International Journal of Data Mining and Bioinformatics (5 papers in press)

Regular Issues

• A Network Enhancement-based Method for Clustering of Single cell RNA-seq Data  
  by Xiaoshu Zhu, Lilu Guo Guo, Rongyuan Li, Yunpei Xu, Fang-xiang Wu, Xiaoqing Peng, Hong-Dong Li 
  Abstract: Single cell RNA sequencing (scRNA-seq) provides a more granular description of gene expression in a single cell. Many clustering methods for scRNA-seq data have been developed to understand cell development and cell differentiation. However, the high dimension and the strong noise make clustering scRNA-seq data challenging. To overcome this problem, we propose a method for clustering scRNA-seq data, called network enhancement-based similarity combined with Louvain (NES-Louvain). In NES-Louvain, the initial similarity matrix is denoised by using a network enhancement method. Then, a path-based similarity measurement is designed to introduce the nodes in high-order paths based on the assumption that including more relevant nodes would improve the similarity of node pairs. Finally, Louvain community detection method is improved to clustering single cells. The experimental results show that NES and NES-Louvain achieve better performance than other methods. Furthermore, NES-Louvain shows robust to perturbation.
  Keywords: similarity measurement; single cell clustering; network enhancement; path-based similarity; Louvain community detection.
  
  
• Finding Correlated Biclusters from Microarray Data Using Modified Lift Algorithm Based on New Residue Score.  
  by Alhadi Bustamam, Soeganda Formalidin, Titin Siswantining, Zuherman Rustam 
  Abstract: The purpose of this research is to find a strong correlation between genes and conditions of diabetes mellitus gene expression data from obese and lean people using three-phase biclustering. The first step is to use singular value decomposition (SVD) to decompose matrix gene expression data into two global-based gene and condition matrices. The second step is to use partition around medoid (PAM) to cluster gene and condition-based matrices using Euclidean distance, forming several biclusters that were further evaluated using the Modified Lift Algorithm based on Pearson correlation, which is a very appropriate method to detect an additivemultiplicative bicluster type. The algorithm processes are run using open-source R software. The resulting biclusters of the proposed algorithm having a strong correlation among genes and samples are obtained so that the method has high potential in future medical research.
  Keywords: correlated bicluster; diabetes mellitus; microarray data; Modified Lift Algorithm (MLA); R software.
  
  
• Measurement of Structural Change in Co-expression Networks from Cancer Gene Expression Data  
  by Qianran Li, Dario Ghersi, Ishwor Thapa, Ling Zhang, Hesham Ali, Kathryn Cooper 
  Abstract: Profiling progression and development of cancer is an important step for informing clinical decisions in the diagnosis, prognosis, and treatment of cancer. The hallmarks of cancer progression are well established; for example, many studies have concluded that surveillance of gene expression relationships during cancer progression would inform diagnostic and treatment decisions. Differential network analysis offers a systems-level insight into cancer progression from a high-level point of view, and once further understood, could become a transformative approach for measuring cancer progression. In this work, we investigate an approach to measure pairwise change in network topology between cancer stages for 4 cancers, including thyroid carcinoma, colon and rectum adenocarcinoma, stomach adenocarcinoma, and kidney renal papillary cell carcinoma. We use a network-based approach to describe systems- level views of how a network model changes over the course of a 4-stage disease progression in these cancers and examine how mutation rate corresponds to network structure. Lastly, we present a case study in comparing primary versus metastatic tumor network structure. The results of this study demonstrate the applicability of such an approach and provide insights into next steps that are needed for differential network comparison in cancer.
  Keywords: Gene expression; Correlation network; Jaccard Similarity; The Cancer Genome Atlas; mutation rate.
  
  
• Identification of candidate biomarkers and pathways in breast cancer by differential network analysis  
  by Onur Mendi, Adem Karahoca 
  Abstract: Breast cancer is one of the most malignant cancers in women worldwide. The aim of the present study was to explore the underlying biological mechanisms of breast cancer. For this purpose, we propose a novel framework to reveal mechanisms that drive disease progression in breast cancer by combining prior knowledge in the literature with differential networking methodology. Our integration framework has resulted in the most important genes and interactions by allowing ranking the breast cancer-specific gene network. YY1, SMARCA5, FOXM1, STAT4, and PTTG1 were found to be the most important genes in breast cancer. Functional and pathway enrichment analyses identified numerous pathways that may play a critical role in disease progression. Considering the success of the comparison of the results with the literature, the systemic lupus erythematosus pathway may be a potential target of breast cancer.
  Keywords: breast cancer; differential network analysis; bioinformatics; microarray.
  
  
• Study on the rule and logical relationship of TCM prescription for lumbago
  by Yong Xiao, Jiaozhi Wang, Shaowu Shen, Xiaoqiong Wang, Yunfang Liu, Yan Wang 
  Abstract: Based on the prescription information of low back pain obtained from the hospital electronic medical record system, this paper first analyzes the efficacy of drugs, the rules of drug such as four qi and five taste, and then the Apriori algorithm is used to find the association rules of prescription drugs. Finally, the high-order logic analysis method based on probability logic is used to mine the high-order logic relationship of prescription drugs.
  Keywords: lumbago;electronic medical record;prescription regularity;logic relationships.