A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> The identification of genes involved in health and disease remains a challenge. We describe a bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. Unlike previous approaches, ours generates distinct prioritizations for multiple heterogeneous data sources, which are then integrated, or fused, into a global ranking using order statistics. In addition, it offers the flexibility of including additional data sources. Validation of our approach revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorgelike birth defects. The approach described here offers an alternative integrative method for gene discovery. <s> BIB001 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> BackgroundThe majority of common diseases are multi-factorial and modified by genetically and mechanistically complex polygenic interactions and environmental factors. High-throughput genome-wide studies like linkage analysis and gene expression profiling, tend to be most useful for classification and characterization but do not provide sufficient information to identify or prioritize specific disease causal genes.ResultsExtending on an earlier hypothesis that the majority of genes that impact or cause disease share membership in any of several functional relationships we, for the first time, show the utility of mouse phenotype data in human disease gene prioritization. We study the effect of different data integration methods, and based on the validation studies, we show that our approach, ToppGene http://toppgene.cchmc.org, outperforms two of the existing candidate gene prioritization methods, SUSPECTS and ENDEAVOUR.ConclusionThe incorporation of phenotype information for mouse orthologs of human genes greatly improves the human disease candidate gene analysis and prioritization. <s> BIB002 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> A particular challenge in biomedical text mining is to find ways of handling ‘comprehensive’ or ‘associative’ queries such as ‘Find all genes associated with breast cancer’. Given that many queries in genomics, proteomics or metabolomics involve these kind of comprehensive searches we believe that a web-based tool that could support these searches would be quite useful. In response to this need, we have developed the PolySearch web server. PolySearch supports >50 different classes of queries against nearly a dozen different types of text, scientific abstract or bioinformatic databases. The typical query supported by PolySearch is ‘Given X, find all Y's’ where X or Y can be diseases, tissues, cell compartments, gene/protein names, SNPs, mutations, drugs and metabolites. PolySearch also exploits a variety of techniques in text mining and information retrieval to identify, highlight and rank informative abstracts, paragraphs or sentences. PolySearch's performance has been assessed in tasks such as gene synonym identification, protein–protein interaction identification and disease gene identification using a variety of manually assembled ‘gold standard’ text corpuses. Its f-measure on these tasks is 88, 81 and 79%, respectively. These values are between 5 and 50% better than other published tools. The server is freely available at http://wishart.biology.ualberta.ca/polysearch <s> BIB003 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> Endeavour (http://www.esat.kuleuven.be/endeavourweb; this web site is free and open to all users and there is no login requirement) is a web resource for the prioritization of candidate genes. Using a training set of genes known to be involved in a biological process of interest, our approach consists of (i) inferring several models (based on various genomic data sources), (ii) applying each model to the candidate genes to rank those candidates against the profile of the known genes and (iii) merging the several rankings into a global ranking of the candidate genes. In the present article, we describe the latest developments of Endeavour. First, we provide a web-based user interface, besides our Java client, to make Endeavour more universally accessible. Second, we support multiple species: in addition to Homo sapiens, we now provide gene prioritization for three major model organisms: Mus musculus, Rattus norvegicus and Caenorhabditis elegans. Third, Endeavour makes use of additional data sources and is now including numerous databases: ontologies and annotations, protein–protein interactions, cis-regulatory information, gene expression data sets, sequence information and text-mining data. We tested the novel version of Endeavour on 32 recent disease gene associations from the literature. Additionally, we describe a number of recent independent studies that made use of Endeavour to prioritize candidate genes for obesity and Type II diabetes, cleft lip and cleft palate, and pulmonary fibrosis. <s> BIB004 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> ToppGene Suite (http://toppgene.cchmc.org; this web site is free and open to all users and does not require a login to access) is a one-stop portal for (i) gene list functional enrichment, (ii) candidate gene prioritization using either functional annotations or network analysis and (iii) identification and prioritization of novel disease candidate genes in the interactome. Functional annotation-based disease candidate gene prioritization uses a fuzzybased similarity measure to compute the similarity between any two genes based on semantic annotations. The similarity scores from individual features are combined into an overall score using statistical meta-analysis. A P-value of each