main/man/import_verbs.1 457B
  119. graphQL-main/man/index.json 6.35KB
  120. graphQL-main/man/one_graph.1 602B
  121. graphQL-main/man/push_graph.1 520B
  122. graphQL-main/man/text_graph.1 536B
  123. graphQL-main/man/token_vector.1 633B
  124. graphQL-main/man/tokens_trim.1 334B
  125. graphQL-main/proj_stats.csv 13.24KB
  126. graphQL-main/src/
  127. graphQL-main/src/Capture.PNG 26.29KB
  128. graphQL-main/src/graphMsg/
  129. graphQL-main/src/graphMsg/EvidenceLinks.vb 4.31KB
  130. graphQL-main/src/graphMsg/EvidenceStream.vb 3.79KB
  131. graphQL-main/src/graphMsg/GraphReader.vb 2.98KB
  132. graphQL-main/src/graphMsg/JSONDump.vb 4.43KB
  133. graphQL-main/src/graphMsg/Message/
  134. graphQL-main/src/graphMsg/Message/EvidenceMsg.vb 3.27KB
  135. graphQL-main/src/graphMsg/Message/IndexByRef.vb 1.93KB
  136. graphQL-main/src/graphMsg/Message/KnowledgeMsg.vb 3.39KB
  137. graphQL-main/src/graphMsg/Message/LinkMsg.vb 3.24KB
  138. graphQL-main/src/graphMsg/Message/TermIndexMsg.vb 2.17KB
  139. graphQL-main/src/graphMsg/SeekIndex.vb 6.8KB
  140. graphQL-main/src/graphMsg/StorageProvider.vb 4.62KB
  141. graphQL-main/src/graphMsg/StreamEmit.vb 11.5KB
  142. graphQL-main/src/graphMsg/graphMsg.vbproj 3.29KB
  143. graphQL-main/src/graphMySQL/
  144. graphQL-main/src/graphMySQL/ColorHashCode.vb 3.72KB
  145. graphQL-main/src/graphMySQL/KnowledgeLink.vb 2.12KB
  146. graphQL-main/src/graphMySQL/KnowlegdeBuilder.vb 17KB
  147. graphQL-main/src/graphMySQL/SharedSSHCode/
  148. graphQL-main/src/graphMySQL/SharedSSHCode/SSH.vb 1.1KB
  149. graphQL-main/src/graphMySQL/SharedSSHCode/SharedSSHCode.projitems 645B
  150. graphQL-main/src/graphMySQL/SharedSSHCode/SharedSSHCode.shproj 1.02KB
  151. graphQL-main/src/graphMySQL/SignatureVerifycation.vb 1.66KB
  152. graphQL-main/src/graphMySQL/graphMySQL.vb 8.21KB
  153. graphQL-main/src/graphMySQL/graphMySQL.vbproj 1.19KB
  154. graphQL-main/src/graphMySQL/graphdb/
  155. graphQL-main/src/graphMySQL/graphdb/graph.vb 11.26KB
  156. graphQL-main/src/graphMySQL/graphdb/graphdb.projitems 923B
  157. graphQL-main/src/graphMySQL/graphdb/graphdb.shproj 1.02KB
  158. graphQL-main/src/graphMySQL/graphdb/hash_index.vb 8.67KB
  159. graphQL-main/src/graphMySQL/graphdb/knowledge.vb 13.53KB
  160. graphQL-main/src/graphMySQL/graphdb/knowledge_cache.vb 9.3KB
  161. graphQL-main/src/graphMySQL/graphdb/knowledge_vocabulary.vb 13.09KB
  162. graphQL-main/src/graphMySQL/graphdb/mysql_model.cmd 204B
  163. graphQL-main/src/graphMySQL/graphdbMySQL.vb 2.53KB
  164. graphQL-main/src/graphMySQL/mysql.vb 1.83KB
  165. graphQL-main/src/graphQL.sln 71.73KB
  166. graphQL-main/src/graphQL/
  167. graphQL-main/src/graphQL/Graph/
  168. graphQL-main/src/graphQL/Graph/Association.vb 2.23KB
  169. graphQL-main/src/graphQL/Graph/EvidenceGraph.vb 9.39KB
  170. graphQL-main/src/graphQL/Graph/EvidencePool.vb 6.88KB
  171. graphQL-main/src/graphQL/Graph/GraphActions.vb 5.86KB
  172. graphQL-main/src/graphQL/Graph/GraphModel.vb 2.03KB
  173. graphQL-main/src/graphQL/Graph/GraphPool.vb 9.48KB
  174. graphQL-main/src/graphQL/Graph/Knowledge.vb 3.36KB
  175. graphQL-main/src/graphQL/KnowledgeBuilder/
  176. graphQL-main/src/graphQL/KnowledgeBuilder/CreateKnowledge.vb 8.72KB
  177. graphQL-main/src/graphQL/KnowledgeBuilder/CreateNetwork.vb 5.57KB
