{"id":582,"date":"2022-08-10T00:20:06","date_gmt":"2022-08-09T16:20:06","guid":{"rendered":"https:\/\/sysmed-net.lab.nycu.edu.tw\/?page_id=582"},"modified":"2026-05-15T10:38:37","modified_gmt":"2026-05-15T02:38:37","slug":"lab-intro","status":"publish","type":"page","link":"https:\/\/sysmed-net.lab.nycu.edu.tw\/index.php\/lab-intro\/","title":{"rendered":"About lab"},"content":{"rendered":"\n
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\u57f7\u884c\u4e2d\u8a08\u756b Ongoing Projects<\/strong><\/h2>\n\n\n\n
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2024.08-2027.07<\/strong><\/h2>\n\n\n\n

\u570b\u5bb6\u79d1\u5b78\u53ca\u6280\u8853\u59d4\u54e1\u6703<\/strong><\/strong>\u5c08\u984c\u7814\u7a76\u8a08\u756b[\u512a\u79c0\u5e74\u8f15\u5b78\u8005]<\/strong><\/h2>\n\n\n\n

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Construction of multi-scale individual-specific networks from multi-omics data for identifying personalized pathway signatures and repurposable drug candidates in head and neck cancer patients<\/p>\n<\/div><\/div>\n\n\n\n

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2026.08-2029.07<\/strong><\/strong><\/h2>\n\n\n\n

\u570b\u5bb6\u79d1\u5b78\u53ca\u6280\u8853\u59d4\u54e1\u6703<\/strong><\/strong>\u5c08\u984c\u7814\u7a76\u8a08\u756b[\u5433\u5927\u7337\u5148\u751f\u7d00\u5ff5\u734e\u8a08\u756b]<\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/h2>\n\n\n\n

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\u8005\u7279\u7570\u6027\u816b\u7624\u5fae\u74b0\u5883\u4e26\u9810\u6e2c\u6297\u764c\u85e5\u7269\u8207\u7d44\u5408\u7642\u6cd5\u4e4b\u7642\u6548<\/p>\n\n\n\n

Deciphering intra- and inter-cellular pathway communication networks from bulk tissue transcriptomes for patient-specific tumor microenvironment modeling and prediction of anticancer drug response and combinations<\/p>\n<\/div><\/div>\n<\/div><\/section><\/div>\n\n\n\n

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2025.01-2027.12<\/strong><\/h2>\n\n\n\n

\u8ca1\u5718\u6cd5\u4eba\u570b\u5bb6\u885b\u751f\u7814\u7a76\u9662<\/strong><\/strong>\u6574\u5408\u6027\u91ab\u85e5\u885b\u751f\u79d1\u6280\u7814\u7a76\u8a08\u756b<\/strong><\/h2>\n\n\n\n

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Personalized network-based machine learning model builds digital twins for predicting anti-cancer drug efficacy in head and neck cancer patients<\/p>\n<\/div><\/div>\n<\/div><\/section><\/div>\n\n\n\n

