MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm

Large scale in silico characterization of repeat expansion variation in human genomes

Significant progress has been made in elucidating single nucleotide polymorphism diversity in the human population. However, the majority of the variation space in the genome is structural and remains partially elusive. One form of structural variation is tandem repeats (TRs). Expansion of TRs are responsible for over 40 diseases, but we hypothesize these represent only a fraction of the pathogenic repeat expansions that exist. Here we characterize long or expanded TR variation in 1,115 human genomes as well as a replication cohort of 2,504 genomes, identified using ExpansionHunter Denovo. We found that individual genomes typically harbor several rare, large TRs, generally in non-coding regions of the genome. We noticed that these large TRs are enriched in their proximity to Alu elements. The vast majority of these large TRs seem to be expansions of smaller TRs that are already present in the reference genome. We are providing this TR profile as a resource for comparison to undiagnosed rare disease genomes in order to detect novel disease-causing repeat expansions.

A New Approach for Predicting the Value of Gene Expression: Two-way Collaborative Filtering

Background:

Predicting the value of gene expression in a given condition is a challenging topic in computational systems biology. Only a limited number of studies in this area have provided solutions to predict the expression in a particular pattern, whether or not it can be done effectively. However, the value of expression for the measurement is usually needed for further meta-data analysis.

Methods:

Because the problem is considered as a regression task where a feature representation of the gene under consideration is fed into a trained model to predict a continuous variable that refers to its exact expression level, we introduced a novel feature representation scheme to support work on such a task based on two-way collaborative filtering. At this point, our main argument is that the expressions of other genes in the current condition are as important as the expression of the current gene in other conditions. For regression analysis, linear regression and a recently popularized method, called Relevance Vector Machine (RVM), are used. Pearson and Spearman correlation coefficients and Root Mean Squared Error are used for evaluation. The effects of regression model type, RVM kernel functions, and parameters have been analysed in our study in a gene expression profiling data comprising a set of prostate cancer samples.

Results:

According to the findings of this study, in addition to promising results from the experimental studies, integrating data from another disease type, such as colon cancer in our case, can significantly improve the prediction performance of the regression model.

Conclusion:

The results also showed that the performed new feature representation approach and RVM regression model are promising for many machine learning problems in microarray and high throughput sequencing analysis.

Classifying Incomplete Gene-Expression Data: Ensemble Learning with Non-Pre-Imputation Feature Filtering and Best-First Search Technique

(1) Background: Gene-expression data usually contain missing values (MVs). Numerous methods focused on how to estimate MVs have been proposed in the past few years. Recent studies show that those imputation algorithms made little difference in classification. Thus, some scholars believe that how to select the informative genes for downstream classification is more important than how to impute MVs. However, most feature-selection (FS) algorithms need beforehand imputation, and the impact of beforehand MV imputation on downstream FS performance is seldom considered. (2) Method: A modified chi-square test-based FS is introduced for gene-expression data. To deal with the challenge of a small sample size of gene-expression data, a heuristic method called recursive element aggregation is proposed in this study. Our approach can directly handle incomplete data without any imputation methods or missing-data assumptions. The most informative genes can be selected through a threshold. After that, the best-first search strategy is utilized to find optimal feature subsets for classification. (3) Results: We compare our method with several FS algorithms. Evaluation is performed on twelve original incomplete cancer gene-expression datasets. We demonstrate that MV imputation on an incomplete dataset impacts subsequent FS in terms of classification tasks. Through directly conducting FS on incomplete data, our method can avoid potential disturbances on subsequent FS procedures caused by MV imputation. An experiment on small, round blue cell tumor (SRBCT) dataset showed that our method found additional genes besides many common genes with the two compared existing methods.