  178. graphQL-main/src/graphQL/KnowledgeBuilder/HubAlignment.vb 2.13KB
  179. graphQL-main/src/graphQL/KnowledgeBuilder/KnowledgeAlignment.vb 4.11KB
  180. graphQL-main/src/graphQL/KnowledgeBuilder/KnowledgeFrameRow.vb 4.49KB
  181. graphQL-main/src/graphQL/KnowledgeDescription.vb 2.56KB
  182. graphQL-main/src/graphQL/KnowledgeTerm.vb 5.68KB
  183. graphQL-main/src/graphQL/Relationship.vb 1.52KB
  184. graphQL-main/src/graphQL/graphQL.vbproj 3.09KB
  185. graphQL-main/src/graphR/
  186. graphQL-main/src/graphR/FieldParser.vb 6.94KB
  187. graphQL-main/src/graphR/KnowledgeGraph.vb 16.78KB
  188. graphQL-main/src/graphR/MsgFile.vb 7.69KB
  189. graphQL-main/src/graphR/Query.vb 7.36KB
  190. graphQL-main/src/graphR/graphMySQL.vb 16.27KB
  191. graphQL-main/src/graphR/graphR.vbproj 4.53KB
  192. graphQL-main/src/graphR/mysqlDatabase.vb 20.31KB
  193. graphQL-main/src/graphR/networkGraph/
  194. graphQL-main/src/graphR/networkGraph/umapKnowledge.vb 3.22KB
  195. graphQL-main/src/graphR/zzz.vb 1.56KB
  196. graphQL-main/src/mysqli/
  197. graphQL-main/src/tumblr_inline_mqvdlydGCp1qz4rgp.png 123.39KB
  198. graphQL-main/src/web/
  199. graphQL-main/src/web/.vscode/
  200. graphQL-main/src/web/.vscode/settings.json 38B
  201. graphQL-main/src/web/etc/
  202. graphQL-main/src/web/etc/accessController.php 232B
  203. graphQL-main/src/web/etc/apache_configs/
  204. graphQL-main/src/web/etc/apache_configs/site.conf 600B
  205. graphQL-main/src/web/etc/bootstrap.php 571B
  206. graphQL-main/src/web/etc/config.php 1.47KB
  207. graphQL-main/src/web/etc/httpd.sh 993B
  208. graphQL-main/src/web/etc/php.ini 61.2KB
  209. graphQL-main/src/web/framework/
  210. graphQL-main/src/web/framework/php.NET/
  211. graphQL-main/src/web/mysql/
  212. graphQL-main/src/web/mysql/designer.png 31.02KB
  213. graphQL-main/src/web/mysql/export.cmd 182B
  214. graphQL-main/src/web/mysql/graphdb.mwb 20.42KB
  215. graphQL-main/src/web/mysql/graphdb.sql 7.31KB
  216. graphQL-main/src/web/mysql/graphdb/
  217. graphQL-main/src/web/mysql/graphdb/graph.vb 19.33KB
  218. graphQL-main/src/web/mysql/graphdb/hash_index.vb 14.46KB
  219. graphQL-main/src/web/mysql/graphdb/knowledge.vb 20.36KB
  220. graphQL-main/src/web/mysql/graphdb/knowledge_vocabulary.vb 23.06KB
  221. graphQL-main/src/web/mysql/graphdb_upgrade.sql 7.71KB
  222. graphQL-main/src/web/mysql/graphdb_upgrades.report.md 2.64KB
  223. graphQL-main/src/web/mysql/text_mining.mwb 14.14KB
  224. graphQL-main/src/web/mysql/text_mining.mwb.bak 14.02KB
  225. graphQL-main/src/web/mysql/text_mining.sql 5.2KB
  226. graphQL-main/src/web/mysql/text_mining_current.sql 2.63KB
  227. graphQL-main/src/web/mysql/text_mining_current_upgrades_to_text_mining.schema_compares.report.md 1.22KB
  228. graphQL-main/src/web/mysql/update.cmd 574B
  229. graphQL-main/src/web/readme.txt 37B
  230. graphQL-main/src/web/src/
  231. graphQL-main/src/web/src/.htaccess 217B
  232. graphQL-main/src/web/src/index.php 302B
  233. graphQL-main/src/web/src/manifest.json 300B
  234. graphQL-main/src/web/src/mining.php 6.65KB
  235. graphQL-main/src/web/views/
  236. graphQL-main/src/web/views/index.html
  237. graphQL-main/test/
  238. graphQL-main/test/evidenceGraphTest/
  239. graphQL-main/test/evidenceGraphTest/aspirin.graph 5.42KB
