Ensemble Methods for MiRNA Target Prediction from Expression Data

Background

microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear relationships between variables. For such reasons, computational methods are often subject to the problem of inconsistent performance across different datasets. On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory.

Results

In this paper, we investigate the performance of some ensemble methods over the commonly used miRNA target prediction methods. We apply eight different popular miRNA target prediction methods to three cancer datasets, and compare their performance with the ensemble methods which integrate the results from each combination of the individual methods. The validation results using experimentally confirmed databases show that the results of the ensemble methods complement those obtained by the individual methods and the ensemble methods perform better than the individual methods across different datasets. The ensemble method, Pearson+IDA+Lasso, which combines methods in different approaches, including a correlation method, a causal inference method, and a regression method, is the best performed ensemble method in this study. Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched. The source codes, datasets, miRNA target predictions by all methods, and the ground truth for validation are available in the Supplementary materials.     

去细胞方法及其在组织工程中的应用

去细胞基质在组织工程及再生医学的大量应用为解决组织器官的修复和重建等难题带来了希望。去细胞方法大致可以分为三类:化学处理法、物理处理法及酶学处理法,且已经应用于组织工程及再生医学的各个方面。本文总结并分类目前常用的去细胞方法及其在组织工程各方面的应用,对目前国内外常用的去细胞方法及其在组织工程及再生医学中的应用进行回顾总结与分析。     

Spatial analysis of two-species interactions

Summary In this paper, I present and discuss some methods for the analysis of univariate and bivariate spatial point pattern data. Examples of such data in ecology include x-y coordinates of organisms in mapped field plots. I illustrate the methods with analyses of data from mapped field plots on Mount St. Helens, Washington state, USA. The statistical methods I emphasize are graphical methods that rely on analysis of distances between organisms. Hypothesis testing for methods like these is easily done using Monte Carlo methods, which I also discuss. For both univariate and bivariate analyses, I find that second-order methods such as K-function plots are often preferable to first-order methods (i.e., QQ-plots). However, for multivariate analyses, these second-order methods are more sensitive to small sample sizes than first-order analyses.     

A Benchmark of Parametric Methods for Horizontal Transfers Detection

Horizontal gene transfer (HGT) has appeared to be of importance for prokaryotic species evolution. As a consequence numerous parametric methods, using only the information embedded in the genomes, have been designed to detect HGTs. Numerous reports of incongruencies in results of the different methods applied to the same genomes were published. The use of artificial genomes in which all HGT parameters are controlled allows testing different methods in the same conditions. The results of this benchmark concerning 16 representative parametric methods showed a great variety of efficiencies. Some methods work very poorly whatever the type of HGTs and some depend on the conditions or on the metrics used. The best methods in terms of total errors were those using tetranucleotides as criterion for the window methods or those using codon usage for gene based methods and the Kullback-Leibler divergence metric. Window methods are very sensitive but less specific and detect badly lone isolated gene. On the other hand gene based methods are often very specific but lack of sensitivity. We propose using two methods in combination to get the best of each category, a gene based one for specificity and a window based one for sensitivity.     

A simulation-based evaluation of methods for inferring linear barriers to gene flow

Different analytical techniques used on the same data set may lead to different conclusions about the existence and strength of genetic structure. Therefore, reliable interpretation of the results from different methods depends on the efficacy and reliability of different statistical methods. In this paper, we evaluated the performance of multiple analytical methods to detect the presence of a linear barrier dividing populations. We were specifically interested in determining if simulation conditions, such as dispersal ability and genetic equilibrium, affect the power of different analytical methods for detecting barriers. We evaluated two boundary detection methods (Monmonier's algorithm and WOMBLING), two spatial Bayesian clustering methods (TESS and GENELAND), an aspatial clustering approach (STRUCTURE), and two recently developed, non-Bayesian clustering methods [PSMIX and discriminant analysis of principal components (DAPC)]. We found that clustering methods had higher success rates than boundary detection methods and also detected the barrier more quickly. All methods detected the barrier more quickly when dispersal was long distance in comparison to short-distance dispersal scenarios. Bayesian clustering methods performed best overall, both in terms of highest success rates and lowest time to barrier detection, with GENELAND showing the highest power. None of the methods suggested a continuous linear barrier when the data were generated under an isolation-by-distance (IBD) model. However, the clustering methods had higher potential for leading to incorrect barrier inferences under IBD unless strict criteria for successful barrier detection were implemented. Based on our findings and those of previous simulation studies, we discuss the utility of different methods for detecting linear barriers to gene flow.     

