Study of meta-analysis strategies for network inference using information-theoretic approaches

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches focused on individual datasets, which typically suffer from some experimental bias and a small number of samples. To date, there are mainly two strategies for the problem of interest: the first one (”data merging”) merges all datasets together and then infers a GRN whereas the other (”networks ensemble”) infers GRNs from every dataset separately and then aggregates them using some ensemble rules (such as ranksum or weightsum). Unfortunately, a thorough comparison of these two approaches is lacking. In this paper, we evaluate the performances of various metaanalysis approaches mentioned above with a systematic set of experiments based on in silico benchmarks. Furthermore, we present a new meta-analysis approach for inferring GRNs from multiple studies. Our proposed approach, adapted to methods based on pairwise measures such as correlation or mutual information, consists of two steps: aggregating matrices of the pairwise measures from every dataset followed by extracting the network from the meta-matrix.Peer ReviewedPostprint (author's final draft

Multiclass microarray gene expression classification based on fusion of correlation features

In this paper, we propose novel algorithmic models based on fusion of independent and correlated gene features for multiclass microarray gene expression classification. It is possible for genes to get co-expressed via different pathways. Moreover, a gene may or may not be co-active for all samples. In this paper, we approach this problem with a optimal feature selection technique using analysis based on statistical techniques to model the complex interactions between genes. The two different types of correlation modelling techniques based on the cross modal factor analysis (CFA) and canonical correlation analysis (CCA) were examined. The subsequent fusion of CCA/CFA features with principal component analysis (PCA) features at feature-level, and at score-level result in significant enhancement in classification accuracy for different data sets corresponding to multiclass microarray gene expression data

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data

<p>Abstract</p> <p>Background</p> <p>Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new hypotheses. Principal Component Analysis (PCA) is a widely used linear method to define the mapping between the high-dimensional data and its low-dimensional representation. During the last decade, many new nonlinear methods for dimension reduction have been proposed, but it is still unclear how well these methods capture the underlying structure of microarray gene expression data. In this study, we assessed the performance of the PCA approach and of six nonlinear dimension reduction methods, namely Kernel PCA, Locally Linear Embedding, Isomap, Diffusion Maps, Laplacian Eigenmaps and Maximum Variance Unfolding, in terms of visualization of microarray data.</p> <p>Results</p> <p>A systematic benchmark, consisting of Support Vector Machine classification, cluster validation and noise evaluations was applied to ten microarray and several simulated datasets. Significant differences between PCA and most of the nonlinear methods were observed in two and three dimensional target spaces. With an increasing number of dimensions and an increasing number of differentially expressed genes, all methods showed similar performance. PCA and Diffusion Maps responded less sensitive to noise than the other nonlinear methods.</p> <p>Conclusions</p> <p>Locally Linear Embedding and Isomap showed a superior performance on all datasets. In very low-dimensional representations and with few differentially expressed genes, these two methods preserve more of the underlying structure of the data than PCA, and thus are favorable alternatives for the visualization of microarray data.</p

Neural plasma

This paper presents a novel type of artificial neural network, called neural plasma, which is tailored for classification tasks involving few observations with a large number of variables. Neural plasma learns to adapt its classification confidence by generating artificial training data as a function of its confidence in previous decisions. In contrast to multilayer perceptrons and similar techniques, which are inspired by topological and operational aspects of biological neural networks, neural plasma is motivated by aspects of high-level behavior and reasoning in the presence of uncertainty. The basic principles of the proposed model apply to other supervised learning algorithms that provide explicit classification confidence values. The empirical evaluation of this new technique is based on benchmarking experiments involving data sets from biotechnology that are characterized by the small-n-large-p problem. The presented study exposes a comprehensive methodology and is seen as a first step in exploring different aspects of this methodology.IFIP International Conference on Artificial Intelligence in Theory and Practice - Neural NetsRed de Universidades con Carreras en Informática (RedUNCI