  240. graphQL-main/test/evidenceGraphTest/createDb.py 1.54KB
  241. graphQL-main/test/evidenceGraphTest/graphTest.R 1.12KB
  242. graphQL-main/test/evidenceGraphTest/network/
  243. graphQL-main/test/evidenceGraphTest/network/graph_1.png 30.47KB
  244. graphQL-main/test/evidenceGraphTest/network/graph_1/
  245. graphQL-main/test/evidenceGraphTest/network/graph_1/meta.json 148B
  246. graphQL-main/test/evidenceGraphTest/network/graph_1/network-edges.csv 160B
  247. graphQL-main/test/evidenceGraphTest/network/graph_1/nodes.csv 528B
  248. graphQL-main/test/evidenceGraphTest/network/graph_1_terms.csv 60B
  249. graphQL-main/test/evidenceGraphTest/network/graph_2.png 37.58KB
  250. graphQL-main/test/evidenceGraphTest/network/graph_2/
  251. graphQL-main/test/evidenceGraphTest/network/graph_2/meta.json 148B
  252. graphQL-main/test/evidenceGraphTest/network/graph_2/network-edges.csv 160B
  253. graphQL-main/test/evidenceGraphTest/network/graph_2/nodes.csv 528B
  254. graphQL-main/test/evidenceGraphTest/network/graph_2_terms.csv 60B
  255. graphQL-main/test/evidenceGraphTest/network/graph_3.png 26.87KB
  256. graphQL-main/test/evidenceGraphTest/network/graph_3/
  257. graphQL-main/test/evidenceGraphTest/network/graph_3/meta.json 148B
  258. graphQL-main/test/evidenceGraphTest/network/graph_3/network-edges.csv 93B
  259. graphQL-main/test/evidenceGraphTest/network/graph_3/nodes.csv 404B
  260. graphQL-main/test/evidenceGraphTest/network/graph_3_terms.csv 43B
  261. graphQL-main/test/evidenceGraphTest/network/graph_4.png 26.83KB
  262. graphQL-main/test/evidenceGraphTest/network/graph_4/
  263. graphQL-main/test/evidenceGraphTest/network/graph_4/meta.json 148B
  264. graphQL-main/test/evidenceGraphTest/network/graph_4/network-edges.csv 93B
  265. graphQL-main/test/evidenceGraphTest/network/graph_4/nodes.csv 412B
  266. graphQL-main/test/evidenceGraphTest/network/graph_4_terms.csv 43B
  267. graphQL-main/test/evidenceGraphTest/network/raw.png 44.77KB
  268. graphQL-main/test/evidenceGraphTest/network/raw/
  269. graphQL-main/test/evidenceGraphTest/network/raw/meta.json 148B
  270. graphQL-main/test/evidenceGraphTest/network/raw/network-edges.csv 617B
  271. graphQL-main/test/evidenceGraphTest/network/raw/nodes.csv 386B
  272. graphQL-main/test/evidenceGraphTest/network/raw_terms.csv 162B
  273. graphQL-main/test/getEdgeSource.R 359B
  274. graphQL-main/test/graphTest.R 1.11KB
  275. graphQL-main/test/hash_test.R 2.14KB
  276. graphQL-main/test/joinMultiples/
  277. graphQL-main/test/joinMultiples/exportTables.py 241B
  278. graphQL-main/test/joinMultiples/join.py 205B
  279. graphQL-main/test/joinTest/
  280. graphQL-main/test/joinTest/aspirin.graph 10.29KB
  281. graphQL-main/test/joinTest/aspirin_graph/
  282. graphQL-main/test/joinTest/aspirin_graph/meta.json 148B
  283. graphQL-main/test/joinTest/aspirin_graph/network-edges.csv 17.66KB
  284. graphQL-main/test/joinTest/aspirin_graph/nodes.csv 6.96KB
  285. graphQL-main/test/joinTest/createDb.py 2.18KB
  286. graphQL-main/test/joinTest/dbTest.py 673B
  287. graphQL-main/test/joinTest/kb1.graph 7.57KB
  288. graphQL-main/test/joinTest/kb2.graph 5.21KB
  289. graphQL-main/test/joinTest/knowledges.xls 3.76KB
  290. graphQL-main/test/joinTest/knowledges_raw.xls 3.98KB
  291. graphQL-main/test/joinTest/network_graph.py 406B
