Modeling disease progression in Multiple Myeloma with Hopfield networks and single-cell RNA-seq.
Modeling disease progression in Multiple Myeloma with Hopfield networks and single-cell RNA-seq.
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利用 Hopfield 网络和单细胞 RNA-seq 模拟多发性骨髓瘤的疾病进展。
DOI：
10.1109/bibm47256.2019.8983325
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Piermarocchi,Carlo
中科院分区：
文献类型：
--
作者：
Domanskyi,Sergii;Hakansson,Alex;Paternostro,Giovanni;Piermarocchi,Carlo
Hopfield神经网络中的联想记忆被映射到基因表达模式，以模拟多发性骨髓瘤(MM)疾病进展的不同路径。该模型是使用来自MM患者以及诊断为意义不明的单克隆性伽马病(MGUS)和阴燃多发性骨髓瘤(SMM)的患者的单细胞RNA-SEQ数据建立的，这两种疾病通常会进展到完全MM。结果：我们识别了MGUS、SMM和MM细胞的不同簇，将它们映射到Hopfield联想记忆模式，并对不同模式之间的转换动力学进行了建模。然后，该模型被用来识别在不同的MM阶段中差异表达的基因，并且其同时抑制与延缓疾病进展有关。
Associative memories in Hopfield's neural networks are mapped to gene expression pattern to model different paths of disease progression towards Multiple Myeloma (MM). The model is built using single cell RNA-seq data from bone marrow aspirates of MM patients as well as patients diagnosed with Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM), two medical conditions that often progress to full MM. Results: We identify different clusters of MGUS, SMM, and MM cells, map them to Hopfield associative memory patterns, and model the dynamics of transition between the different patterns. The model is then used to identify genes that are differentialy expressed across different MM stages and whose simultaneous inhibition is associated to a delayed disease progression.