Mol Oncol:人工智能预测乳腺癌对化疗的耐受性,助力个体化治疗

2015-09-27 MedSci MedSci原创

患有同种疾病的不同病人在接受同一种治疗方案时,其获得的疗效也会存在不同,这就给医生留下了一个难题:他们怎样才能知道哪种治疗方法对该名病人最有效呢?如果医生选择了适合该名病人的疗法,那么他就可能重获健康,但如果选择错了,那么,他的健康将可能被彻底摧毁。现在,来自加拿大西方大学的科研人员将可能帮助医生解决这个难题。他们开发出了一套机器学习算法--人工智能的其中一个分支--通过基因数据的分析得出最可能的

患有同种疾病的不同病人在接受同一种治疗方案时,其获得的疗效也会存在不同,这就给医生留下了一个难题:他们怎样才能知道哪种治疗方法对该名病人最有效呢?如果医生选择了适合该名病人的疗法,那么他就可能重获健康,但如果选择错了,那么,他的健康将可能被彻底摧毁。现在,来自加拿大西方大学的科研人员将可能帮助医生解决这个难题。

他们开发出了一套机器学习算法--人工智能的其中一个分支--通过基因数据的分析得出最可能的有效治疗方案,并且让该治疗方案变得更加个体化。

该项研究的负责人Peter Rogan表示,由于人工智能可以查看所有相互之间有影响的基因,所以它是一种非常强大的药物结果预测工具,并称越早使用最具疗效的法案,病人治愈的可能性越大。

研究人员在本次研究中使用了一套含有40个基因的数据,据悉,这些基因在90%的乳腺癌肿瘤中找到。在接受实验的近350名癌症病人当中,至少都会接受紫杉醇或吉西他滨其中一种化疗药物。之后,他们让系统对数据展开处理并找出药物跟病人基因之间存在的关系。结果显示,同时接受两种药物的治疗率为84%,只接受紫杉醇的为82%,只接受吉西他滨的则在62%到71%之间。

现在,研究人员打算进一步优化这套算法,他们将为系统输入更多数据,以此提高预测的准确率。

原始出处:

Dorman SN, Baranova K, Knoll JH, Urquhart BL, Mariani G, Carcangiu ML, Rogan PK.Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning. Mol Oncol. 2015 Aug 22. pii: S1574-7891(15)00146-5.

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    2016-03-14 minlingfeng
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    2015-10-18 liangzhi1220

    人工智能潜力巨大

    0

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    2015-09-29 zhouqu_8
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    2015-09-27 medcardio

    虽然机器算法刚刚开始,但未来潜力巨大,有可能帮助临床医生做更正确的决策

    0

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