模式识别与人工智能
模式識彆與人工智能
모식식별여인공지능
Moshi Shibie yu Rengong Zhineng
2014年
7期
584-590
,共7页
基因调控网络%互信息%l1-正则化%Logistic 回归
基因調控網絡%互信息%l1-正則化%Logistic 迴歸
기인조공망락%호신식%l1-정칙화%Logistic 회귀
Gene Regulatory Networks%Mutual Information%l1-Regularization%Logistic Regression
目前，由大多数基因调控网络的重构方法推导出的网络结构是静态的，即不随时间改变。但在细胞周期或一个有机体的不同生长阶段，调控网络的拓扑结构会发生显著变化。这为深入了解基因调控的时空机制带来困难。因此，文中提出一种基于时延互信息和核权重l1正则化Logistic回归模型学习时变结构基因调控网络的算法。将其应用于两种生物情景数据：黑腹果蝇在不同阶段的肌肉发育和酿酒酵母苯菌灵中毒后的反应。实验结果显示，该方法能反映不同细胞状态对基因间相互作用的影响，有效获取基因调控网络随时间变化的动态效应。
目前，由大多數基因調控網絡的重構方法推導齣的網絡結構是靜態的，即不隨時間改變。但在細胞週期或一箇有機體的不同生長階段，調控網絡的拓撲結構會髮生顯著變化。這為深入瞭解基因調控的時空機製帶來睏難。因此，文中提齣一種基于時延互信息和覈權重l1正則化Logistic迴歸模型學習時變結構基因調控網絡的算法。將其應用于兩種生物情景數據：黑腹果蠅在不同階段的肌肉髮育和釀酒酵母苯菌靈中毒後的反應。實驗結果顯示，該方法能反映不同細胞狀態對基因間相互作用的影響，有效穫取基因調控網絡隨時間變化的動態效應。
목전，유대다수기인조공망락적중구방법추도출적망락결구시정태적，즉불수시간개변。단재세포주기혹일개유궤체적불동생장계단，조공망락적탁복결구회발생현저변화。저위심입료해기인조공적시공궤제대래곤난。인차，문중제출일충기우시연호신식화핵권중l1정칙화Logistic회귀모형학습시변결구기인조공망락적산법。장기응용우량충생물정경수거：흑복과승재불동계단적기육발육화양주효모분균령중독후적반응。실험결과현시，해방법능반영불동세포상태대기인간상호작용적영향，유효획취기인조공망락수시간변화적동태효응。
At present, network structures derived from most gene regulatory network reconstruction methods are static, which do not change with time. However, in the cell cycle or different growth stages of an organismat, the topology of regulatory network changes significantly, which makes it difficult to understand the spatial-temporal mechanism of gene regulation. Therefore, an algorithm for the network is proposed based on time lagged Mutual Information ( TLMI) and a kernel-reweighted l1-regularized logistic regression model. Two biological scenarios, the developmental stages of Drosophila melanogaster and the response of Saccharomyces cerevisiae to benomyl poisoning, are analyzed. The experimental results show that the proposed method reflects the impact of different cell states of interaction between genes and effectively acquires the dynamic effect of gene regulatory networks changing with time.