annotation of a test gene is derived by random sampling of the whole genome. The protein–protein interaction network (PPIN)-based disease candidate gene prioritization uses social and Web networks analysis algorithms (extended versions of the PageRank and HITS algorithms, and the K-Step Markov method). We demonstrate the utility of ToppGene Suite using 20 recently reported GWAS-based gene–disease associations (including novel disease genes) representing five diseases. ToppGene ranked 19 of 20 (95%) candidate genes within the top 20%, while ToppNet ranked 12 of 16 (75%) candidate genes among the top 20%. <s> BIB005 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> Summary: DisGeNET is a plugin for Cytoscape to query and analyze human gene-disease networks. DisGeNET allows user-friendly access to a new gene―disease database that we have developed by integrating data from several public sources. DisGeNET permits queries restricted to (i) the original data source, (ii) the association type, (iii) the disease class or (iv) specific gene(s)/disease(s). It represents gene-disease associations in terms of bipartite graphs and provides gene centric and disease centric views of the data. It assists the user in the interpretation and exploration of the genetic basis of human diseases by a variety of built-in functions. Moreover, DisGeNET permits multicolouring of nodes (genes/diseases) according to standard disease classification for expedient visualization. Availability: DisGeNET is compatible with Cytoscape 2.6.3 and 2.7.0, please visit http://ibi.imim.es/DisGeNET/DisGeNETweb.html for installation guide, user tutorial and download Contact: Ifurlong@imim.es Supplementary information: Supplementary data are available at Bioinformatics online. <s> BIB006 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> BackgroundDiscovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals.To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network.ResultsWe have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (Simple Expression Ranking). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the Heat Kernel Diffusion Ranking leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%.ConclusionIn this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype. <s> BIB007 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> BackgroundThis paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as L∞, L1, and L2 MKL. In particular, L2 MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing L∞ MKL method. In real biomedical applications, L2 MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources.ResultsWe provide a theoretical analysis of the relationship between the L2 optimization of kernels in the dual problem with the L2 coefficient regularization in the primal problem. Understanding the dual L2 problem grants a unified view on MKL and enables us to extend the L2 method to a wide range of machine learning problems. We implement L2 MKL for ranking and classification problems and compare its performance with the sparse L∞ and the averaging L1 MKL methods. The experiments are carried out on six real biomedical data sets and two large scale UCI data sets. L2 MKL yields better performance on most of the benchmark data sets. In particular, we propose a novel L2 MKL least squares support vector machine (LSSVM) algorithm, which is shown to be an efficient and promising classifier for large scale data sets processing.ConclusionsThis paper extends the statistical framework of genomic data fusion based on MKL. Allowing non-sparse weights on the data sources is an attractive option in settings where we believe most data sources to be relevant to the problem at hand and want to avoid a "winner-takes-all" effect seen in L∞ MKL, which can be detrimental to the performance in prospective studies. The notion of optimizing L2 kernels can be straightforwardly extended to ranking, classification, regression, and clustering algorithms. To tackle the computational burden of MKL, this paper proposes several novel LSSVM based MKL algorithms. Systematic comparison on real data sets shows that LSSVM MKL has comparable performance as the conventional SVM MKL algorithms. Moreover, large scale numerical experiments indicate that when cast as semi-infinite programming, LSSVM MKL can be solved more efficiently than SVM MKL.AvailabilityThe MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/l2lssvm.html. <s> BIB008 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-consuming and expensive. Efficient computational methods are therefore needed to prioritize genes within the list of candidates, by exploiting the wealth of information available about the genes in various databases. Here we propose ProDiGe, a novel algorithm for Prioritization of Disease Genes. ProDiGe implements a novel machine learning strategy based on learning from positive and unlabeled examples, which allows to integrate various sources of information about the genes, to share information about known disease genes across diseases, and to perform genome-wide searches for new disease genes. Experiments on real data show that ProDiGe outperforms state-of-the-art methods for the prioritization of genes in human diseases. <s> BIB009 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> BackgroundThe identification of genes involved in human complex diseases remains a great challenge in computational systems biology. Although methods have been developed to use disease phenotypic similarities with a protein-protein interaction network for the prioritization of candidate genes, other valuable omics data sources