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研究揭示深度神经网络在前列腺癌发现中的应用
作者:小柯机器人 发布时间:2021/9/24 14:23:10

美国丹娜-法伯癌症研究所Eliezer M. Van Allen小组揭示深度神经网络在前列腺癌发现中的应用。2021年9月22日,《自然》杂志在线发表了这一最新研究成果。

研究人员开发了P-NET,一个生物学上的深度学习模型,通过完整的模型可解释性来对前列腺癌患者进行分层,并评估治疗耐药性的分子驱动因素,从而确定治疗目标。研究人员证明,P-NET可以利用分子数据预测癌症状态,其性能优于其他建模方法。此外,P-NET内的生物可解释性揭示了已知的和新的分子改变候选基因,如MDM4和FGFR1,这些候选基因牵涉到预测晚期疾病并在体外得到验证。

广义上讲,生物学上完全可解释的神经网络使前列腺癌的临床前发现和临床预测成为可能,并可能具有跨癌症类型的普遍适用性。

据悉,确定介导前列腺癌临床侵袭性表型的分子特征仍然是一个重大的生物学和临床挑战。机器学习模型应用于生物医学问题的可解释性方面的最新进展可能使临床癌症基因组学的发现和预测成为可能。

附:英文原文

Title: Biologically informed deep neural network for prostate cancer discovery

Author: Elmarakeby, Haitham A., Hwang, Justin, Arafeh, Rand, Crowdis, Jett, Gang, Sydney, Liu, David, AlDubayan, Saud H., Salari, Keyan, Kregel, Steven, Richter, Camden, Arnoff, Taylor E., Park, Jihye, Hahn, William C., M. Van Allen, Eliezer

Issue&Volume: 2021-09-22

Abstract: The determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer remains a major biological and clinical challenge1,2. Recent advances in interpretability of machine learning models as applied to biomedical problems may enable discovery and prediction in clinical cancer genomics3,4,5. Here we developed P-NET—a biologically informed deep learning model—to stratify patients with prostate cancer by treatment-resistance state and evaluate molecular drivers of treatment resistance for therapeutic targeting through complete model interpretability. We demonstrate that P-NET can predict cancer state using molecular data with a performance that is superior to other modelling approaches. Moreover, the biological interpretability within P-NET revealed established and novel molecularly altered candidates, such as MDM4 and FGFR1, which were implicated in predicting advanced disease and validated in vitro. Broadly, biologically informed fully interpretable neural networks enable preclinical discovery and clinical prediction in prostate cancer and may have general applicability across cancer types.

DOI: 10.1038/s41586-021-03922-4

Source: https://www.nature.com/articles/s41586-021-03922-4

期刊信息

Nature:《自然》,创刊于1869年。隶属于施普林格·自然出版集团,最新IF:43.07
官方网址:http://www.nature.com/
投稿链接:http://www.nature.com/authors/submit_manuscript.html