  292. graphQL-main/test/knowledges/
  293. graphQL-main/test/knowledges/aspirin.graph 1.02MB
  294. graphQL-main/test/knowledges/aspirin_graph/
  295. graphQL-main/test/knowledges/aspirin_graph/meta.json 148B
  296. graphQL-main/test/knowledges/aspirin_graph/network-edges.csv 17.61KB
  297. graphQL-main/test/knowledges/aspirin_graph/nodes.csv 6.96KB
  298. graphQL-main/test/knowledges/chebi.csv 331B
  299. graphQL-main/test/knowledges/createDb.py 1.93KB
  300. graphQL-main/test/knowledges/dbTest.py 673B
  301. graphQL-main/test/knowledges/graph.svg 85.39KB
  302. graphQL-main/test/knowledges/hmdb.csv 409B
  303. graphQL-main/test/knowledges/isoleucine/
  304. graphQL-main/test/knowledges/isoleucine/chebi.csv 457B
  305. graphQL-main/test/knowledges/isoleucine/hmdb.csv 1.93KB
  306. graphQL-main/test/knowledges/isoleucine/kegg.csv 159B
  307. graphQL-main/test/knowledges/kegg.csv 257B
  308. graphQL-main/test/knowledges/knowledges.xls 3.66KB
  309. graphQL-main/test/knowledges/knowledges_raw.xls 4.29KB
  310. graphQL-main/test/knowledges/leucine/
  311. graphQL-main/test/knowledges/leucine/hmdb.csv 1.11KB
  312. graphQL-main/test/knowledges/leucine/kegg.csv 288B
  313. graphQL-main/test/knowledges/network_graph.py 406B
  314. graphQL-main/test/knowledges/viewdb.py 432B
  315. graphQL-main/test/knowledges/water.csv 195B
  316. graphQL-main/test/knowledges/water_chebi.csv 193B
  317. graphQL-main/test/network/
  318. graphQL-main/test/network/graph_1.png 66.71KB
  319. graphQL-main/test/network/graph_1/
  320. graphQL-main/test/network/graph_1/meta.json 148B
  321. graphQL-main/test/network/graph_1/network-edges.csv 745B
  322. graphQL-main/test/network/graph_1/nodes.csv 2.75KB
  323. graphQL-main/test/network/graph_1_terms.csv 12B
  324. graphQL-main/test/network/graph_2.png 279.14KB
  325. graphQL-main/test/network/graph_2/
  326. graphQL-main/test/network/graph_2/meta.json 148B
  327. graphQL-main/test/network/graph_2/network-edges.csv 6.41KB
  328. graphQL-main/test/network/graph_2/nodes.csv 17.67KB
  329. graphQL-main/test/network/graph_2_terms.csv 12B
  330. graphQL-main/test/network/graph_3.png 113.35KB
  331. graphQL-main/test/network/graph_3/
  332. graphQL-main/test/network/graph_3/meta.json 148B
  333. graphQL-main/test/network/graph_3/network-edges.csv 1.82KB
  334. graphQL-main/test/network/graph_3/nodes.csv 4.83KB
  335. graphQL-main/test/network/graph_3_terms.csv 12B
  336. graphQL-main/test/network/raw.png 305.88KB
  337. graphQL-main/test/network/raw/
  338. graphQL-main/test/network/raw/meta.json 148B
  339. graphQL-main/test/network/raw/network-edges.csv 18.76KB
  340. graphQL-main/test/network/raw/nodes.csv 8.99KB
  341. graphQL-main/test/network/raw_terms.csv
  342. graphQL-main/test/network_graph/
  343. graphQL-main/test/network_graph/cluster.csv 33.02KB
  344. graphQL-main/test/network_graph/clustering.R 557B
  345. graphQL-main/test/network_graph/facebook_combined.txt 834.36KB
  346. graphQL-main/test/network_graph/graph/
  347. graphQL-main/test/network_graph/graph/meta.json 148B
  348. graphQL-main/test/network_graph/graph/network-edges.csv 2.15MB
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资源介绍:

在脚本中导入R#包，然后创建一个空的图形数据库： import graphQL kb = MsgFile::open()
RESEARCH Open Access
cytoHubba: identifying hub objects and sub-
