Biology subjects

Loscalzo, J.

Publications and source records attributed to Loscalzo, J..

18 recordsLinked to original sources

bioRxiv2026

2-Hydroxyglutarate Redirects Fatty Acid Partitioning to Mitigate Lipotoxic Stress and Preserve Metabolic Fuel

The role of 2-hydroxyglutarate in lipid metabolism is currently unknown. Here we show that 2HG redistributes the partitioning of fatty acids into triglyceride storage and away from membrane phospholipid synthesis, mitochondrial oxidation, and lipotoxic intermediates. In primary human cardiac and vascular cells, both enantiomers, D2HG and L2HG, expanded triglyceride stores and lipid droplets while selectively depleting phosphatidylethanolamine, with L2HG acting more potently than D2HG despite lower intracellular accumulation. Mechanistically, L2HG increases DGAT-dependent triglyceride synthesis, slows triglyceride turnover, and constrains the ethanolamine branch of the Kennedy pathway. This response limits fatty acid oxidation, long-chain acylcarnitine accumulation, and lipid peroxidation independently of pseudohypoxic transcription or canonical lipid storage regulators, while also remodeling the phosphoproteome and redox proteome. L2HG accumulation induces hypertriglyceridemia in mice, redistributes the acyl chain composition of cardiac triglycerides, and limits ischemia-induced acylcarnitine accumulation in the heart, mirroring a positive association between circulating 2HG and triglycerides in humans. Thus, 2HG expands metabolic flexibility from whether fatty acids are used as fuel to how that fuel is allocated among storage, membrane synthesis, and oxidation.

cell biology↗

bioRxiv2026

Explainable Physicochemical Determinants of Protein Ligand Binding via Non-Covalent Interactions

Protein-ligand binding governs enzymatic catalysis, metabolic homeostasis, and therapeutic modulation. Thus, the accurate prediction of these interactions underpins modern rational drug discovery. However, existing deep-learning frameworks largely operate as black-box predictors that fail to resolve the individual residues mediating binding or decode the fundamental non-covalent forces that drive molecular recognition. To address these limitations, we present ExplainBind, an interaction-aware framework that predicts binding likelihood, localizes specific binding residues at single-amino-acid resolution rather than coarse pocket-level regions, and decodes the underlying non-covalent interaction patterns, all zero-shot, without requiring prior three-dimensional structural inputs. To support residue- and interaction-level training and evaluation, we construct InteractBind, a protein-ligand benchmark with residue-atom interaction maps. Simultaneously, benchmarking experiments demonstrate that ExplainBind consistently outperforms state-of-the-art baselines across diverse protein and ligand spaces, maintaining high precision when generalized to entirely novel sequences and chemical scaffolds. When applied to two unseen therapeutic targets, ExplainBind successfully ranks potent angiotensin-converting enzyme (ACE) inhibitors and clarifies differences in their potency via affinity-stratified interaction landscapes. Furthermore, we demonstrate the prospective utility of ExplainBind by discovering novel inhibitors and activators of L-2-hydroxyglutarate dehydrogenase (L2HGDH) through wet-lab validation, with mechanistically distinct interaction profiles providing a clear molecular rationale for their divergent functional outcomes. Collectively, these results establish ExplainBind as a powerful, generalizable tool for mechanistically informed, interpretable drug discovery.

molecular biology↗

bioRxiv2026

NetMedGPT - A network medicine foundation model for extensive disease mechanism mining and drug repurposing

Network medicine leverages large biomedical knowledge graphs (KGs) to model disease mechanisms and identify therapeutic opportunities. However, most deep learning approaches that use KGs in biomedicine remain task-specific, limiting their ability to generalize across diverse applications within a unified framework. Here, we introduce NetMedGPT, a transformer-based foundation model trained on a large-scale biomedical KG using masked token prediction. By learning contextualized representations of biomedical nodes, NetMedGPT enables unified, zero-shot inference across different drug discovery tasks. Specifically, in five tasks, i.e., predicting the association of drugs with indications, targets, adverse drug reactions, contraindications, and off-label uses, NetMedGPT consistently outperforms all specialized baselines, achieving area under the precision-recall curve gains of between 2.2% and 26%. When evaluated on independent external datasets, NetMedGPT outperformed baseline on an expert-curated clinical indications set and also preferentially prioritized clinically relevant drug-disease pairs in ClinicalTrials.gov. NetMedGPTs generative capability further supports the construction of mechanistically plausible subnetworks offering biological insights. NetMedGPT provides a unified foundation model for network medicine that supports scalable hypothesis generation and provides potential to accelerate drug repurposing. We further provided an interactive interface (https://prototypes.cosy.bio/chatnetmedgpt/) that allows users to obtain model inferences through natural-language queries.

