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Many bioinformatics studies aim to find features that differentiate between two or more classes. Recent work proposes a Bayesian framework for feature selection that places a prior on the label-conditioned feature distribution. Assuming independent features, the optimal Bayesian filter is obtained and has been solved for Gaussian features. Here we extend the optimal Bayesian filter for categorical...
Merging gene expression datasets is a simple way to increase the number of samples in an analysis. However experimental and data processing conditions, which are proper to each dataset or batch, generally influence the expression values and can hide the biological effect of interest. It is then important to normalize the bigger merged dataset, as failing to adjust for those batch effects may adversely...
Molecularly targeted therapies significantly contribute to the efforts of personalized approaches for cancer diagnosis and chemotherapeutic treatment. One of a critical step to identify target molecules is to determine the most representative features for different patient's sub-groups. Breast cancer, one of the most heterogeneous cancer has five main subtypes, so accurately identify gene signatures...
While high-throughput single cell technologies enable in depth examination of specific cell subsets, these experiments lack the context of these subsets in other cell types and diseases. We compared novel dendritic and monocyte signatures from single cell RNAseq with bulk transcriptome of immune cells to show that the gene signatures for the novel cell subsets are also up-regulated in functionally...
Differential gene expression analysis is one of the significant efforts in single cell RNA sequencing (scRNAseq) analysis to discover the specific changes in expression levels of individual cell types. Since scRNAseq exhibits multimodality, large amounts of zero counts, and sparsity, it is different from the traditional bulk RNA sequencing (RNAseq) data. The new challenges of scRNAseq data promote...
Biomarkers have tremendous potential in different phases of treatment such as risk assessment, screening/detection, diagnosis and patient's response prediction. In this paper, we present an approach for development of a generic tool for an end to end analysis of expression data to identify the probable biomarkers. We follow machine learning as well as network analysis approaches in parallel. We use...
In this paper, we propose a new synchronization-inspired co-clustering algorithm by dynamic simulation, called CoSync, which aims to discover biologically relevant subgroups embedding in a given gene expression data matrix. The basic idea is to view a gene expression data matrix as a dynamical system, and the weighted two-sided interactions are imposed on each element of the matrix from both aspects...
Breast cancer is the most common type of invasive cancer in females. It accounts for 18.2% of all cancer deaths worldwide. Although somatic mutations play important roles in cancer development and prognosis, the outcome predictions are largely based on the expression of marker genes. We submit that developing an innovative prognostic model incorporating somatic mutations with gene expression can improve...
MicroRNA is a type of short non-coding RNAs, which post-transcriptionally regulate gene expressions. It has been well-documented that human microRNAs contribute in the disease development, such as cancers and obesity. While most microRNA functional studies heavily rely on the regulatory interactions between microRNAs and their target messenger RNAs, the accumulating evidence has shown that the altered...
Precision Medicine is one of the most rapidly evolving areas within medicine, which allows utilization of patient's own genomic information in guiding personalized treatments and customized therapy. Applying the public data obtained from human genomic and cancer cell line studies in the diagnosis and treatment of diseases is a challenging task as these data are spread across different repositories...
It is critical to be able to identify longitudinally changing genes in temporal data so that studies can be focused on how gene expression changes in a dynamic way. While biological networks continue to play a significant role in modeling and characterizing complex relationships in biological systems, most network modeling studies in biomedical research focus on snapshot or “static” network-based...
Association rule mining is an important machine learning tool for unveiling critical biological relations between genes from omics data. Previous approaches typically are designed for one single genomic dataset, and most of them use a single minimum support threshold globally. To overcome the above two general limitations, in this work, we present a novel Transcriptomic and Proteomic Rule Mining (TrapRM)...
The NanoString nCounter Analysis System is a medium-throughput gene expression quantification technique that is becoming increasingly popular in the fields of immunology and oncology due to its ease of use and sensitivity, particularly in the analysis of formalin-fixed paraffin embedded samples. Despite the growing interest in NanoString, systematic analysis frameworks for the reproducible analysis...
Genes that share transcription factors are biologically driven to show a more likely measurable correlation in their gene expression. No modern method of visualization displays these intricate co-expression and correlation patterns better than a graph. Structural observations about a co-expression graph can reveal the secrets of the biological system that it models, but experimentally validated co-expression...
Algorithms that map graphs into feature vectors encoding the presence/absence of specific subgraphs, have shown excellent performance in various data mining tasks. Discriminative subgraphs have been successfully utilized as features for graphs classification. Most of the existing algorithms mine for discriminative subgraphs that completely appear frequently in graphs belonging to one class label and...
Biomarkers discovery research requires the integrated analyses of a variety of the data across multiple domains, including clinical data, pathology data, gene expression, epigenetic data. Proper analysis can help understand the biological mechanism and better interpret the impact of the markers to disease. Realising the nature of the data in biomedical research and translational biomedicine, we developed...
Anti-cancer therapies have different responses to different patients. There is a need to identify biomarkers for effectiveness of drugs beside the biomarkers for the diseases like cancer to advance the field personalized medicine. We have used a panel of cancer cell lines from Genomics of Drug Sensitivity in Cancer (GDSC) to capture the sensitivity of drugs. By combining the genetic information such...
With the advent of next-generation sequencing technologies, a considerable effort has been put into sequencing the epigenomes of different species. The efforts such as “Encode” and “Roadmap” epigenomics projects provide an opportunity to compare epigenomes across species (especially between human and mouse). This study is an effort to understand how different histone modifications vary/co-appear between...
Big data analysis has been pervasively adopted as a method to analyze the tremendous amount of daily generated high throughput data in an efficient and accurate manner. Among the series of tools available in the field of big biomedical data, correlation networks are one of the most powerful tools for modelling gene expression, which is important in the study of disease and ageing. With the help of...
Non-negative Matrix Factorization (NMF) is widely used as a data dimensionality reduction tool. However, the assumption of most conventional NMF-based methods is that the gene expression data are only destroyed by Gaussian noise. In practice, the gene expression data are unavoidably destroyed by sparse noise. Although Sparsity-Regularized Robust NMF by using L1/2 constraint (L1/2-RNMF) can achieve...
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