have been largely overlooked in these methods.MethodsWith this understanding, we proposed a method called BRIDGE to prioritize candidate genes by integrating disease phenotypic similarities with such omics data as protein-protein interactions, gene sequence similarities, gene expression patterns, gene ontology annotations, and gene pathway memberships. BRIDGE utilizes a multiple regression model with lasso penalty to automatically weight different data sources and is capable of discovering genes associated with diseases whose genetic bases are completely unknown.ResultsWe conducted large-scale cross-validation experiments and demonstrated that more than 60% known disease genes can be ranked top one by BRIDGE in simulated linkage intervals, suggesting the superior performance of this method. We further performed two comprehensive case studies by applying BRIDGE to predict novel genes and transcriptional networks involved in obesity and type II diabetes.ConclusionThe proposed method provides an effective and scalable way for integrating multi omics data to infer disease genes. Further applications of BRIDGE will be benefit to providing novel disease genes and underlying mechanisms of human diseases. <s> BIB010 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> The discovery of novel disease-associated variations in genes is often a daunting task in highly heterogeneous disease classes. We seek a generalizable algorithm that integrates multiple publicly available genomic data sources in a machine-learning model for the prioritization of candidates identified in patients with retinal disease. To approach this problem, we generate a set of feature vectors from publicly available microarray, RNA-seq, and ChIP-seq datasets of biological relevance to retinal disease, to observe patterns in gene expression specificity among tissues of the body and the eye, in addition to photoreceptor-specific signals by the CRX transcription factor. Using these features, we describe a novel algorithm, positive and unlabeled learning for prioritization (PULP). This article compares several popular supervised learning techniques as the regression function for PULP. The results demonstrate a highly significant enrichment for previously characterized disease genes using a logistic regression method. Finally, a comparison of PULP with the popular gene prioritization tool ENDEAVOUR shows superior prioritization of retinal disease genes from previous studies. The java source code, compiled binary, assembled feature vectors, and instructions are available online at https://github.com/ahwagner/PULP. <s> BIB011 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> PolySearch2 (http://polysearch.ca) is an online text-mining system for identifying relationships between biomedical entities such as human diseases, genes, SNPs, proteins, drugs, metabolites, toxins, metabolic pathways, organs, tissues, subcellular organelles, positive health effects, negative health effects, drug actions, Gene Ontology terms, MeSH terms, ICD-10 medical codes, biological taxonomies and chemical taxonomies. PolySearch2 supports a generalized ‘Given X, find all associated Ys’ query, where X and Y can be selected from the aforementioned biomedical entities. An example query might be: ‘Find all diseases associated with Bisphenol A’. To find its answers, PolySearch2 searches for associations against comprehensive collections of free-text collections, including local versions of MEDLINE abstracts, PubMed Central full-text articles, Wikipedia full-text articles and US Patent application abstracts. PolySearch2 also searches 14 widely used, text-rich biological databases such as UniProt, DrugBank and Human Metabolome Database to improve its accuracy and coverage. PolySearch2 maintains an extensive thesaurus of biological terms and exploits the latest search engine technology to rapidly retrieve relevant articles and databases records. PolySearch2 also generates, ranks and annotates associative candidates and present results with relevancy statistics and highlighted key sentences to facilitate user interpretation. <s> BIB012 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> Prior biological knowledge and phenotype information may help to identify disease genes from human whole-genome and whole-exome sequencing studies. We developed Phenolyzer (http://phenolyzer.usc.edu), a tool that uses prior information to implicate genes involved in diseases. Phenolyzer exhibits superior performance over competing methods for prioritizing Mendelian and complex disease genes, based on disease or phenotype terms entered as free text. <s> BIB013 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype-phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance. <s> BIB014 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org. <s> BIB015 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders with clinical heterogeneity and a substantial polygenic component. High-throughput methods for ASD risk gene identification produce numerous candidate genes that are time-consuming and expensive to validate. Prioritization methods can identify high-confidence candidates. Previous ASD gene prioritization methods have focused on a priori knowledge, which excludes genes with little functional annotation or no protein product such as long non-coding RNAs (lncRNAs).We have developed a support vector machine (SVM) model, trained using brain developmental gene expression data, for the classification and prioritization of ASD risk genes. The selected feature model had a mean accuracy of 76.7%, mean specificity of 77.2% and mean