Circular RNAs in Clear Cell Renal Cell Carcinoma: Their Microarray-Based Identification, Analytical Validation, and Potential Use in a Clinico-Genomic Model to Improve Prognostic Accuracy

Circular RNAs (circRNAs) may act as novel cancer biomarkers. However, a genome-wide evaluation of circRNAs in clear cell renal cell carcinoma (ccRCC) has yet to be conducted. Therefore, the objective of this study was to identify and validate circRNAs in ccRCC tissue with a focus to evaluate their potential as prognostic biomarkers. A genome-wide identification of circRNAs in total RNA extracted from ccRCC tissue samples was performed using microarray analysis. Three relevant differentially expressed circRNAs were selected (circEGLN3, circNOX4, and circRHOBTB3), their circular nature was experimentally confirmed, and their expression-along with that of their linear counterparts-was measured in 99 malignant and 85 adjacent normal tissue samples using specifically established RT-qPCR assays. The capacity of circRNAs to discriminate between malignant and adjacent normal tissue samples and their prognostic potential (with the endpoints cancer-specific, recurrence-free, and overall survival) after surgery were estimated by C-statistics, Kaplan-Meier method, univariate and multivariate Cox regression analysis, decision curve analysis, and Akaike and Bayesian information criteria. CircEGLN3 discriminated malignant from normal tissue with 97% accuracy. We generated a prognostic for the three endpoints by multivariate Cox regression analysis that included circEGLN3, circRHOBT3 and linRHOBTB3. The predictive outcome accuracy of the clinical models based on clinicopathological factors was improved in combination with this circRNA-based signature. Bootstrapping as well as Akaike and Bayesian information criteria confirmed the statistical significance and robustness of the combined models. Limitations of this study include its retrospective nature and the lack of external validation. The study demonstrated the promising potential of circRNAs as diagnostic and particularly prognostic biomarkers in ccRCC patients

Pattern Recognition Software and Techniques for Biological Image Analysis

The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays

Multi-class gene expression biomarker panel identification for the diagnosis of paediatric febrile illness

Febrile illness in children can result from infections by diverse viral or bacterial pathogens as well as inflammatory conditions or cancer. The limitations of the existing diagnostic pipeline, which relies on clinical symptoms and signs, pathogen detection, empirical treatment and diagnoses of exclusion, contribute to missed or de- layed diagnosis and unnecessary antibiotic use. The potential of host gene expression biomarkers measured in blood has been demonstrated for simplified binary diagnostic questions however, the clinical reality is that multiple potential aetiologies must be considered and prioritised on the basis of likelihood and risks of severe disease. In order to identify a biomarker panel which better reflects this clinical reality, we applied a multi-class supervised learning approach to whole blood transcriptomic datasets from children with infectious and inflammatory disease. Three datasets were used for the analyses presented here, a single microarray dataset, a meta-analysis of 12 publicly available microarray datasets and a newly generated RNA-sequencing dataset. These were used for preliminary investigations of the approach, discovery of a multi-class biomarker panel of febrile illness and valida- tion of the biomarker panel respectively. In the merged microarray discovery dataset a two-stage approach to feature selection and classification, based on LASSO and Ridge penalised regression was applied to distinguish 18 disease classes. Cost-sensitivity was incorporated in the approach as aetiologies of febrile illness vary considerably in the risk of severe disease. The resulting 161 transcript biomarker panel could reliably distinguish bacterial, viral, inflammatory, tuberculosis and malarial disease as well as pathogen specific aetiologies. The panel was then validated in a newly generated RNA-Seq dataset and compared to previously published binary biomarker panels. The analyses presented here demonstrate that a single test for the diagnosis of acute febrile illness in children is possible using host RNA biomarkers. A test which could distinguish multiple aetiologies soon after presentation could be used to reduce unnecessary antibiotic use, improve targetting of antibiotics to bacterial species and reduce delays in the diagnosis of inflammatory diseases.Open Acces