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\u7814\u7a76\u65b9\u5411 Research Directions<\/strong><\/h2>\n\n\n\n
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SysMed.net \u5be6\u9a57\u5ba4\u81f4\u529b\u65bc\u5c07\u7d30\u80de\u5167\u5206\u5b50\u9593\u7684\u95dc\u4fc2\u5efa\u69cb\u6210\u751f\u7269\u7db2\u8def\u8abf\u63a7\u6a21\u578b\uff0c\u7528\u65bc\u63a2\u8a0e\u7d30\u80de\u5167\u5206\u5b50\u9593\u5982\u4f55\u900f\u904e\u4ea4\u4e92\u4f5c\u7528\u4f86\u8abf\u63a7\u7d30\u80de\u884c\u70ba\uff0c\u9032\u4e00\u6b65\u7d50\u5408\u75be\u75c5\u57fa\u56e0\u9ad4\/\u86cb\u767d\u8cea\u9ad4\u7b49\u591a\u9ad4\u5b78\u8cc7\u6599\u4f86\u63a2\u7a76\u75be\u75c5\u7684\u767c\u751f\u539f\u56e0\u3002\u6211\u5011\u5df2\u767c\u8868\u591a\u9805\u65b0\u6982\u5ff5\u53ca\u65b0\u6280\u8853\uff0c\u4f8b\u5982\uff1a\u86cb\u767d\u8cea\u9593\u4ea4\u4e92\u4f5c\u7528\u5bb6\u65cf (PPI family\uff1b\u767c\u8868\u65bcNucleic Acids Research, 2009<\/em>)\u548c\u86cb\u767d\u8cea\u6a21\u7d44\u5bb6\u65cf (MoNetFamily\uff1b\u767c\u8868\u65bcNucleic Acids Research, 2012<\/em>)\uff1b\u900f\u904e\u6620\u5c04\u4e0d\u540c\u7269\u7a2e\u4e4b\u4ea4\u4e92\u4f5c\u7528\u5be6\u9a57\u8cc7\u6599\u65bc\u7279\u5b9a\u7269\u7a2e\uff0c\u53ef\u7cbe\u6e96\u9810\u6e2c\u86cb\u767d\u8cea\u9593\u4ea4\u4e92\u4f5c\u7528\u3001\u86cb\u767d\u8cea\u6a21\u7d44\u53ca\u6a21\u7d44\u9593\u4ea4\u4e92\u4f5c\u7528\uff0c\u9032\u800c\u6a21\u64ec\u771f\u5be6\u7d30\u80de\u5167\u7684\u751f\u7269\u7db2\u8def\u3002<\/p>\n<\/div><\/div>\n<\/div><\/section><\/div>\n\n\n\n

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\u69cb\u5efa\u60a3\u8005\u6578\u4f4d\u5206\u8eab\u61c9\u7528\u65bc\u500b\u4eba\u5316\u81e8\u5e8a\u8a3a\u65b7\u53ca\u6cbb\u7642<\/strong><\/strong><\/strong><\/h2>\n\n\n\n

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\u91dd\u5c0d\u500b\u9ad4\u60a3\u8005\u3001\u55ae\u7d30\u80de\u548c\u85e5\u7269\u57fa\u56e0\u7d44\u5b78\u5efa\u7acb\u764c\u75c7\u805a\u7126\u7db2\u8def\u6a21\u578b\uff0c\u7528\u65bc\u63a2\u8a0e\u816b\u7624\u5167\u7570\u8cea\u6027\u6a5f\u5236\uff0c\u8f14\u52a9\u91ab\u85e5\u7522\u696d\u958b\u767c\u500b\u4eba\u5316\u751f\u7269\u6a19\u8a18\u53ca\u65b0\u9069\u61c9\u75c7\u85e5\u7269(\u6216\u7d44\u5408\u85e5\u7269)\uff0c\u5e0c\u5180\u672a\u4f86\u80fd\u767c\u5c55\u6210\u764c\u75c7\u81e8\u5e8a\u6cbb\u7642\u6c7a\u7b56\u4e4b\u8f14\u52a9\u5de5\u5177\u3002\u8fd1\u5e74\u4f86\uff0c\u6211\u5011\u5718\u968a\u8207\u4e09\u7e3d\u53ca\u5317\u91ab\u651c\u624b\u9032\u884c\u81e8\u5e8a\u8a66\u9a57\u5408\u4f5c\uff0c\u900f\u904e\u60a3\u8005\u500b\u9ad4\u6216\u55ae\u7d30\u80de\u4e4b\u591a\u9ad4\u5b78\u8cc7\u6599\uff0c\u4ee5\u60a3\u8005\u500b\u9ad4\u7db2\u8def\u6a21\u578b (SWEET\uff1b\u767c\u8868\u65bcBriefings in Bioinformatics<\/em>, 2023<\/em>)\u8207\u958b\u767c\u4e2d\u7684\u55ae\u7d30\u80de\u7db2\u8def\u6a21\u578b (SINUM\uff1b\u767c\u8868\u65bcBMC Bioinformatics<\/em>, 2024 & CHOSEN) \u70ba\u57fa\u790e\uff0c\u900f\u904e\u6240\u958b\u767c\u4e4b\u57fa\u56e0\u96c6\u76f8\u95dc\u6027\u5bcc\u96c6\u5206\u6790 \u65b9\u6cd5(Gscore \uff1b\u767c\u8868\u65bcNucleic Acids Research<\/em>, 2024)\u8fa8\u8b58\u6cbb\u7642\u85e5\u7269\u4e4b\u4f5c\u7528\u751f\u5316\u9014\u5f91\uff0c\u9032\u4e00\u6b65\u7d50\u5408\u6a5f\u5668\/\u6df1\u5ea6\u5b78\u7fd2\u69cb\u5efa\u60a3\u8005\u6578\u4f4d\u5206\u8eab (Digital Twin)\uff0c\u7528\u65bc\u767c\u5c55\u6b21\u4e16\u4ee3\u7cbe\u6e96\u91ab\u7642\u6c7a\u7b56\u8f14\u52a9\u7cfb\u7d71\uff0c\u63d0\u4f9b\u60a3\u8005\u500b\u4eba\u5316\u75be\u75c5\u9032\u7a0b\u6a21\u64ec\u3001\u85e5\u7269\u7642\u6548\u9810\u6e2c\u4ee5\u53ca\u9810\u5f8c\u8a55\u4f30\u3002<\/p>\n<\/div><\/div>\n<\/div><\/section><\/div>\n\n\n\n