sensitivity of 74.4%. Gene lists comprised of an ASD risk gene and adjacent genes were ranked using the model's decision function output. The known ASD risk genes were ranked on average in the 77.4th, 78.4th and 80.7th percentile for sets of 101, 201 and 401 genes respectively. Of 10,840 lncRNA genes, 63 were classified as ASD-associated candidates with a confidence greater than 0.95. Genes previously associated with brain development and neurodevelopmental disorders were prioritized highly within the lncRNA gene list.liangjw@clemson.eduSupplementary information: Supplementary data are available at Bioinformatics online. <s> BIB016 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> BackgroundThe human phenome has been widely used with a variety of genomic data sources in the inference of disease genes. However, most existing methods thus far derive phenotype similarity based on the analysis of biomedical databases by using the traditional term frequency-inverse document frequency (TF-IDF) formulation. This framework, though intuitive, not only ignores semantic relationships between words but also tends to produce high-dimensional vectors, and hence lacks the ability to precisely capture intrinsic semantic characteristics of biomedical documents. To overcome these limitations, we propose a framework called mimvec to analyze the human phenome by making use of the state-of-the-art deep learning technique in natural language processing.ResultsWe converted 24,061 records in the Online Mendelian Inheritance in Man (OMIM) database to low-dimensional vectors using our method. We demonstrated that the vector presentation not only effectively enabled classification of phenotype records against gene ones, but also succeeded in discriminating diseases of different inheritance styles and different mechanisms. We further derived pairwise phenotype similarities between 7988 human inherited diseases using their vector presentations. With a joint analysis of this phenome with multiple genomic data, we showed that phenotype overlap indeed implied genotype overlap. We finally used the derived phenotype similarities with genomic data to prioritize candidate genes and demonstrated advantages of this method over existing ones.ConclusionsOur method is capable of not only capturing semantic relationships between words in biomedical records but also alleviating the dimensional disaster accompanying the traditional TF-IDF framework. With the approaching of precision medicine, there will be abundant electronic records of medicine and health awaiting for deep analysis, and we expect to see a wide spectrum of applications borrowing the idea of our method in the near future. <s> BIB017 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> BACKGROUND ::: The inflammatory bowel diseases (IBDs) are chronic inflammatory disorders, associated with genetic, immunologic, and environmental factors. Although hundreds of genes are implicated in IBD etiology, it is likely that additional genes play a role in the disease process. We developed a machine learning-based gene prioritization method to identify novel IBD-risk genes. ::: ::: ::: METHODS ::: Known IBD genes were collected from genome-wide association studies and annotated with expression and pathway information. Using these genes, a model was trained to identify IBD-risk genes. A comprehensive list of 16,390 genes was then scored and classified. ::: ::: ::: RESULTS ::: Immune and inflammatory responses, as well as pathways such as cell adhesion, cytokine-cytokine receptor interaction, and sulfur metabolism were identified to be related to IBD. Scores predicted for IBD genes were significantly higher than those for non-IBD genes (P < 10). There was a significant association between the score and having an IBD publication (P < 10). Overall, 347 genes had a high prediction score (>0.8). A literature review of the genes, excluding those used to train the model, identified 67 genes without any publication concerning IBD. These genes represent novel candidate IBD-risk genes, which can be targeted in future studies. ::: ::: ::: CONCLUSIONS ::: Our method successfully differentiated IBD-risk genes from non-IBD genes by using information from expression data and a multitude of gene annotations. Crucial features were defined, and we were able to detect novel candidate risk genes for IBD. These findings may help detect new IBD-risk genes and improve the understanding of IBD pathogenesis. <s> BIB018 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> Abstract In solving the gene prioritization problem, ranking candidate genes from most to least promising is attempted before further experimental validation. Integrating the results of various data sources and methods tends to result in a better performance when solving the gene prioritization problem. Therefore, a wide range of datasets and algorithms was investigated; these included topological features of protein networks, physicochemical characteristics and blast similarity scores of protein sequences, gene ontology, biological pathways, and tissue-based data sources. The novelty of this study lies in how the best-performing methods and reliable multi-genomic data sources were applied in an efficient two-step approach. In the first step, various multi-genomic data sources and algorithms were evaluated and seven best-performing rankers were then applied to prioritize candidate genes in different ways. In the second step, global prioritization was obtained by aggregating several scoring schemes. The results showed that protein networks, functional linkage networks, gene