58 research outputs found
Participation of microRNAs in human interactome: extraction of microRNA–microRNA regulations
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ParSel: Parallel Selection of Micro-RNAs for Survival Classification in Cancers
No full textIt is known that tumor micro-RNAs (miRNA) can define patient survival and treatment response. We present a framework to identify miRNAs which are predictive of cancer survival. The framework attempts to rank the miRNAs by exploring their collaborative role in gene regulation. Our approach tests a significantly large number of combinatorial cases leveraging parallel computation. We carefully avoided parametric assumptions involved in evaluations of miRNA expressions but used rigorous statistical computation to assign an importance score to a miRNA. Experimental results on three cancer types namely, KIRC, OV and GBM verify that the top ranked miRNAs obtained using the proposed framework produce better classification accuracy as compared to some best practice variable selection methods. Some of these top ranked miRNA are also known to be associated with related diseases
A scoring scheme for online feature selection: Simulating model performance without retraining
No full textIncreasing the number of features increases the complexity of a model even if the additional feature does not improve its decision-making capacity. Irrelevant features may also cause overfitting and reduce interpretability of the concerned model. It is, therefore, important that the features are optimally selected before a model is built. In the case of online learning, new instances are periodically discovered, and the respective model is tactically retrained as required. Similarly, there are many real-life situations where hundreds of new features are discovered periodically, and the existing model needs to be retrained or tested for its performance improvement. Supervised selection of feature subset usually requires creation of multiple suboptimal models, thus incurring time-intensive computations. Unsupervised selections, although faster, largely rely on some subjective definition of feature relevance. In this paper, we introduce a score that accurately determines the importance of the features. The proposed score is appropriate for online feature selection scenarios for its low time complexity and ability to interpret performance improvement of the current model after the addition of a new feature, without invoking a retraining
Determining a relative importance among ordered lists
No full textImplementations and techniques for determining a relative importance among ordered lists are generally discussed
McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data
No full textMotivation: Single-cell RNA sequencing has been proved to be revolutionary for its potential of zooming into complex biological systems. Genome-wide expression analysis at single-cell resolution provides a window into dynamics of cellular phenotypes. This facilitates the characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on the development or emergence of specific cell populations and phenotypes. However, owing to the paucity of input RNA, a typical single cell RNA sequencing data features a high number of dropout events where transcripts fail to get amplified.Results: We introduce mcImpute, a low-rank matrix completion based technique to impute dropouts in single cell expression data. On a number of real datasets, application of mcImpute yields significant improvements in the separation of true zeros from dropouts, cell-clustering, differential expression analysis, cell type separability, the performance of dimensionality reduction techniques for cell visualization, and gene distribution.Availability and Implementation:https://github.com/aanchalMongia/McImpute_scRNAse
dropClust: efficient clustering of ultra-large scRNA-seq data
No full textDroplet based single cell transcriptomics has recently enabled parallel screening of tens of thousands of single cells. Clustering methods that scale for such high dimensional data without compromising accuracy are scarce. We exploit Locality Sensitive Hashing, an approximate nearest neighbour search technique to develop a de novo clustering algorithm for large-scale single cell data. On a number of real datasets, dropClust outperformed the existing best practice methods in terms of execution time, clustering accuracy and detectability of minor cell sub-types
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