networks from complex interactome
Chia-Hao Chin
1†
, Shu-Hwa Chen
2†
, Hsin-Hung Wu
6
, Chin-Wen Ho
5
, Ming-Tat Ko
2,6*
, Chung-Yen Lin
2,3,4*
From Asia Pacific Bioinformatics Network (APBioNet) Thirteenth International Conference on Bioinformatics
(InCoB2014)
Sydney, Australia. 31 July - 2 August 2014
Abstract
Background: Network is a useful way for presenting many types of biological data including protein-protein
interactions, gene regulations, cellular pathways, and signal transductions. We can measure nodes by their network
features to infer their importance in the network, and it can help us identify central elements of biological
networks.
Results: We introduce a novel Cytoscape plugin cytoHubba for ranking nod es in a network by their network
features. CytoHubba provides 11 topological analysis methods including Degree, Edge Percolated Component,
Maximum Neighborhood Component, Density of Maximum Neighborhood Component, Maximal Clique Centrality
and six centralities (Bottleneck, EcCentricity, Closeness, Radiality, Betweenness, and Stress) ba sed on shortest paths.
Among the eleven methods, the new proposed method, MCC, has a better performance on the precision of
predicting essential proteins from the yeast PPI network.
Conclusions: CytoHubba provide a user-friendly interface to explore important nodes in biological networks. It
computes all eleven methods in one stop shopping way . Besides, researchers are able to combine cytoHubba with
and other plugins into a novel analysi s scheme. The network and sub-networks caught by this topological analysis
strategy will lead to new insights on essential regulatory networks and protein drug targets for experimental
biologists. According to cytoscape plugin download statistics, the accumulated number of cytoHubba is around
6,700 times since 2010.
Background
Recent breakthroughs in high-throughput techniques
lead experimental data deluges in genomics, proteomics,
transcriptomics, metabolomics and interactomics. These
data can be represented as netwo rks, in which the nodes
as surrogates for proteins, metabolites, or transcripts, are
connected by edges to show the interactions, reactions,
or regulations among nodes. Network analysis can help
us understand the fu nction of an individual node and th e
collaboration between other nodes. For example, network
centralities rank nodes of a biological network acco rding
to a given importance concept, and Jeong et al.applied
this method on a protein-protein interaction network of
baker’s yeast (Saccharomyces cerevisiae)[1].Theyfound
tha t the degree of a protein correlates with the essential-
ity of its gene; in other word, proteins with higher
degrees are more likely to be essential proteins.
Cytoscape [2] is an open platform with many plugins to
expand both the visualization options and the network
analysis power. Via Cytoscape, the graphical view of a net-
work is easy accessed, and multiple layers o f information
including large-scale, genome-wise experiments, and pro-
tein function annotations can be granted on the interac-
tome. Several Cytoscape plugins can score and rank the
nodes by network features. For example, NetworkAnalyzer
[3] and CentiScaPe [4] computes various topological net-
work parameters for undirected and/ or directed networks.
