PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research.

Understanding the relationship between the pathophysiology of infectious disease, the biology of the causative agent and the development of therapeutic and diagnostic approaches is dependent on the synthesis of a wide range of types of information. Provision of a comprehensive and integrated disease phenotype knowledgebase has the potential to provide novel and orthogonal sources of information for the understanding of infectious agent pathogenesis, and support for research on disease mechanisms. We have developed PathoPhenoDB, a database containing pathogen-to-phenotype associations. PathoPhenoDB relies on manual curation of pathogen-disease relations, on ontology-based text mining as well as manual curation to associate host disease phenotypes with infectious agents. Using Semantic Web technologies, PathoPhenoDB also links to knowledge about drug resistance mechanisms and drugs used in the treatment of infectious diseases. PathoPhenoDB is accessible at http://patho.phenomebrowser.net/ , and the data are freely available through a public SPARQL endpoint

Vec2SPARQL:integrating SPARQL queries and knowledge graph embeddings

<div>Recent developments in machine learning have led to a rise of large</div><div>number of methods for extracting features from structured data. The features</div><div>are represented as vectors and may encode for some semantic aspects of data.</div><div>They can be used in a machine learning models for different tasks or to com-</div><div>pute similarities between the entities of the data. SPARQL is a query language</div><div>for structured data originally developed for querying Resource Description Frame-</div><div>work (RDF) data. It has been in use for over a decade as a standardized NoSQL</div><div>query language. Many different tools have been developed to enable data shar-</div><div>ing with SPARQL. For example, SPARQL endpoints make your data interopera-</div><div>ble and available to the world. SPARQL queries can be executed across multi-</div><div>ple endpoints. We have developed a Vec2SPARQL, which is a general frame-</div><div>work for integrating structured data and their vector space representations.</div><div>Vec2SPARQL allows jointly querying vector functions such as computing sim-</div><div>ilarities (cosine, correlations) or classifications with machine learning models</div><div>within a single SPARQL query. We demonstrate applications of our approach</div><div>for biomedical and clinical use cases. Our source code is freely available at</div><div>https://github.com/bio-ontology-research-group/vec2sparql and we make a</div><div>Vec2SPARQL endpoint available at http://sparql.bio2vec.net/</div

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.</p

DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier.

Predicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype-phenotype association being available for humans and model organisms. Combined with recent advances in machine learning, it may now be possible to predict human phenotypes resulting from particular molecular aberrations. We developed DeepPheno, a neural network based hierarchical multi-class multi-label classification method for predicting the phenotypes resulting from loss-of-function in single genes. DeepPheno uses the functional annotations with gene products to predict the phenotypes resulting from a loss-of-function; additionally, we employ a two-step procedure in which we predict these functions first and then predict phenotypes. Prediction of phenotypes is ontology-based and we propose a novel ontology-based classifier suitable for very large hierarchical classification tasks. These methods allow us to predict phenotypes associated with any known protein-coding gene. We evaluate our approach using evaluation metrics established by the CAFA challenge and compare with top performing CAFA2 methods as well as several state of the art phenotype prediction approaches, demonstrating the improvement of DeepPheno over established methods. Furthermore, we show that predictions generated by DeepPheno are applicable to predicting gene-disease associations based on comparing phenotypes, and that a large number of new predictions made by DeepPheno have recently been added as phenotype databases

A Machine Learning Based Approach for Similarity Search on Biodiversity Knowledge Graphs

Mass biodiversity data from scientific collections will be provided by world-wide digitization efforts like iDigBio in the U.S and DiSSCo in Europe. This opens up an increasing amount of data on wild type organisms, which enables the building of large biodiversity knowledge graphs comprising, inter alia, sequence, trait and occurrence data. Knowledge graphs model information in the form of entities and their relationships expressed in good practice as ontology-based annotations. Based on ontological descriptions, semantic similarity analysis makes linking of wild type data to genomic and proteonomic data of model organisms possible and thus supports knowledge discovery of crop wild relatives and underutilized species of interest for medicine, breeding and agriculture. Since classical similarity measurements focus on recording differences between character states (aiming to describe disease phenotypes), but not the character states in the sense of trait variations itself, new methods for similarity search are required. Machine learning algorithms operate on feature vectors, which are numeric representations of data (images, class labels etc) in n-dimensional vector space. We established a machine learning based workflow for similarity search on biodiversity entities using feature learning on ontologies and an associated RDF knowledge graph to project structured trait data into vector space. Vectors are then compared applying a similarity function (e.g. cosine similarity) to determine similarity between taxa based on trait semantics. We will present an application example of machine learning on biodiversity knowledge graphs using a pipeline built upon OPA2Vec, a method to generate feature vectors from the logical content of ontologies (Smaili et al. 2018), to successfully cluster plant species for life form and ecotype (e.g. tree vs. perennial plant) on the basis of their annotations with the Flora Phenotype Ontology (Hoehndorf et al. 2016)

Machine Learning with Biomedical Ontologies

Ontologies are increasingly being used to provide background knowledge in machine learning models. We provide an introduction to different methods that use ontologies in machine learning models. We will start the tutorial by introducing semantic similarity measures that rely on axioms in ontologies to compare domain entities. From semantic similarity, we will develop and discuss unsupervised machine learning methods that can “embed” ontologies in vector spaces to allow comparison of domain entities based on similarity in these spaces. We will introduce mOWL, a software library for machine learning with ontologies, based on which the methods we discuss can be implemented. Throughout the tutorial, we will use biomedical examples for hands-on tasks. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at GitHub