An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.
An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.
复制标题
一种改进的二元差分进化算法来推断肿瘤系统发育树
DOI：
10.1155/2017/5482750
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发表时间：
2017
影响因子：
--
通讯作者：
Zhu W
中科院分区：
文献类型：
--
作者：
Liang Y;Liao B;Zhu W
肿瘤发生是一个突变积累过程，可能始于突变的创始细胞。肿瘤发展的进化性质使得系统发育模型适合通过遗传变异数据推断肿瘤进化。拷贝数变异（CNV）是基因组的主要遗传标记，涉及到许多基因、疾病位点和功能元件。荧光原位杂交（FISH）可以精确测量数百个单细胞的多个基因拷贝数。提出了一种基于FISH平台的改进二进制差分进化算法BDEP来推断肿瘤系统发生树。肿瘤进展树的拓扑分析表明，在肿瘤形成的不同阶段，肿瘤亚细胞扩张的途径变化很大。分类实验表明，基于树的特征比基于数据的特征在区分肿瘤方面有更好的效果。所构建的系统发生树在描述肿瘤发展过程方面具有很好的性能，优于其他同类算法。
Tumourigenesis is a mutation accumulation process, which is likely to start with a mutated founder cell. The evolutionary nature of tumor development makes phylogenetic models suitable for inferring tumor evolution through genetic variation data. Copy number variation (CNV) is the major genetic marker of the genome with more genes, disease loci, and functional elements involved. Fluorescence in situ hybridization (FISH) accurately measures multiple gene copy number of hundreds of single cells. We propose an improved binary differential evolution algorithm, BDEP, to infer tumor phylogenetic tree based on FISH platform. The topology analysis of tumor progression tree shows that the pathway of tumor subcell expansion varies greatly during different stages of tumor formation. And the classification experiment shows that tree-based features are better than data-based features in distinguishing tumor. The constructed phylogenetic trees have great performance in characterizing tumor development process, which outperforms other similar algorithms.
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影响因子：
64.8
作者：
Greaves, Mel;Maley, Carlo C.
通讯作者：
Maley, Carlo C.
影响因子：
64.8
作者：
Navin N;Kendall J;Troge J;Andrews P;Rodgers L;McIndoo J;Cook K;Stepansky A;Levy D;Esposito D;Muthuswamy L;Krasnitz A;McCombie WR;Hicks J;Wigler M
通讯作者：
Wigler M
影响因子：
7
作者：
Ha G;Roth A;Khattra J;Ho J;Yap D;Prentice LM;Melnyk N;McPherson A;Bashashati A;Laks E;Biele J;Ding J;Le A;Rosner J;Shumansky K;Marra MA;Gilks CB;Huntsman DG;McAlpine JN;Aparicio S;Shah SP
通讯作者：
Shah SP
影响因子：
6
作者：
Heselmeyer-Haddad, Kerstin;Garcia, Lissa Y. Berroa;Ried, Thomas
通讯作者：
Ried, Thomas
影响因子：
11.2
作者：
Kanao, H;Enomoto, T;Murata, Y
通讯作者：
Murata, Y