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\u4e3b\u8981\u7814\u7a76\u6210\u679c Research <\/strong>Achievements<\/h2>\n\n\n\n
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\u5f9e\u57fa\u56e0\/\u86cb\u767d\u8cea\/\u7d30\u80de\u9593\u4ea4\u4e92\u4f5c\u7528\u5230\u5efa\u7acb\u75be\u75c5\u8abf\u63a7\u7db2\u8def\u6a21\u578b<\/strong><\/h2>\n\n\n\n

\u96a8\u8457\u5404\u7a2e\u7d44\u5b78\u8cc7\u6599(Omics data)\u7684\u5feb\u901f\u589e\u9577\uff0c\u5982\u4f55\u6574\u5408\u9019\u4e9b\u8cc7\u6599\u8207\u751f\u7269\u7db2\u8def\u4f86\u63a2\u8a0e\u75be\u75c5\u6a5f\u5236\u6210\u70ba\u7576\u524d\u7684\u91cd\u8981\u8ab2\u984c\u3002\u7136\u800c\u7d55\u5927\u90e8\u5206\u7269\u7a2e\u4e4b\u4ea4\u4e92\u4f5c\u7528\u9ad4(Interactomes)\u4e26\u4e0d\u5b8c\u6574\uff1b\u975c\u614b\u7684\u7db2\u8def\u7121\u6cd5\u53cd\u6620\u7d30\u80de\u5167\u7684\u8b8a\u52d5\u5c1a\u672a\u88ab\u89e3\u6c7a\u3002\u6211\u5011\u904e\u53bb\u5df2\u767c\u8868\u591a\u9805\u65b0\u6982\u5ff5\u53ca\u65b0\u6280\u8853\uff1a<\/p>\n<\/div><\/div>\n\n\n\n