* Correspondence: mtko@iis.sinica.edu.tw; cylin@iis.sinica.edu.tw
† Contributed equally
2
Institute of Information Science, Academia Sinica, No. 128 Academia Rd.,
Sec. 2, Taipei 115, Taiwan
Full list of author information is available at the end of the article
Chin et al. BMC Systems Biology 2014, 8(Suppl 4):S11
http://www.biomedcentral.com/1752-0509/8/S4/S11
© 2014 Chin et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Co mmons
Attribution License (http://creative commons.org/licenses/by/4.0), which permits unrestricted use, distribution, and re production in
any medium, provided the original work is properly cited. The Creative Commons Public Domain Ded ication waiv er (http://
creativecom mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
These plugins provide more centrality measures than
other commonly used, but some other important features
and recent developed methods are not included. Different
methods focus on different topological features, or similar
features with different scoring strategies. To make the net-
work analysis easier for biologists to utilize more network
features, we compose cytoHub ba plugin to execute our
newly developed algorithms and several popular algo-
rithms. The enhanced node retrieving function in cyto-
Hubba control panel helps researchers to search and
explore the network and to extract user interesting
subnetwork.
Results and discussion
The Usage of cytoHubba
CytoHubba provides a simple interface to analyze a net-
work with eleven scoring methods. First, scores from all
eleven methods are granted to each node in a preloaded
PPI network by e xecuting “ compute hubba result” func-
tion in the cytoHubb a options in cytoscape menu bar
[plugins]. Next, top-ranked nodes of a particular scoring
method are retrie ved from the cytoHubba tab in Cytos-
cape control panel, listed in the result panel, and the sub-
graph of these selected nodes are shown in the main win-
dow with a color scheme from highly essential (re d) to
essential (green). The sub-graph of essential nodes is
extendable to includ e nodes that directly interact wit h
these top-ranked nodes by the option of “check first stage
node“ in control panel | hubba. Network topological fea-
tures of nodes ar e retrievable in the data panel as options
of node attributes. Tutorials and demo video are available
in the website (http://hub.iis.sinica.edu.tw/cytohubba).
An example of cytoHubba result using the Cytoscape
example dataset galFiltered.cys is shown in Figure 1.
CytoHubba control panel is also a handy tool to
retrieve subnetwork from the whole big PPI set. A list
of nodes can be extracted by an ID list from the whole
hubba-computed network. This manipulation can be
extended to include direct linking partners (check on
the option “check first st age node” ), saved, and re-sub-
mitted to cytoHubba to evaluate the node essentiality on
the selected sub-network. For th ose nodes with no
Figure 1 T wo s cree n-sh ots showing centra lity analysi s wi th cytoHu bba.(A)AcytoHubba analysis result using the example dataset
galFiltered.cys in the Cytoscape. After the calculation (from the Cytoscape menu bar [plugins] ® cytoHubba ® compute hubba result), top 10
essential nodes ranked by MCC scores were selected (control panel | hubba | ® select nodes® make check on “hubba nodes"® select a
method from “choose a ranking method” and determine how many nodes are selected from “top group” ® [submit])from the yeast network.
(B) Node and edge annotations are accessible through Cytoscape Data Panel.
Chin et al. BMC Systems Biology 2014, 8(Suppl 4):S11
http://www.biomedcentral.com/1752-0509/8/S4/S11
Page 2 of 7
direct link in between, cyt oHubba provides a shortest
path detect ion tool (“display the shortest path” on the
display option). All connectible but not direct connected
node pairs in a network, retrieved either by ID search or
by top-ranked in to pological feature score, ar e con-
nected by dotted-lines with number of the smallest edge
number (shortest path) to make thi s link. The stepping-
stone nodes and edges composing the shortest path will
be expanded by a mouse right-click action. Comparing
with the other cytoscape plugin Short estPath which
sketches the path between two nodes [5], cytoHubba
fetches the s hortes t path among a group of nodes. This
abstractive view provides the distance among essential
nodes.