Structure- and sequence-based function prediction for non-homologous proteins

The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recent developments of local structure-based and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, protein function prediction and extended similarity group, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures.     

A comparison of methods for measuring primary productivity and community respiration in streams

Carbon dioxide and oxygen exchange procedures for measuring community metabolism (two open stream methods and three chamber methods) were compared on the same reach of a third-order stream. Open stream methods were complicated by high diffusion rates and yielded net community primary productivity estimates lower than those obtained with chamber methods. Chamber methods yielded variable productivity and respiration data. However, when normalized for chlorophyll a, productivity estimates from the chamber methods were within an expected range for the system. Balances of photosynthesis and respiration from the chamber methods were similar between methods and indicated that autotrophic or heterotrophic processes could dominate the system. Considerations in applying the various procedures are discussed.     

母体外周血中胎儿有核红细胞的分离和富集方法的研究进展

母体外周血中分离的胎儿有核红细胞（fNRBCs）包含胎儿完整的遗传信息,可用于无创产前诊断。fNRBCs的分离和富集方法主要分为三类：物理分选法、抗原-抗体结合分离法和增殖法。不同的方法获得的fNRBCs的数量和纯度不同,多种方法联合使用可以提高富集产物中fNRBCs的纯度和数量。本文就母体外周血中fNRBCs的分离和富集方法进行综述。     

Detection and identification of microorganisms in wine: a review of molecular techniques

The microbial ecology of wine is complex. Microbes can play both positive and negative roles in the quality of the final product. Due to this impact, the microbial ecology of wine has been well studied. Traditional indirect methods, such as plating, have largely been replaced by a number of molecular methods. These methods are typically either indirect methods used for identification of cultured organisms, or direct methods used to profile whole populations or identify specific microbes in a mixed population. These molecular methods offer a number of advantages over traditional methods including speed and precision. This review will examine both direct and indirect molecular methods, provide examples of their impact on the study of the microbial ecology of wine, and also discuss their strengths and limitations.     

Cytochemistry of mature angiosperm pollen

The problems involved in applying histochemical and cytochemical methods to mature angiosperm pollen for bright light and fluorescence microscopy are discussed. These methods can be used for general examination or to reveal particular structures or groups of substances. The main methods of testing pollen viability and germinability based on stains and semiquantitative methods are also reviewed. The main methods of staining and their applications are summarised.     

去细胞方法及其在组织工程中的应用

去细胞基质在组织工程及再生医学的大量应用为解决组织器官的修复和重建等难题带来了希望。去细胞方法大致可以分为三类：化学处理法、物理处理法及酶学处理法,且已经应用于组织工程及再生医学的各个方面。本文总结并分类目前常用的去细胞方法及其在组织工程各方面的应用,对目前国内外常用的去细胞方法及其在组织工程及再生医学中的应用进行回顾总结与分析。     

膜蛋白跨膜区预测方法的评价

基因组计划所产生的大量蛋白质序列迫切需要从理论上预测跨膜区。对现有预测跨膜区的方法进行评价 ,不仅可以帮助生物学家选择合适的方法 ,而且可以为生物信息学家发展新算法提供指导。采用了最新的膜蛋白数据库作为基本测试集合并选择了水溶性蛋白序列作为对照组 ,对目前已经公开发表且提供网上服务的跨膜区预测方法进行了评价和分析。经过分析比较 ,HMMTOP在所有的方法中综合预测效果最佳     

