A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics

Background: Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods: Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results: We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions: Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge

PathExpand: Extending biological pathways using molecular interaction networks

We present a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners. The added proteins display distinctive network topological features and molecular function annotations, and can be proposed as putative new components, and/or as regulators of the communication between the different cellular processes. Finally, these extended pathways and processes are used to analyze their enrichment in cancer mutated genes. Significant associations between mutated genes and certain processes are identified, enabling an analysis of the influence of previously non-annotated cancer mutated genes

Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology

Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data. In this study, we demonstrated that machine learning classifiers trained on gene functional similarities, using Gene Ontology (GO), can improve the identification of genes involved in complex diseases. For this purpose, we developed a supervised machine learning methodology to predict complex disease genes. The proposed pipeline was assessed using Autism Spectrum Disorder (ASD) candidate genes. A quantitative measure of gene functional similarities was obtained by employing different semantic similarity measures. To infer the hidden functional similarities between ASD genes, various types of machine learning classifiers were built on quantitative semantic similarity matrices of ASD and non-ASD genes. The classifiers trained and tested on ASD and non-ASD gene functional similarities outperformed previously reported ASD classifiers. For example, a Random Forest (RF) classifier achieved an AUC of 0. 80 for predicting new ASD genes, which was higher than the reported classifier (0.73). Additionally, this classifier was able to predict 73 novel ASD candidate genes that were enriched for core ASD phenotypes, such as autism and obsessive-compulsive behavior. In addition, predicted genes were also enriched for ASD co-occurring conditions, including Attention Deficit Hyperactivity Disorder (ADHD). We also developed a KNIME workflow with the proposed methodology which allows users to configure and execute it without requiring machine learning and programming skills. Machine learning is an effective and reliable technique to decipher ASD mechanism by identifying novel disease genes, but this study further demonstrated that their performance can be improved by incorporating a quantitative measure of gene functional similarities. Source code and the workflow of the proposed methodology are available at https://github.com/Muh-Asif/ASD-genes-prediction.This work was supported by the Portuguese Fundação para a Ciência e Tecnologia (SFRH/BD/52485/2014 to MA and DeST: Deep Semantic Tagger PTDC/CCI-BIO/28685/2017).info:eu-repo/semantics/publishedVersio

Identifying disease-associated genes based on artificial intelligence

Identifying disease-gene associations can help improve the understanding of disease mechanisms, which has a variety of applications, such as early diagnosis and drug development. Although experimental techniques, such as linkage analysis, genome-wide association studies (GWAS), have identified a large number of associations, identifying disease genes is still challenging since experimental methods are usually time-consuming and expensive. To solve these issues, computational methods are proposed to predict disease-gene associations. Based on the characteristics of existing computational algorithms in the literature, we can roughly divide them into three categories: network-based methods, machine learning-based methods, and other methods. No matter what models are used to predict disease genes, the proper integration of multi-level biological data is the key to improving prediction accuracy. This thesis addresses some limitations of the existing computational algorithms, and integrates multi-level data via artificial intelligence techniques. The thesis starts with a comprehensive review of computational methods, databases, and evaluation methods used in predicting disease-gene associations, followed by one network-based method and four machine learning-based methods. The first chapter introduces the background information, objectives of the studies and structure of the thesis. After that, a comprehensive review is provided in the second chapter to discuss the existing algorithms as well as the databases and evaluation methods used in existing studies. Having the objectives and future directions, the thesis then presents five computational methods for predicting disease-gene associations. The first method proposed in Chapter 3 considers the issue of non-disease gene selection. A shortest path-based strategy is used to select reliable non-disease genes from a disease gene network and a differential network. The selected genes are then used by a network-energy model to improve its performance. The second method proposed in Chapter 4 constructs sample-based networks for case samples and uses them to predict disease genes. This strategy improves the quality of protein-protein interaction (PPI) networks, which further improves the prediction accuracy. Chapter 5 presents a generic model which applies multimodal deep belief nets (DBN) to fuse different types of data. Network embeddings extracted from PPI networks and gene ontology (GO) data are fused with the multimodal DBN to obtain cross-modality representations. Chapter 6 presents another deep learning model which uses a convolutional neural network (CNN) to integrate gene similarities with other types of data. Finally, the fifth method proposed in Chapter 7 is a nonnegative matrix factorization (NMF)-based method. This method maps diseases and genes onto a lower-dimensional manifold, and the geodesic distance between diseases and genes are used to predict their associations. The method can predict disease genes even if the disease under consideration has no known associated genes. In summary, this thesis has proposed several artificial intelligence-based computational algorithms to address the typical issues existing in computational algorithms. Experimental results have shown that the proposed methods can improve the accuracy of disease-gene prediction