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  1. \u900f\u904e\u6620\u5c04\u4e0d\u540c\u7269\u7a2e\u4e4b\u4ea4\u4e92\u4f5c\u7528\u5be6\u9a57\u8cc7\u6599\u65bc\u7279\u5b9a\u7269\u7a2e\uff0c\u53ef\u7cbe\u6e96\u9810\u6e2c\u86cb\u767d\u8cea\u9593\u4ea4\u4e92\u4f5c\u7528\u3001\u86cb\u767d\u8cea\u6a21\u7d44\u53ca\u6a21\u7d44\u9593\u4ea4\u4e92\u4f5c\u7528\uff0c\u9032\u800c\u6a21\u64ec\u771f\u5be6\u7d30\u80de\u5167\u7684\u751f\u7269\u7db2\u8def\u3002\u86cb\u767d\u8cea\u9593\u4ea4\u4e92\u4f5c\u7528\u5bb6\u65cf(PPI family\uff1b\u767c\u8868\u65bcNucleic Acids Research, 2009<\/em>)\u548c\u86cb\u767d\u8cea\u6a21\u7d44\u5bb6\u65cf(MoNetFamily\uff1b\u767c\u8868\u65bcNucleic Acids Research, 2012<\/em>)\u3002<\/li>\n\n\n\n
  2. \u900f\u904e\u5fc5\u8981\u57fa\u56e0\u5206\u6790\u3001\u6a21\u7d44\u8b8a\u5316\u5206\u6790\u53ca\u5927\u898f\u6a21\u57fa\u56e0\u5171\u8868\u73fe\u5206\u6790\uff0c\u6211\u5011\u6210\u529f\u767c\u73fe\u86cb\u767d\u8cea\u6a21\u7d44\u5728\u751f\u5316\u7db2\u8def\u7684\u7d44\u6210\u539f\u5247\u53ca\u8b8a\u52d5\u884c\u70ba\uff08\u767c\u8868\u65bcScientific Reports, 2015<\/em>)\uff0c\u8a72\u6210\u679c\u6709\u52a9\u65bc\u751f\u7269\u5b78\u5bb6\u627e\u51fa\u7d30\u80de\u5728\u7279\u5b9a\u72c0\u614b\u4e0b\uff08\u5982\u764c\u75c7\uff09\u7684\u95dc\u9375\u57fa\u56e0\u53ca\u6f5b\u5728\u85e5\u7269\u6a19\u9776\u3002<\/li>\n\n\n\n
  3. \u6211\u5011\u8207\u5317\u91ab\u5171\u540c\u767c\u5c55\u4e00\u500b\u7cfb\u7d71\u5316\u6574\u5408\u8a08\u7b97\u6a21\u578b\uff0c\u900f\u904e\u5206\u679015\u7a2e\u764c\u75c75,922\u816b\u7624\u57fa\u56e0\u8868\u73fe\u8cc7\u6599\u53ca\u81e8\u5e8a\u8cc7\u8a0a\uff0c\u5927\u898f\u6a21\u91dd\u5c0d2,594\u500b\u4eba\u985e\u819c\u86cb\u767d\u9810\u6e2c\u4e26\u5efa\u7acb\u5176\u764c\u75c7\u819c\u86cb\u767d\u8abf\u63a7\u7db2\u8def(CaMPNets\uff1b\u767c\u8868\u65bcNature Communications, 2019<\/em>)\uff0c\u53ef\u9032\u4e00\u6b65\u7528\u65bc\u63a2\u8a0e\u764c\u75c7\u540c\u8cea\/\u7570\u8cea\u6027\u53ca\u767c\u5c5515\u7a2e\u764c\u75c7\u7684\u9810\u5f8c\u751f\u7269\u6a19\u8a18\u7d44\u8207\u85e5\u7269\u6a19\u9776\u86cb\u767d\uff1b\u6211\u5011\u66f4\u900f\u904e\u6b64\u7cfb\u7d71\u6210\u529f\u627e\u5230\u4e00\u500b\u65b0\u7a4e\u6291\u5236\u5291 (\u820a\u85e5\u65b0\u7528\uff1b\u5df2\u7372\u5f97\u5169\u9805\u5c08\u5229)\uff0c\u53ef\u6709\u6548\u6291\u5236\u4e73\u764c\u8f49\u79fb\u767c\u751f\u3002<\/li>\n\n\n\n