Fine-scale mapping in case-control samples using locus scoring and haplotype-sharing methods

Both haplotype-based and locus-based methods have been proposed as the most powerful methods to employ when fine mapping by association. Although haplotype-based methods utilize more information, they may lose power as a result of overparameterization, given the large number of haplotypes possible over even a few loci. Recently methods have been developed that cluster haplotypes with similar structure in the hope that this reflects shared genealogical ancestry. The aim is to reduce the number of parameters while retaining the genotype information relating to disease susceptibility. We have compared several haplotype-based methods with locus-based methods. We utilized 2 regions (D2 and D4) simulated to be in linkage disequilibrium and to be associated with disease susceptibility, combining 5 replicates at a time to produce 4 datasets that were analyzed. We found little difference in the performance of the haplotype-based methods and the locus-based methods in this dataset.     

Meta-analysis of the difference of medians

We consider the problem of meta-analyzing two-group studies that report the median of the outcome. Often, these studies are excluded from meta-analysis because there are no well-established statistical methods to pool the difference of medians. To include these studies in meta-analysis, several authors have recently proposed methods to estimate the sample mean and standard deviation from the median, sample size, and several commonly reported measures of spread. Researchers frequently apply these methods to estimate the difference of means and its variance for each primary study and pool the difference of means using inverse variance weighting. In this work, we develop several methods to directly meta-analyze the difference of medians. We conduct a simulation study evaluating the performance of the proposed median-based methods and the competing transformation-based methods. The simulation results show that the median-based methods outperform the transformation-based methods when meta-analyzing studies that report the median of the outcome, especially when the outcome is skewed. Moreover, we illustrate the various methods on a real-life data set.     

Differential expression analyses for single-cell RNA-Seq: old questions on new data

Background: Single-cell RNA sequencing (scRNA-seq) is an emerging technology that enables high resolution detection of heterogeneities between cells. One important application of scRNA-seq data is to detect differential expression (DE) of genes. Currently, some researchers still use DE analysis methods developed for bulk RNA-Seq data on single-cell data, and some new methods for scRNA-seq data have also been developed. Bulk and single-cell RNA-seq data have different characteristics. A systematic evaluation of the two types of methods on scRNA-seq data is needed. Results: In this study, we conducted a series of experiments on scRNA-seq data to quantitatively evaluate 14 popular DE analysis methods, including both of traditional methods developed for bulk RNA-seq data and new methods specifically designed for scRNA-seq data. We obtained observations and recommendations for the methods under different situations. Conclusions: DE analysis methods should be chosen for scRNA-seq data with great caution with regard to different situations of data. Different strategies should be taken for data with different sample sizes and/or different strengths of the expected signals. Several methods for scRNA-seq data show advantages in some aspects, and DEGSeq tends to outperform other methods with respect to consistency, reproducibility and accuracy of predictions on scRNA-seq data.     

A comparison of the accuracy and precision of several open-water oxygen productivity techniques

Several methods for the estimation of the reaeration coefficient were compared by determining the ability of the methods to recover the correct K value from a computer-simulated stream oxygen record affected by a variety of non-ideal conditions. Noisy data and long observation intervals were not a serious problem for most methods. Saturating photosynthesis, fluctuating light intensity, afternoon depression and temperature variation caused failures by some methods but were well handled by others. Serious impairment of all methods occurred with low productivity or high K. In general, the best-performing methods were the modified hysteresis, nighttime regression, daytime regression, Odum and Hornberger-Kelly daytime methods.     

