Mauricio Soto Gomez

Many real-world problems are naturally modeled as heterogeneous graphs, where nodes and edges represent multiple types of entities and relations. Existing learning models for heterogeneous graph representation usually depend on the computation of specific, user-defined heterogeneous paths, or on the application of large, and often non-scalable, deep neural network architectures. We propose Het, an extension of the ntv algorithm, designed to embed heterogeneous graphs by capturing the topological and structural characteristics of the graph and the semantic information underlying the different types of nodes and edges; this is performed by introducing a simple stochastic node-type switching strategy in second-order random walk processes. Empirical results on synthetic graphs, as well as on benchmark and real-world biomedical graphs, show that Het achieves comparable performance with respect to state-of-the-art methods for heterogeneous graphs in node label prediction tasks.

Motivation: Precision medicine leverages patient-specific multimodal data to improve prevention, diagnosis, prognosis, and treatment of diseases. Advancing precision medicine requires the non-trivial integration of complex, heterogeneous, and potentially high-dimensional data sources, such as multi-omics and clinical data. In the literature, several approaches have been proposed to manage missing data, but are usually limited to the recovery of subsets of features for a subset of patients. A largely overlooked problem is the integration of multiple sources of data when one or more of them are completely missing for a subset of patients, a relatively common condition in clinical practice. Results: We propose miss-Similarity Network Fusion (miss-SNF), a novel general-purpose data integration approach designed to manage completely missing data in the context of patient similarity networks. miss-SNF integrates incomplete unimodal patient similarity networks by leveraging a non-linear message-passing strategy borrowed from the SNF algorithm. miss-SNF is able to recover missing patient similarities and is “task agnostic”, in the sense that can integrate partial data for both unsupervised and supervised prediction tasks. Experimental analyses on nine cancer datasets from The Cancer Genome Atlas (TCGA) demonstrate that miss-SNF achieves state-of-the-art results in recovering similarities and in identifying patients subgroups enriched in clinically relevant variables and having differential survival. Moreover, amputation experiments show that miss-SNF supervised prediction of cancer clinical outcomes and Alzheimer’s disease diagnosis with completely missing data achieves results comparable to those obtained when all the data are available. Availability and implementation: miss-SNF code, implemented in R, is available at https://github.com/AnacletoLAB/missSNF.

Motivation: Precision medicine leverages patient-specific multimodal data to improve prevention, diagnosis, prognosis and treatment of diseases. Advancing precision medicine requires the non-trivial integration of complex, heterogeneous and potentially high-dimensional data sources, such as multi-omics and clinical data. In literature several approaches have been proposed to manage missing data, but usually limited to the recovery of subsets of features for a subset of patients. A largely overlooked problem is the integration of multiple sources of data when one or more of them are completely missing for a subset of patients, a relatively common condition in clinical practice. Results: We propose miss-Similarity Network Fusion (miss-SNF), a novel general-purpose data integration approach designed to manage completely missing data in the context of patient similarity networks. Miss-SNF integrates incomplete unimodal patient similarity networks by leveraging a non-linear message-passing strategy borrowed from the SNF algorithm. Miss-SNF is able to recover missing patient similarities and is “task agnostic”, in the sense that can integrate partial data for both unsupervised and supervised prediction tasks. Experimental analyses on nine cancer datasets from The Cancer Genome Atlas (TCGA) demonstrate that miss-SNF achieves state-of-the-art results in recovering similarities and in identifying patients subgroups enriched in clinically relevant variables and having differential survival. Moreover, amputation experiments show that miss-SNF supervised prediction of the overall survival and progression-free interval events with completely missing data achieves results comparable to those obtained when all the data are available. Availability and Implementation: miss-SNF code, implemented in R, is available at https://github.com/AnacletoLAB/missSNF .

Motivation We recently introduced RNA-KG, an ontology-based knowledge graph that integrates biological data on RNAs from over 60 public databases. RNA-KG captures functional relationships and interactions between RNA molecules and other biomolecules, chemicals, and biomedical concepts such as diseases and phenotypes, all represented within graph-structured bio-ontologies. We present the first comprehensive computational analysis of RNA-KG, evaluating the potential of graph representation learning and machine learning models to predict node types and edges within the graph. Results We performed node classification experiments to predict up to 81 distinct node types, and performed both generic and specific edge prediction tasks. Generic edge prediction focused on identifying the presence of an edge irrespective of its type, while specific edge prediction targeted specific interactions between ncRNAs, e.g. miRNA-miRNA or siRNA-mRNA, or relationships between ncRNA and biomedical concepts, e.g. miRNA-disease or lncRNA-Gene Ontology term relationships. Using embedding methods for homogeneous graphs, such as LINE and node2vec, in combination with machine learning models like decision trees and random forests, we achieved balanced accuracy exceeding 90% for the 20 most common node types and over 80% for most specific edge prediction tasks. These results show that simple embedding methods for homogeneous graphs can successfully predict nodes and edges of the RNA-KG, paving the way to discover novel ncRNA interactions and laying the foundation for further exploration and utilization of this rich information source to enhance prediction accuracy and support further research into the “RNA world”. Code Availability Python code to reproduce the experiments is available at https://github.com/AnacletoLAB/RNA-KG_homogeneous_emb_analysis

Motivation: We recently introduced RNA-knowledge graph (KG), an ontology-based KG that integrates biological data on RNAs from over 60 public databases. RNA-KG captures functional relationships and interactions between RNA molecules and other biomolecules, chemicals, and biomedical concepts such as diseases and phenotypes, all represented within graph-structured bio-ontologies. We present the first comprehensive computational analysis of RNA-KG, evaluating the potential of graph representation learning and machine learning models to predict node types and edges within the graph. Results: We performed node classification experiments to predict up to 81 distinct node types, and performed both generic- and specific-edge prediction tasks. Generic-edge prediction focused on identifying the presence of an edge irrespective of its type, while specific-edge prediction targeted specific interactions between ncRNAs, e.g. between microRNAs (miRNA-miRNA) or between small interfering RNA-messenger and RNA-messenger molecules (siRNA-mRNA), or relationships between ncRNA and biomedical concepts, e.g. miRNA-disease or lncRNA-Gene Ontology term relationships. Using embedding methods for homogeneous graphs, such as Large-scale Information Network Embedding (LINE) and node2vec, in combination with machine learning models like decision trees and random forests, we achieved balanced accuracy exceeding 90% for the 20 most common node types and over 80% for most specific-edge prediction tasks. These results show that simple embedding methods for homogeneous graphs can successfully predict nodes and edges of the RNA-KG, paving the way to discover novel ncRNA interactions and laying the foundation for further exploration, and utilization of this rich information source to enhance prediction accuracy and support further research into the “RNA world.” Availability and implementation: Python code to reproduce the experiments is available at https://github.com/AnacletoLAB/RNA-KG_homogeneous_emb_analysis