  4. \u6211\u5011\u8207\u65e5\u672cNVIDIA\u516c\u53f8\u5171\u540c\u5408\u4f5c\u900f\u904e\u6df1\u5ea6\u5b78\u7fd2(Deep Learning)\u958b\u767cAI \u667a\u6167\u6f14\u7b97\u6a21\u578b\uff0c\u85c9\u7531\u5206\u6790\u816b\u7624\u57fa\u56e0\u9ad4\u5927\u6578\u64da\u8cc7\u6599\u9032\u884c\u764c\u75c7\u4e9e\u578b\u7cbe\u6e96\u6aa2\u6e2c\u6280\u8853\uff0c\u53ef\u61c9\u7528\u65bc\u60a3\u8005\u764c\u75c7\u4e9e\u7fa4\u53ca\u55ae\u7d30\u80de\u5206\u985e\u751a\u81f3\u662f\u81e8\u5e8a\u8cc7\u8a0a\u9810\u6e2c(\u767c\u8868\u65bcJournal of Bioinformatics and Computational Biology, 2019<\/em>)\u3002<\/li>\n\n\n\n
  5. \u6211\u5011\u8207\u65e5\u672c\u4eac\u90fd\u5927\u5b78\u5408\u4f5c\u958b\u767c\u591a\u500b\u5e03\u723e\u7db2\u8def\u4e2d\u7684\u5171\u540c\u5438\u5f15\u5b50\u8fa8\u8b58\u6a21\u578b\uff0c\u9019\u4e9b\u6a21\u578b\u6a21\u578b\u53ef\u9032\u4e00\u6b65\u70ba\u63a2\u7d22\u816b\u7624\u7684\u540c\u8cea\u6027\u548c\u7570\u8cea\u6027\u63d0\u4f9b\u7dda\u7d22(\u767c\u8868\u65bcIEEE-ACM Transactions on Computational Biology and Bioinformatics<\/em>, 2023)\u3002<\/li>\n<\/ol>\n<\/div><\/div><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
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    1. \u70ba\u4e86\u8b58\u5225\u8207\u7279\u5b9a\u57fa\u56e0\/\u86cb\u767d\u8cea\u8868\u73fe\u8cc7\u6599\u76f8\u95dc\u7684\u751f\u5316\u9014\u5f91\uff0c\u6211\u5011\u9032\u4e00\u6b65\u958b\u767c\u4e86\u57fa\u56e0\u96c6\u76f8\u95dc\u6027\u5bcc\u96c6\u5206\u6790\u65b9\u6cd5 (Gscore\uff1b\u767c\u8868\u65bcNucleic Acids Research<\/em>, 2024)\uff0cGscore\u4ee5\u8868\u73fe\u8cc7\u6599\u5efa\u69cb\u5171\u8868\u73fe\u7db2\u8def\uff0c\u7528\u4ee5\u8b58\u5225\u76ee\u6a19\u57fa\u56e0\u6216\u57fa\u56e0\u7d44\u662f\u5426\u8207\u7279\u5b9a\u529f\u80fd\/\u751f\u5316\u9014\u5f91\u76f8\u4e92\u95dc\u806f\uff0c\u6e96\u78ba\u6027\u4e0d\u50c5\u512a\u65bc\u73fe\u6709\u65b9\u6cd5\uff0c\u540c\u6642\u80fd\u5920\u91dd\u5c0d\u53ef\u80fd\u5f71\u97ff\u76ee\u6a19\u751f\u5316\u9014\u5f91\u7684\u57fa\u56e0\u9032\u884c\u5f71\u97ff\u7a0b\u5ea6\u6392\u5e8f\uff0c\u70ba\u5f8c\u7e8c\u5be6\u9a57\u63d0\u4f9b\u6307\u5f15\uff1b\u5206\u6790\u7d50\u679c\u8b49\u5be6\u53ef\u6709\u6548\u61c9\u7528\u65bc16\u7a2e\u75be\u75c5(\u5305\u542b\u591a\u7a2e\u764c\u75c7\u3001\u795e\u7d93\u9000\u5316\u6027\u75be\u75c5\u8207COVID-19\u7b49) \u57fa\u56e0\u8868\u73fe\u8cc7\u6599\u4e4b\u751f\u5316\u9014\u5f91\u8a3b\u91cb\u548c\u5206\u6790\uff1b\u4f8b\u5982\uff1a\u6211\u5011\u8207\u6e05\u5927\u738b\u96ef\u975c\u6559\u6388\u5408\u4f5c\uff0c\u61c9\u7528\u4e0a\u8ff0\u65b9\u6cd5\u6210\u529f\u63ed\u793aPKM2-GSH-SLC7A11 \u8ef8\u5fc3\u662f\u8abf\u7bc0\u764c\u7d30\u80de\u4ee3\u8b1d\u5e73\u8861\u8207\u9435\u51cb\u4ea1\u654f\u611f\u6027\u7684\u95dc\u9375\u6a5f\u5236 (\u767c\u8868\u65bcAdvanced Science<\/em>, 2025)\uff0c\u4e26\u63d0\u51fa\u7d50\u5408 PKM2 \u6d3b\u5316\u5291\u8207\u9435\u51cb\u4ea1\u8a98\u5c0e\u5291\u7684\u5354\u540c\u7642\u6cd5\u7b56\u7565\u3002<\/li>\n\n\n\n
    2. \u6211\u5011\u767c\u5c55\u4e86\u5168\u65b0\u7684SINUM\u65b9\u6cd5(\u767c\u8868\u65bcBMC Bioinformatics<\/em>, 2024 )\uff0c\u5229\u7528MI\u5ea6\u91cf\u69cb\u5efa\u55ae\u7d30\u80de\u7db2\u8def\uff0c\u4e26\u9a57\u8b49\u4e86\u5728\u5206\u7fa4\u5206\u6790\u4e0a\uff0cSINUM\u65b9\u6cd5\u76f8\u8f03\u65bc\u904e\u53bb\u7684CSN\u65b9\u6cd5\u8207\u50b3\u7d71\u57fa\u56e0\u8868\u73fe\u5206\u6790\uff0c\u5177\u6709\u66f4\u9ad8\u7684\u6e96\u78ba\u6027\u3002\u6211\u5011\u7684\u65b9\u6cd5\u4e0d\u53ea\u80fd\u5920\u5728\u55ae\u7d30\u80de\u89e3\u6790\u5ea6\u4e0a\u7528\u4f86\u8b58\u5225\u57fa\u56e0\u4e4b\u9593\u7684\u95dc\u4fc2\uff0c\u66f4\u662f\u64c1\u6709\u6316\u6398\u6f5b\u85cf\u95dc\u9375\u57fa\u56e0\u7684\u80fd\u529b\u3002<\/strong><\/li>\n\n\n\n
    3. \u70ba\u4e86\u5f9e\u60a3\u8005\u500b\u9ad4\u5168\u57fa\u56e0\u7d44\u8868\u73fe\u8cc7\u6599\u4e2d\u63d0\u53d6\u7279\u5b9a\u60c5\u5883\u4e0b\u7684\u751f\u7269\u7db2\u8def\u8a0a\u606f\uff0c\u6211\u5011\u8fd1\u671f\u958b\u767c\u4e86\u55ae\u6a23\u672c\u7db2\u8def\u63a8\u6e2c\u65b9\u6cd5\u7528\u65bc\u5efa\u7acb\u60a3\u8005\u500b\u9ad4\u7db2\u8def (SWEET\uff1b\u767c\u8868\u65bcBriefings in Bioinformatics<\/em>, 2023)\uff0cSWEET\u65b9\u6cd5\u4e0d\u50c5\u80fd\u6709\u6548\u8fa8\u8b58\u500b\u4eba\u5316\u85e5\u7269\u6a19\u9776\u548c\u7528\u85e5\u9810\u6e2c\uff0c\u9084\u80fd\u5f9e\u7db2\u8def\u5c64\u7d1a\u5c0d\u764c\u75c7\u9032\u884c\u985e\u578b\/\u4e9e\u578b\u7684\u9810\u6e2c\u8207\u5206\u985e\uff0c\u9032\u800c\u4fc3\u9032\u500b\u4eba\u5316\u6cbb\u7642\u4e4b\u958b\u767c\u4ee5\u53ca\u76f8\u95dc\u81e8\u5e8a\u61c9\u7528\uff1b\u4f8b\u5982\uff1a\u6211\u5011\u8207\u5317\u69ae\u694a\u6155\u83ef\u6559\u6388\u5408\u4f5c\uff0c\u61c9\u7528\u751f\u7269\u7db2\u8def\u6a21\u578b\u6210\u529f\u63ed\u793a\u982d\u9838\u764c\u75c5\u60a3\u7684\u9435\u51cb\u4ea1\u3001\u4e0a\u76ae\u7d30\u80de\u9593\u8cea\u8f49\u5316\u4ee5\u53ca\u514d\u75ab\u7279\u5fb5\u7684\u76f8\u95dc\u6027 (\u767c\u8868\u65bcAdvanced Science<\/em>, 2023)\u3002<\/li>\n<\/ol>\n<\/div><\/div><\/div>\n<\/div>\n<\/div><\/div>\n<\/div><\/section><\/div>\n\n\n\n
      <\/div>\n\n\n\n