Confidence measures for protein fold recognition

MOTIVATION: We present an extensive evaluation of different methods and criteria to detect remote homologs of a given protein sequence. We investigate two associated problems: first, to develop a sensitive searching method to identify possible candidates and, second, to assign a confidence to the putative candidates in order to select the best one. For searching methods where the score distributions are known, p-values are used as confidence measure with great success. For the cases where such theoretical backing is absent, we propose empirical approximations to p-values for searching procedures. RESULTS: As a baseline, we review the performances of different methods for detecting remote protein folds (sequence alignment and threading, with and without sequence profiles, global and local). The analysis is performed on a large representative set of protein structures. For fold recognition, we find that methods using sequence profiles generally perform better than methods using plain sequences, and that threading methods perform better than sequence alignment methods. In order to assess the quality of the predictions made, we establish and compare several confidence measures, including raw scores, z-scores, raw score gaps, z-score gaps, and different methods of p-value estimation. We work our way from the theoretically well backed local scores towards more explorative global and threading scores. The methods for assessing the statistical significance of predictions are compared using specificity--sensitivity plots. For local alignment techniques we find that p-value methods work best, albeit computationally cheaper methods such as those based on score gaps achieve similar performance. For global methods where no theory is available methods based on score gaps work best. By using the score gap functions as the measure of confidence we improve the more powerful fold recognition methods for which p-values are unavailable. AVAILABILITY: The benchmark set is available upon request.     

Methods for gas chromatography-olfactometry: a review

Gas chromatography-olfactometry methods are used in flavor research to determine the odor active compounds in foods. In this review, the four major methods for gas chromatography-olfactometry are described and their potentials and limitations discussed. The methods include dilution analysis, detection frequency methods, posterior intensity methods and time-intensity methods. The value of gas chromatography olfactometry data is shown to depend directly on the gas chromatography-olfactometry method, as well as on sample preparation and analytical conditions. Each of the methods has been used frequently and has its advantages and disadvantages. However, on the methodological side, a considerable area is still to be explored, which would contribute to the interpretation of the data and would improve the value of these techniques for both fundamental and applied research.     

Review of the methods used in the determination of phytoestrogens

Interest in analytical methods for plant estrogens (phytoestrogens) has risen sharply in the past 10 years. In this review, we examine the existing analytical methods based on separations by gas-liquid chromatography, high-performance liquid chromatography and capillary electrophoresis in addition to methods of detection by ultraviolet absorption, fluorescence, electrochemical oxidation/reduction and mass spectrometry. These methods are compared with other methods of phytoestrogen analysis utilizing immunoassay approaches. The advantages and disadvantages of each of these methods are highlighted and potential areas for further development identified.     

Evaluation of residue-residue contact prediction methods: From retrospective to prospective

Sequence-based residue contact prediction plays a crucial role in protein structure reconstruction. In recent years, the combination of evolutionary coupling analysis (ECA) and deep learning (DL) techniques has made tremendous progress for residue contact prediction, thus a comprehensive assessment of current methods based on a large-scale benchmark data set is very needed. In this study, we evaluate 18 contact predictors on 610 non-redundant proteins and 32 CASP13 targets according to a wide range of perspectives. The results show that different methods have different application scenarios: (1) DL methods based on multi-categories of inputs and large training sets are the best choices for low-contact-density proteins such as the intrinsically disordered ones and proteins with shallow multi-sequence alignments (MSAs). (2) With at least 5L (L is sequence length) effective sequences in the MSA, all the methods show the best performance, and methods that rely only on MSA as input can reach comparable achievements as methods that adopt multi-source inputs. (3) For top L/5 and L/2 predictions, DL methods can predict more hydrophobic interactions while ECA methods predict more salt bridges and disulfide bonds. (4) ECA methods can detect more secondary structure interactions, while DL methods can accurately excavate more contact patterns and prune isolated false positives. In general, multi-input DL methods with large training sets dominate current approaches with the best overall performance. Despite the great success of current DL methods must be stated the fact that there is still much room left for further improvement: (1) With shallow MSAs, the performance will be greatly affected. (2) Current methods show lower precisions for inter-domain compared with intra-domain contact predictions, as well as very high imbalances in precisions between intra-domains. (3) Strong prediction similarities between DL methods indicating more feature types and diversified models need to be developed. (4) The runtime of most methods can be further optimized.