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 15,188 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The classification of serum samples based on mass spectrometry (MS) has been increasingly used for monitoring disease progression and for diagnosing early disease. However, the classification task in mass spectrometry data is extremely challenging due to the very huge size of peaks (features) on mass spectra. Linear discriminant analysis (LDA) has been widely used for dimension reduction and feature extraction in many applications. However, the conversional LDA suffers from the singularity problem when dealing with high-dimensional features. Another critical limitation is its linearity property which results in failing in classification problems over nonlinearly clustered data sets. To overcome such problems, we develop a new fast kernel discriminant analysis (FKDA) that is pretty fast in the calculation of optimal discriminant vectors. FKDA is applied to the classification of liver cancer mass spectrometry data that consist of three categories: hepatocellular carcinoma, cirrhosis, and healthy that was originally analyzed by Ressom et al. We demonstrate the superiority and effectiveness of FKDA when compared to other classification techniques.
IEEE/ACM Trans Comput Biol Bioinform
PMID:Fast kernel discriminant analysis for classification of liver cancer mass spectra. 2047 3

Extraction of meaningful information from large experimental data sets is a key element in bioinformatics research. One of the challenges is to identify genomic markers in Hepatitis B Virus (HBV) that are associated with HCC (liver cancer) development by comparing the complete genomic sequences of HBV among patients with HCC and those without HCC. In this study, a data mining framework, which includes molecular evolution analysis, clustering, feature selection, classifier learning, and classification, is introduced. Our research group has collected HBV DNA sequences, either genotype B or C, from over 200 patients specifically for this project. In the molecular evolution analysis and clustering, three subgroups have been identified in genotype C and a clustering method has been developed to separate the subgroups. In the feature selection process, potential markers are selected based on Information Gain for further classifier learning. Then, meaningful rules are learned by our algorithm called the Rule Learning, which is based on Evolutionary Algorithm. Also, a new classification method by Nonlinear Integral has been developed. Good performance of this method comes from the use of the fuzzy measure and the relevant nonlinear integral. The nonadditivity of the fuzzy measure reflects the importance of the feature attributes as well as their interactions. These two classifiers give explicit information on the importance of the individual mutated sites and their interactions toward the classification (potential causes of liver cancer in our case). A thorough comparison study of these two methods with existing methods is detailed. For genotype B, genotype C subgroups C1, C2, and C3, important mutation markers (sites) have been found, respectively. These two classification methods have been applied to classify never-seen-before examples for validation. The results show that the classification methods have more than 70 percent accuracy and 80 percent sensitivity for most data sets, which are considered high as an initial scanning method for liver cancer diagnosis.
IEEE/ACM Trans Comput Biol Bioinform
PMID:Data mining on DNA sequences of hepatitis B virus. 2123 23

Directional association measured by functional dependency can answer important questions on relationships between variables, for example, in discovery of molecular interactions in biological systems. However, when one has no prior information about the functional form of a directional association, there is not a widely established statistical procedure to detect such an association. To address this issue, here we introduce an exact functional test for directional association by examining the strength of functional dependency. It is effective in promoting functional patterns by reducing statistical power on non-functional patterns. We designed an algorithm to carry out the test using a fast branch-and-bound strategy, which achieved a substantial speedup over brute-force enumeration. On data from an epidemiological study of liver cancer, the test identified the hepatitis status of a subject as the most influential risk factor among others for the cancer phenotype. On human lung cancer transcriptome data, the test selected 1049 transcription start sites of putative noncoding RNAs directionally associated with lung cancers, stronger than 95% of 589 curated cancer genes. These predictions include non-monotonic interaction patterns, to which other routine tests were insensitive. Complementing symmetric (non-directional) association methods such as Fisher's exact test, the exact functional test is a unique exact statistical test for evaluating evidence for causal relationships.
IEEE/ACM Trans Comput Biol Bioinform 2018 Feb 26
PMID:A fast exact functional test for directional association and cancer biology applications. 2999 84