      \u7814\u7a76\u65b9\u5411\u53ca\u5408\u4f5c\u5925\u4f34 Research Collaboration & Collaborative Achievements<\/strong><\/h2>\n\n\n\n
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      \u5be6\u9a57\u5ba4\u5df2\u61c9\u7528\u6240\u767c\u5c55\u4e4b\u6982\u5ff5\u53ca\u65b9\u6cd5\u8207\u570b\u5167\u5916\u6578\u500b\u9802\u5c16\u6a5f\u69cb\u53ca\u4f01\u696d\u5177\u9577\u671f\u5408\u4f5c\u95dc\u4fc2\uff0c\u5305\u542b\u65e5\u672c\u4eac\u90fd\u5927\u5b78\u3001\u6fb3\u6d32\u8499\u7d0d\u8a31\u5927\u5b78\u3001Nvidia AI Technology Center (\u65e5\u672c\u53ca\u65b0\u52a0\u5761)\u3001\u53f0\u5317\u91ab\u5b78\u5927\u5b78\u3001\u4e09\u8ecd\u7e3d\u91ab\u9662\u3001\u53f0\u5317\u69ae\u6c11\u7e3d\u91ab\u9662\u3001\u9ad8\u96c4\u91ab\u5b78\u9662<\/strong>\u7b49\uff0c\u4f8b\u5982\uff1a\u6211\u5011\u8207\u5317\u91ab\u5171\u540c\u767c\u5c55\u4e00\u500b\u7cfb\u7d71\u5316\u6574\u5408\u6a21\u578b\uff0c\u900f\u904e\u5206\u679015\u7a2e\u764c\u75c7\u4e26\u91dd\u5c0d2,594\u500b\u4eba\u985e\u819c\u86cb\u767d\u9810\u6e2c\u4e26\u5efa\u7acb\u5176\u764c\u75c7\u819c\u86cb\u767d\u8abf\u63a7\u7db2\u8def(CaMPNets\uff1b\u767c\u8868\u65bcNature Communications, 2019<\/em>)\uff1b\u6211\u5011\u66f4\u900f\u904e\u6b64\u7cfb\u7d71\u6210\u529f\u627e\u5230\u4e00\u500b\u65b0\u7a4e\u6291\u5236\u5291 (\u820a\u85e5\u65b0\u7528\uff1b\u5df2\u7372\u5f97\u5169\u9805\u5c08\u5229\u3010\u5c08\u5229\u865f\uff1aI713875 & I736452 \u3011<\/strong>)\uff0c\u53ef\u6709\u6548\u6291\u5236\u4e73\u764c\u8f49\u79fb\u767c\u751f\u3002\u76ee\u524d\u8207\u5408\u4f5c\u5925\u4f34\u5171\u540c\u767c\u886818\u7bc7\u570b\u969b\u671f\u520a\uff0c\u4e26\u6bcf\u5e74\u63d0\u4f9b\u5be6\u9a57\u5ba4\u512a\u79c0\u5b78\u751f\u6216\u7814\u7a76\u4eba\u54e1\u524d\u5f80\u65e5\u672c\u4eac\u90fd\u5927\u5b78\u751f\u7269\u8cc7\u8a0a\u4e2d\u5fc3\u4ea4\u6d41\u8a2a\u554f\u7684\u6a5f\u6703(\u7531\u79d1\u6280\u90e8\u611b\u56e0\u65af\u5766\u57f9\u690d\u8a08\u756b\u652f\u6301)\u3002<\/p>\n\n\n\n