The performance
The studies of protein-protein interactions will be more
powerful whe n the interactome co verage increases. How-
ever, the complexity of the network will also increase,
that always hampers computation tasks. After the optimi-
zation on the programs, cytoHubba is able to complete all
eleven analysis of a small network (e.g. 330 nodes, 360
edges), a mid dle size one (7,600 nodes, 20,000 edges) and
a large set (11,500 nodes, 33,600 edges) in few seconds,
around 30 seco nds a nd fe w minute s, respe ctively, on a
common desktop/ notebook (Cytoscape version 2.6.x /
2.7.x / 2.8.x on Window 7/8 platform; hardware spec as
Intel i7, 8 GB of RAM). CytoHubba has been updated
several times since 2009 (from v1.0 to v1.6). It is freely
accessible in Cyt oscape App store (http://apps.cyt oscape.
org/apps/cytohubba). The accumul ated dow nloading
number is around 6,500 (http://chianti.ucsd.edu/cyto_-
web/plugins/plugindownloadstatistics.php, statistics on
May 2014). And it is used widely to analyze cancer meta-
bolic network[6], innate immune network[7], complex
biofilm communities[8] and so on.
Validation by Predict yeast essential proteins
We use cytoHubba to score all proteins in the yeast pro -
tein interaction network by the e leven methods. DIP
database (http://dip.do e-mbi.ucla.edu, version: 20140117)
is composed of 4,908 proteins and 21,732 interactions
after removing self-interactions and redundant records.
The essential protein lists are collected from Saccharo-
myces Genome Deletion Project (SGDP) and Saccharo-
myces Genome Databa se (SGD). There are 1,122 and
1,280 proteins defined as essential pr oteins by SGDP and
SGD respectively. We use the union set (1,297 proteins)
for verifying the performance of the predictions.
The statistics of yeast PPIs are shown in Table 1. Twenty
three percent (=1148/4908) of the proteins in this network
are defined as esse ntial proteins in the dataset. We call a
nodeishigh-degreeifthenumberofitsneighborsisgreater
than a threshold; otherwise we call it a low-degree node,
where the threshold is the maximum integer such that
2 ×
v∈V,De
g
(v)>t
Deg(v) >
v∈V
Deg(v
)
.Thethreshold
of the PPI network (DIP 20140117) used in this paper is 21.
As shown as Table 1, there are 4,396 proteins in low-degree
category and 512 proteins in high-degree category, in which
908proteinsand214proteins are essential proteins.
Table 2 shows the performance (precision of predic-
tion) to predict essential proteins in top × ranked node
identified by each method. In most methods, the preci-
sion decrea ses w hen the selected n umber increase.
Besides, a local-based method is better than global-
based method in discovering yeast essential proteins. To
further understand the preference of network feature
selected by different methods, we compare the number
of proteins in common in the top 100 ranked of any
two scoring methods (Table 3). The top 100 ranked list
of Closeness is most identical to the result of Radiality
(99%), indicate that the network features detected by
these two methods are very similar. MCC shares less
common components to oth er methods (less than 30%).
The top 100 ranked proteins suggested by DMNC do
not appear in other methods’ list except MCC (30%),
means this method detect different features from the
other ten methods.
As shown as Table 1, in the y east PPIs, 21% of pro-
teins in low -degree cat egory are essential prote ins and
42% of proteins are essential proteins in high-degree
category. Accordingly, if we pick a protein randomly
from the high-degree pool, the probability that an essen-
tial protein being chosen is 0.42, an d 0.21 from a low-
degree protein p ool respectively. Table 4 is the number
of essential proteins found in the top × list with low-
degree feature. Fo r example, among the top 30 protein
ranked by DMNC, 29 proteins are in the l ow-degree
category, in which 21 out of 29 proteins are essential
proteins. Methods except DMNC, MCC and EcCentri-
city tend to assign higher scores to a node when it owns
more neighbors while almost no any low-degree pro-
teins are found in their top × list. In other word, these
methods cannot find low-degree essential proteins.
Conclusions
In this study, we im plement o ur network scoring meth-
ods, MCC, MNC and DMNC, and eight other popular
Table 1 Statistics of Yeast PPIs used in this study (DIP
database, 20140117 released set), in the aspects of
degree and essentiality
Total Low-degree High-degree
The number of proteins 4908 4396 512
Essential proteins (%) 1148 908 214
(23%) (21%) (42%)
Chin et al. BMC Systems Biology 2014, 8(Suppl 4):S11
http://www.biomedcentral.com/1752-0509/8/S4/S11
Page 3 of 7
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