ontology, and biological pathway data sources have a significant impact on the quality of the gene prioritization approach. The findings also demonstrated a direct relationship between the degree of genes and the ranking quality of the evaluated tools. This approach outperformed previously published algorithms (e.g., DIR, GPEC, GeneDistiller, and Endeavour) in all evaluation metrices and led to the development of GPS software. Its user-friendly interface and accuracy makes GPS a powerful tool for the identification of human disease genes. GPS is available at http://gpsranker.com and http://LBB.ut.ac.ir . <s> BIB019 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> Motivation Computational gene prioritization can aid in disease gene identification. Here, we propose pBRIT (prioritization using Bayesian Ridge regression and Information Theoretic model), a novel adaptive and scalable prioritization tool, integrating Pubmed abstracts, Gene Ontology, Sequence similarities, Mammalian and Human Phenotype Ontology, Pathway, Interactions, Disease Ontology, Gene Association database and Human Genome Epidemiology database, into the prediction model. We explore and address effects of sparsity and inter‐feature dependencies within annotation sources, and the impact of bias towards specific annotations. Results pBRIT models feature dependencies and sparsity by an Information‐Theoretic (data driven) approach and applies intermediate integration based data fusion. Following the hypothesis that genes underlying similar diseases will share functional and phenotype characteristics, it incorporates Bayesian Ridge regression to learn a linear mapping between functional and phenotype annotations. Genes are prioritized on phenotypic concordance to the training genes. We evaluated pBRIT against nine existing methods, and on over 2000 HPO‐gene associations retrieved after construction of pBRIT data sources. We achieve maximum AUC scores ranging from 0.92 to 0.96 against benchmark datasets and of 0.80 against the time‐stamped HPO entries, indicating good performance with high sensitivity and specificity. Our model shows stable performance with regard to changes in the underlying annotation data, is fast and scalable for implementation in routine pipelines. Availability and implementation http://biomina.be/apps/pbrit/; https://bitbucket.org/medgenua/pbrit. <s> BIB020 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Score Aggregation <s> BackgroundThe uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability.ResultsWe propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods.ConclusionsScuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba. <s> BIB021
This group includes tools implementing various strategies of aggregating all found pieces of evidence within each data source and then between different data sources into a total score. For example, Polysearch BIB012 , BIB003 recognizes sentences supporting gene-disease associations, weights them according to their reliability and summarizes weights into the total relevancy score. When relevancy scores computed for all genes, Polysearch standardizes them and uses for prioritization. Similarly, DisGeNET BIB014 ,  , BIB006 and Open Targets BIB015 integrate data from multiple evidence sources. For each gene, they compute a weighted sum over all individual genedisease association scores. Each weighting coefficient is determined by the reliability of association and the type of data source it came from. Thus, strong genetic associations discovered in human make a bigger impact into the overall gene score, than less reliable associations inferred from animal models or text mining. Tools operating with seed genes employ similar idea to summarize gene-gene associations. Initially, they score each candidate by its similarity with seeds, considering each evidence sources independently, and then combine all data source-specific scores into a total score. GPS BIB019 follows the most straightforward way to integrate multiple rankings: for each gene, it calculates a simple rank average over seven independent rankings. ToppGene BIB005 , BIB002 and Endeavour BIB001 ,  , BIB004 realize more sophisticated approaches to obtain the overall ranking. They convert data source-specific scores into p-values and apply meta-analysis-based techniques to compute the overall p-value for each gene. Score aggregation approaches described above have at least two drawbacks. First, these tools favor genes top-ranked in a maximal number of evidence sources. Meanwhile, they may not consider various reliability and potential dependency of evidence sources. Second, tools from this category do not take into account the fact that the impact of independent rankings into the total score may not be additive. These deficiencies have been partly overcome with the development of machine learning methods. Similar to ToppGene and Endeavour, machine learning-based methods represent genes as n-dimensional feature vectors, use seed genes as positive training exemplars, genes other than seeds or candidates as negative exemplars, and then classify candidates. Machine learning methods such as multiple linear  , BIB020 , BIB010 and logistic BIB013 , BIB011 regressions, kernel-based approaches BIB007 , BIB016 , BIB021 , neural networks BIB017 and others BIB018 were successfully applied for gene prioritization. Recent works have demonstrated that machine learning-based methods tend to outperform other score aggregators BIB011 , BIB008 , BIB009 , possibly owing to their ability to capture unknown or non-linear feature relationships and tuning model parameters.