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      <\/i><\/span><\/i><\/span><\/div>\u5408\u4f5c\u5171\u540c\u767c\u8868<\/span><\/div>
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      1. \u6211\u5011\u8207\u5317\u91ab\u5171\u540c\u767c\u5c55\u4e00\u500b\u7cfb\u7d71\u5316\u6574\u5408\u8a08\u7b97\u6a21\u578b\uff0c\u900f\u904e\u5206\u679015\u7a2e\u764c\u75c75,922\u816b\u7624\u57fa\u56e0\u8868\u73fe\u8cc7\u6599\u53ca\u81e8\u5e8a\u8cc7\u8a0a\uff0c\u5927\u898f\u6a21\u91dd\u5c0d2,594\u500b\u4eba\u985e\u819c\u86cb\u767d\u9810\u6e2c\u4e26\u5efa\u7acb\u5176\u764c\u75c7\u819c\u86cb\u767d\u8abf\u63a7\u7db2\u8def(CaMPNets\uff1b\u767c\u8868\u65bcNature Communications, 2019<\/em>)\uff0c\u53ef\u9032\u4e00\u6b65\u7528\u65bc\u63a2\u8a0e\u764c\u75c7\u540c\u8cea\/\u7570\u8cea\u6027\u53ca\u767c\u5c5515\u7a2e\u764c\u75c7\u7684\u9810\u5f8c\u751f\u7269\u6a19\u8a18\u7d44\u8207\u85e5\u7269\u6a19\u9776\u86cb\u767d\uff1b\u6211\u5011\u66f4\u900f\u904e\u6b64\u7cfb\u7d71\u6210\u529f\u627e\u5230\u4e00\u500b\u65b0\u7a4e\u6291\u5236\u5291 (\u820a\u85e5\u65b0\u7528\uff1b\u5df2\u7372\u5f97\u5169\u9805\u5c08\u5229)\uff0c\u53ef\u6709\u6548\u6291\u5236\u4e73\u764c\u8f49\u79fb\u767c\u751f\u3002<\/li>\n\n\n\n
      2. \u6211\u5011\u8207\u65e5\u672cNVIDIA\u516c\u53f8\u5171\u540c\u5408\u4f5c\u900f\u904e\u6df1\u5ea6\u5b78\u7fd2(Deep Learning)\u958b\u767cAI \u667a\u6167\u6f14\u7b97\u6a21\u578b\uff0c\u85c9\u7531\u5206\u6790\u816b\u7624\u57fa\u56e0\u9ad4\u5927\u6578\u64da\u8cc7\u6599\u9032\u884c\u764c\u75c7\u4e9e\u578b\u7cbe\u6e96\u6aa2\u6e2c\u6280\u8853\uff0c\u53ef\u61c9\u7528\u65bc\u60a3\u8005\u764c\u75c7\u4e9e\u7fa4\u53ca\u55ae\u7d30\u80de\u5206\u985e\u751a\u81f3\u662f\u81e8\u5e8a\u8cc7\u8a0a\u9810\u6e2c(\u767c\u8868\u65bcJournal of Bioinformatics and Computational Biology, 2019<\/em>)\u3002<\/li>\n\n\n\n
      3. \u8207\u81fa\u5317\u69ae\u6c11\u7e3d\u91ab\u9662\u5408\u4f5c\uff0c\u61c9\u7528\u4e26\u6210\u529f\u63ed\u793a\u982d\u9838\u764c\u75c5\u60a3\u7684\u9435\u4f9d\u8cf4\u6027\u6b7b\u4ea1\u3001\u4e0a\u76ae\u7d30\u80de\u9593\u8cea\u8f49\u5316\u4ee5\u53ca\u514d\u75ab\u7279\u5fb5\u7684\u76f8\u95dc\u6027 (\u767c\u8868\u65bcAdvanced Science, 2023<\/em>)\u3002<\/li>\n<\/ol>\n<\/div><\/div><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
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        Publications<\/a><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":4,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-582","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sysmed-net.lab.nycu.edu.tw\/index.php\/wp-json\/wp\/v2\/pages\/582","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sysmed-net.lab.nycu.edu.tw\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sysmed-net.lab.nycu.edu.tw\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sysmed-net.lab.nycu.edu.tw\/index.php\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/sysmed-net.lab.nycu.edu.tw\/index.php\/wp-json\/wp\/v2\/comments?post=582"}],"version-history":[{"count":129,"href":"https:\/\/sysmed-net.lab.nycu.edu.tw\/index.php\/wp-json\/wp\/v2\/pages\/582\/revisions"}],"predecessor-version":[{"id":4442,"href":"https:\/\/sysmed-net.lab.nycu.edu.tw\/index.php\/wp-json\/wp\/v2\/pages\/582\/revisions\/4442"}],"wp:attachment":[{"href":"https:\/\/sysmed-net.lab.nycu.edu.tw\/index.php\/wp-json\/wp\/v2\/media?parent=582"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}