Gliozzo, J. | Search bioRxiv

bioRxiv2025

miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources

MotivationPrecision 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. ResultsWe 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 Implementationmiss-SNF code, implemented in R, is available at https://github.com/AnacletoLAB/missSNF.

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bioRxiv2025

RNA Knowledge-Graph analysis through homogeneous embedding methods

MotivationWe 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. ResultsWe 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 AvailabilityPython code to reproduce the experiments is available at https://github.com/AnacletoLAB/RNA-KG_homogeneous_emb_analysis

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bioRxiv2024

Intrinsic-Dimension analysis for guiding dimensionality reduction and data fusion in multi-omics data processing

The advent of high-throughput sequencing technologies has revolutionized the field of multi-omics patient data analysis. While these techniques offer a wealth of information, they often generate datasets with dimensions far surpassing the number of available cases. This discrepancy in size gives rise to the challenging "small-sample-size" problem, significantly compromising the reliability of any subsequent estimate, whether supervised or unsupervised. This calls for effective dimensionality reduction techniques to transform high-dimensional datasets into lower-dimensional spaces, making the data manageable and facilitating subsequent analyses. Unfortunately, the definition of a proper di-mensionality reduction pipeline is not an easy task; besides the problem of identifying the best dimensionality reduction method, the definition of the dimension of the lower-dimensional space into which each dataset should be transformed is a crucial issue that influences all the subsequent analyses and should therefore be carefully considered. Further, the availability of multi-modal data calls for proper data-fusion techniques to produce an integrated patient-view into which redundant information is removed while salient and complementary information across views is leveraged to improve the performance and reliability of both unsupervised and supervised learning techniques. This paper proposes leveraging the intrinsic dimensionality of each view in a multi-modal dataset to define the dimensionality of the lower-dimensional space where the view is transformed by dimensionality reduction algorithms. Further, it presents a thorough experimental study that compares the traditional application of a unique-step of dimensionality reduction with a two-step approach, involving a prior feature selection followed by feature extraction. Through this comparative evaluation, we scrutinize the performance of widely used dimensionality reduction algorithms. Importantly, we also investigate their impact on unsupervised data-fusion techniques, which are pivotal in biomedical research. Our findings shed light on the most effective strategies for handling high-dimensional multi-omics patient data, offering valuable insights for future studies in this domain. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=159 HEIGHT=200 SRC="FIGDIR/small/576822v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@c39539org.highwire.dtl.DTLVardef@79f681org.highwire.dtl.DTLVardef@1269fcforg.highwire.dtl.DTLVardef@c5e31d_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWe introduce a flexible pipeline to guide in a principled way feature selection and feature extraction methods to reduce the high dimensions and to contrast the curse of dimensionality that affects multi-omics data. C_LIO_LIWe harness the power of cutting-edge Intrinsic Dimensionality (id) estimation through block-analysis, providing an unbiased estimation of the individual ids for each view within a multi-modal dataset. C_LIO_LIWe use an exhaustive set of diverse multi-omics cancer datasets from the well-known TCGA dataset to show that the automatic analysis of the distribution of the block-ids characterizing each omics-view leverages dimensionality reduction, by (1) evidencing feature noise and redundancy, and (2) providing an unbiased estimate of the id for each view, to be used for setting the dimension of the reduced space. This avoids empirical or heuristic choices and allows tailoring the reduction to each data-view. C_LIO_LIThe crucial information gained by block-analysis allowed proposing a two-step dimensionality-reduction approach combining feature selection and feature extraction. Our comparative evaluation shows the effectiveness of the proposed technique and its synergy with state-of-the-art data-fusion techniques applied in a multi-omics context. C_LIO_LIWe show that the proposed reduction pipeline leverages traditional dimensionality reduction and state-of-the-art data-fusion algorithms. Indeed, it obtains effective performance when predicting overall survival events with simple random forest classifiers, often preferred in the biomedical field due to their robustness, efficiency, and interpretable nature. C_LI

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