“第四十八章 开发癌症疫苗”版本间的差异
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==教学资料== | ==教学资料== | ||
| − | * [[教学参考- | + | * [[教学参考-48|教学参考]] |
| − | * [http://aigraph.cslt.org/courses/ | + | * [http://aigraph.cslt.org/courses/48/course-48.pptx 课件] |
* 小清爱提问：人工智能如何助力开发癌症疫苗？ [https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247486775&idx=1&sn=f5115c5ed723303b683a25c35a93f901&chksm=c30807f5f47f8ee35cc32c7611fd1ac4cc271623eaa20e5e074e55c7c99104cd322a9ec65aeb&scene=178#rd] | * 小清爱提问：人工智能如何助力开发癌症疫苗？ [https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247486775&idx=1&sn=f5115c5ed723303b683a25c35a93f901&chksm=c30807f5f47f8ee35cc32c7611fd1ac4cc271623eaa20e5e074e55c7c99104cd322a9ec65aeb&scene=178#rd] | ||
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* 细胞免疫 [https://www.bilibili.com/video/BV1cZ4y1k7Zq/?spm_id_from=333.788.recommend_more_video.-1] | * 细胞免疫 [https://www.bilibili.com/video/BV1cZ4y1k7Zq/?spm_id_from=333.788.recommend_more_video.-1] | ||
* CAR-T细胞免疫疗法 [https://www.bilibili.com/video/BV1Zg4y1B7Us?spm_id_from=333.337.search-card.all.click] | * CAR-T细胞免疫疗法 [https://www.bilibili.com/video/BV1Zg4y1B7Us?spm_id_from=333.337.search-card.all.click] | ||
| + | * 一分半解读CAR-T疗法 [https://www.bilibili.com/video/BV16W41157MX/?spm_id_from=333.788.recommend_more_video.-1] | ||
| + | * 肿瘤免疫疗法之免疫检查点抑制剂 [https://www.bilibili.com/video/BV1MZ4y1c7Ff?spm_id_from=333.337.search-card.all.click] | ||
| + | * 体免疫的“刹车”系统：新免疫疗法为癌症患者带来福音 [https://www.bilibili.com/video/BV1Db411A7Un/?spm_id_from=333.788.recommend_more_video.2] | ||
==演示链接== | ==演示链接== | ||
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* Hu Z, Ott P A, Wu C J. Towards personalized, tumour-specific, therapeutic vaccines for cancer[J]. Nature Reviews Immunology, 2018, 18(3): 168-182. [https://www.nature.com/articles/nri.2017.131] | * Hu Z, Ott P A, Wu C J. Towards personalized, tumour-specific, therapeutic vaccines for cancer[J]. Nature Reviews Immunology, 2018, 18(3): 168-182. [https://www.nature.com/articles/nri.2017.131] | ||
| − | * Andreatta, M. & Nielsen, M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 32, 511–517 (2016). | + | * Andreatta, M. & Nielsen, M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 32, 511–517 (2016). [https://academic.oup.com/bioinformatics/article/32/4/511/1744469] |
| − | * Calling cancer’s bluff with neoantigen vaccines， https://www.nature.com/articles/d41586-017-08706-3 | + | * Calling cancer’s bluff with neoantigen vaccines， [https://www.nature.com/articles/d41586-017-08706-3] |
* Ngoc Hieu Tran, Rui Qiao, Lei Xin, Xin Chen, Baozhen Shan，Ming Li， Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines， Nature Machine Intelligence, 2, pages764–771(2020) [https://www.nature.com/articles/s42256-020-00260-4#MOESM2] | * Ngoc Hieu Tran, Rui Qiao, Lei Xin, Xin Chen, Baozhen Shan，Ming Li， Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines， Nature Machine Intelligence, 2, pages764–771(2020) [https://www.nature.com/articles/s42256-020-00260-4#MOESM2] | ||
| + | * Ball P. First quantum computer to pack 100 qubits enters crowded race[J]. Nature, 2021, 599(7886): 542-542. [https://www.nature.com/articles/d41586-021-03476-5] | ||
2022年8月26日 (五) 04:55的最后版本
教学资料
扩展阅读
视频展示
- 疫苗的历史：人类与病毒之间的史诗级战役，最终谁赢了？ [5]
- 细胞免疫 [6]
- CAR-T细胞免疫疗法 [7]
- 一分半解读CAR-T疗法 [8]
- 肿瘤免疫疗法之免疫检查点抑制剂 [9]
- 体免疫的“刹车”系统：新免疫疗法为癌症患者带来福音 [10]
演示链接
开发者资源
高级读者
- Hu Z, Ott P A, Wu C J. Towards personalized, tumour-specific, therapeutic vaccines for cancer[J]. Nature Reviews Immunology, 2018, 18(3): 168-182. [13]
- Andreatta, M. & Nielsen, M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 32, 511–517 (2016). [14]
- Calling cancer’s bluff with neoantigen vaccines， [15]
- Ngoc Hieu Tran, Rui Qiao, Lei Xin, Xin Chen, Baozhen Shan，Ming Li， Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines， Nature Machine Intelligence, 2, pages764–771(2020) [16]
- Ball P. First quantum computer to pack 100 qubits enters crowded race[J]. Nature, 2021, 599(7886): 542-542. [17]