Liver cancer is one of the deadliest cancers in the world. To find effective therapies for this cancer, it is indispensable to identify key genes, which may play critical roles in the incidence of the liver cancer. To identify key genes of the liver cancer with high accuracy, we integrated multiple microarray gene expression data sets to compute common differentially expressed genes, which will result more accurate than those from individual data set. To find the main functions or pathways that these genes are involved in, some enrichment analyses were performed including functional enrichment analysis, pathway enrichment analysis, and disease association study. Based on these genes, a protein-protein interaction network was constructed and analyzed to identify key genes of the liver cancer by combining the local and global influence of nodes in the network. The identified key genes, such as TOP2A, ESR1, and KMO, have been demonstrated to be key biomarkers of the liver cancer in many publications. All the results suggest that our method can effectively identify key genes of the liver cancer. Moreover, our method can be applied to other types of data sets to select key genes of other complex diseases.
IEEE/ACM Trans Comput Biol Bioinform 2018 Oct 05
PMID:Identifying Key Genes of Liver Cancer by Networking of Multiple Data Sets. 3029 39

Bayesian networks are a powerful method for identifying causal relationships among variables. However, as the network size increases, the time complexity of searching the optimal structure grows exponentially. We proposed a novel search algorithm - Fast and Furious Bayesian Network (FFBN). Compared to the existing greedy search algorithm, FFBN uses significantly fewer model configuration rules to determine the causal direction of edges when constructing the Bayesian network, which leads to greatly improved computational speed. We benchmarked the performance of FFBN by reconstructing gene regulatory networks (GRNs) from two DREAM5 challenge datasets. In both datasets, FFBN shows a much faster speed than the existing greedy search algorithm, while maintaining equally good or better performance in recall and precision. We then constructed three whole transcriptome GRNs for primary liver cancer (PL), primary colon cancer (PC) and colon to liver metastasis (CLM) expression data, which the existing greedy search algorithms failed. Three GRNs contain 12,099 common genes. Unprecedentedly, our newly developed FFBN algorithm is able to build up GRNs at a scale larger than 10,000 genes. Using FFBN, we discovered that CLM has its unique cancer molecular mechanisms and shares a certain degree of similarity with both PL and PC.
IEEE/ACM Trans Comput Biol Bioinform 2019 Oct 01
PMID:A Fast and Furious Bayesian Network and Its Application of Identifying Colon Cancer to Liver Metastasis Gene Regulatory Networks. 3158 Oct 91

Hepatocellular carcinoma (HCC) is a common type of liver cancer and has a high mortality world-widely. The diagnosis, prognoses, and therapeutics are very poor due to the unclear molecular mechanism of progression of the disease. To unveil the molecular mechanism of progression of HCC, we extract a large sample of mRNA expression levels from the GEO database where a total of 167 samples were used for study, and out of them, 115 samples were from HCC tumor tissue. This study aims to investigate the module of differentially expressed genes (DEGs) which are co-expressed only in HCC sample data but not in normal tissue samples. Thereafter, we identified the highly significant module of significant co-expressed genes and formed a PPI network for these genes. There were only six genes (namely, MSH3, DMC1, ALPP, IL10, ZNF223, and HSD17B7) obtained after analysis of the PPI network. Out of six only MSH3, DMC1, HSD17B7, and IL10 were found enriched in GO Term & Pathway enrichment analysis and these candidate genes were mainly involved in cellular process, metabolic and catalytic activity, which promote the development & progression of HCC. Lastly, the composite 3-node FFL reveals the driver miRNAs and TFs associated with our key genes.
IEEE/ACM Trans Comput Biol Bioinform 2020 Aug 14
PMID:Deciphering key genes and miRNAs associated with Hepatocellular carcinoma via network-based approach. 3279 71

Many of the known prognostic gene signatures for cancer are individual genes or combination of genes, found by the analysis of microarray data. However, many random gene expression signatures are more predictive than known cancer signatures, and such predictive power of random signatures is largely attributed to cell proliferation genes. With the availability of RNA-seq gene expression data for thousands of human cancer patients, we have analyzed RNA-seq and clinical data of cancer patients and constructed gene correlation networks specific to individual cancer patients. From the gene correlation networks, we derived potential prognostic gene pairs for liver cancer, pancreatic cancer, and stomach cancer. In this paper, we present a new approach to inferring prognostic signatures from patient-specific gene correlation networks. Evaluation of our approach with comprehensive data of liver cancer, pancreatic cancer, and stomach cancer showed that our approach is general and that gene pairs found by our approach are more reliable prognostic signatures than genes. Our approach will be useful for constructing patient-specific gene correlation networks and for the prognosis of patients. The web server for dynamically constructing patient-specific gene networks and for finding prognostic gene pairs is accessible at http://bclab.inha.ac.kr/LPS.
IEEE/ACM Trans Comput Biol Bioinform 2020 Aug 18
PMID:A New Approach to Deriving Prognostic Gene Pairs from Cancer Patient-specific Gene Correlation Networks. 3280 42