Titlebook: Computational Systems-Biology and Bioinformatics; First International Jonathan H. Chan,Yew-Soon Ong,Sung-Bae Cho Conference proceedings 20

Truman

书目名称Computational Systems-Biology and Bioinformatics影响因子(影响力)




书目名称Computational Systems-Biology and Bioinformatics影响因子(影响力)学科排名




书目名称Computational Systems-Biology and Bioinformatics网络公开度




书目名称Computational Systems-Biology and Bioinformatics网络公开度学科排名




书目名称Computational Systems-Biology and Bioinformatics被引频次




书目名称Computational Systems-Biology and Bioinformatics被引频次学科排名




书目名称Computational Systems-Biology and Bioinformatics年度引用




书目名称Computational Systems-Biology and Bioinformatics年度引用学科排名




书目名称Computational Systems-Biology and Bioinformatics读者反馈




书目名称Computational Systems-Biology and Bioinformatics读者反馈学科排名

梯田

Study of the Structural Pathology Caused by CYP2C9 Polymorphisms towards Flurbiprofen Metabolism Usi constructed based on the X-ray crystal structure of CYP2C9-flurbiprofen complex. Herein, molecular docking of CYP2C9*1 and CYP2C9*13 with flurbiprofen was performed in search for flurbiprofen orientation that corresponds to its binding state before undergoing monooxygenation. Subsequently, molecula

刺耳

corpuscle

发酵剂

Mining Regulatory Elements in Non-coding Regions of ,the expected non-coding regions of the co-expressed gene groups. As the preliminary step of this work, we investigate whether different motif patterns can be detected in the specified non-coding regions of co-expressed genes with different functional categories. The similar prediction results from M

鸵鸟

Prediction of Non-coding RNA and Their Targets in , GenomeRecognition Particle RNA, Group II intron RNA and Yfr1. It has been reported that Yfr1 has been found in most cyanobacterial genomes sequenced. The result showed that more than 70 putative loci were similar to Group II intron RNAs. In addition, approximately 100 predicted ncRNA loci were not matched

鸵鸟

易发怒

Demulcent

违法事实

Unsupervised Algorithms for Population Classification and Ancestry Informative Marker Selectione, and Student’s t-test, to cluster unlabeled genotype data and determine AIMs, respectively. This unsupervised approach can avoid potential ascertainment biases due to mistakenly label some samples or having unlabeled data to be classified.