bioinformatics↗

bioRxiv2024

NetMedPy: A Python package for Large-Scale Network Medicine Screening

SummaryNetwork medicine leverages the quantification of information flow within sub-cellular networks to elucidate disease etiology and comorbidity, as well as to predict drug efficacy and identify potential therapeutic targets. However, current Network Medicine toolsets often lack computationally efficient data processing pipelines that support diverse scoring functions, network distance metrics, and null models. These limitations hamper their application in large-scale molecular screening, hypothesis testing, and ensemble modeling. To address these challenges, we introduce NetMedPy, a highly efficient and versatile computational package designed for comprehensive Network Medicine analyses. AvailabilityNetMedPy is an open-source Python package under an MIT license. Source code, documentation, and installation instructions can be downloaded from https://github.com/menicgiulia/NetMedPy and https://pypi.org/project/NetMedPy. The package can run on any standard desktop computer or computing cluster.

bioinformatics↗

bioRxiv2024

DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis

Adverse drug reactions (ADRs) are a major concern in clinical healthcare, significantly affecting patient safety and drug development. The need for a deeper understanding of ADR mechanisms is crucial for improving drug safety profiles in drug design and drug repurposing. This study introduces DREAMER (Drug adverse REAction Mechanism ExplaineR), a novel network-based method for exploring the mechanisms underlying adverse drug reactions and disease phenotypes at a molecular level by leveraging a comprehensive knowledge graph obtained from various datasets. By considering drugs and diseases that cause similar phenotypes, and investigating their commonalities regarding their impact on specific modules of the protein-protein interaction network, DREAMER can robustly identify protein sets associated with the biological mechanisms underlying ADRs and unravel the causal relationships that contribute to the observed clinical outcomes. Applying DREAMER to 649 ADRs, we identified proteins associated with the mechanism of action for 67 ADRs across multiple organ systems, e.g., ventricular arrhythmia, metabolic acidosis, and interstitial pneumonitis. In particular, DREAMER highlights the importance of GABAergic signaling and proteins of the coagulation pathways for personality disorders and intracranial hemorrhage, respectively. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol (targeting KCNH2), ranolazine (targeting SCN5A, currently under clinical trial), and diltiazem (indicated drug targeting CACNA1C and SCN3A) as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes emphasizing the importance of network-based analyses with integrative data for enhancing drug safety and accelerating the discovery of novel therapeutic strategies.

systems biology↗

bioRxiv2024

Branched chain α-ketoacids aerobically activate HIF1α signaling in vascular cells

Hypoxia-inducible factor 1 (HIF1) is a master regulator of numerous biological processes under low oxygen tensions. Yet, the mechanisms and biological consequences of aerobic HIF1 activation by intrinsic factors, particularly in primary cells remain elusive. Here, we show that HIF1 signaling is activated in several human primary vascular cells under ambient oxygen tensions, and in vascular smooth muscle cells (VSMCs) of normal human lung tissue, which contributed to a relative resistance to further enhancement of glycolytic activity in hypoxia. Mechanistically, aerobic HIF activation is mediated by paracrine secretion of three branched chain -ketoacids (BCKAs), which suppress prolyl hydroxylase domain-containing protein 2 (PHD2) activity via direct inhibition and via lactate dehydrogenase A (LDHA)-mediated generation of L-2-hydroxyglutarate (L2HG). Metabolic dysfunction induced by BCKAs was observed in the lungs of rats with pulmonary arterial hypertension (PAH) and in pulmonary artery smooth muscle cells (PASMCs) from idiopathic PAH patients. BCKA supplementation stimulated glycolytic activity and promoted a phenotypic switch to the synthetic phenotype in PASMCs of normal and PAH subjects. In summary, we identify BCKAs as novel signaling metabolites that activate HIF1 signaling in normoxia and that the BCKA-HIF1 pathway modulates VSMC function and may be relevant to pulmonary vascular pathobiology.

cell biology↗

bioRxiv2023

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

BackgroundIneffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. MethodsHere, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. ResultsscDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohns disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohns disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. ConclusionWe propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrios potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

immunology↗

bioRxiv2023

3D structural human interactome reveals proteome-wide perturbations by disease mutations

Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from [~]60,000 germline exomes and [~]36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of [~]11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institutes Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

bioinformatics↗

bioRxiv2022

Integrating pharmacogenomics and cheminformatics with diverse disease phenotypes for cell type-guided drug discovery

Large-scale pharmacogenomic resources, such as the Connectivity Map (CMap), have greatly assisted computational drug discovery. However, despite their widespread use, CMap-based methods have thus far been agnostic to the biological activity of drugs, as well as to the genomic effects of drugs in multiple disease contexts. Here, we present a network-based statistical approach, Pathopticon, that uses CMap to build cell type-specific gene-drug perturbation networks and integrates these networks with cheminformatic data and diverse disease phenotypes for a nested prioritization of cell lines and drugs. Pathopticon demonstrates a better prediction performance than solely cheminformatic measures and state-of-the-art methods that rely exclusively on pharmacogenomic data. Top predictions made by Pathopticon have high chemical structural diversity, suggesting their potential for building compound libraries. In a proof-of-concept application on vein graft disease, we validate the predicted drugs in vitro and demonstrate that Pathopticon helps pinpoint the shared intermediate phenotypes targeted by each prediction. Overall, our analytical framework integrating pharmacogenomics and cheminformatics provides a feasible blueprint for a cell typespecific drug discovery and repositioning platform with broad implications for the efficiency and success of drug development.

systems biology↗

bioRxiv2022

Local Generation and Efficient Evaluation of Numerous Drug Combinations in a Single Sample

We develop a method that allows one to test a large number of drug combinations in a single cell culture sample. We rely on randomness of drug uptake in individual cells as a tool to create and encode drug treatment regimens. A single sample containing thousands of cells is treated with a combination of fluorescently barcoded drugs. We create independent transient drug gradients across the cell culture sample to produce heterogeneous local drug combinations. After incubation period, the ensuing phenotype and corresponding drug barcodes for each cell are recorded. We use these data for statistical prediction of the response to the drugs treatment in a macroscopic population of cells. To further application of this technology, we developed a fluorescent barcoding method that does not require any chemical drug(s) modifications. We also developed segmentation-free image analysis capable of handling large optical fields containing thousands of cells in the sample, even in confluent growth condition. The technology necessary to execute our method is readily available in most biological laboratories, does not require robotic or microfluidic devices, and dramatically reduces resource needs and resulting costs of the traditional high-throughput studies.

pharmacology and toxicology↗

bioRxiv2021

Assessment of community efforts to advance computational prediction of protein-protein interactions

Comprehensive insights from the human protein-protein interaction (PPI) network, known as the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of new PPIs. Many such approaches have been proposed. However, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 24 representative network-based methods to predict PPIs across five different interactomes, including a synthetic interactome generated by the duplication-mutation-complementation model, and the interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. We selected the top-seven methods through a computational validation on the human interactome. We next experimentally validated their top-500 predicted PPIs (in total 3,276 predicted PPIs) using the yeast two-hybrid assay, finding 1,177 new human PPIs (involving 633 proteins). Our results indicate that task-tailored similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods. Through experimental validation, we confirmed that the top-ranking methods show promising performance externally. For example, from the top 500 PPIs predicted by an advanced similarity-base method [MPS(B&T)], 430 were successfully tested by Y2H with 376 testing positive, yielding a precision of 87.4%. These results establish advanced similarity-based methods as powerful tools for the prediction of human PPIs.

systems biology↗

bioRxiv2021

Multidose evaluation of 6,710 drug repurposing library identifies potent SARS-CoV-2 infection inhibitors In Vitro and In Vivo.

Identification of host factors contributing to replication of viruses and resulting disease progression remains a promising approach for development of new therapeutics. Here, we evaluated 6710 clinical and preclinical compounds targeting 2183 host proteins by immunocytofluorescence-based screening to identify SARS-CoV-2 infection inhibitors. Computationally integrating relationships between small molecule structure, dose-response antiviral activity, host target and cell interactome networking produced cellular networks important for infection. This analysis revealed 389 small molecules, >12 scaffold classes and 813 host targets with micromolar to low nanomolar activities. From these classes, representatives were extensively evaluated for mechanism of action in stable and primary human cell models, and additionally against Beta and Delta SARS-CoV-2 variants and MERS-CoV. One promising candidate, obatoclax, significantly reduced SARS-CoV-2 viral lung load in mice. Ultimately, this work establishes a rigorous approach for future pharmacological and computational identification of novel host factor dependencies and treatments for viral diseases.

microbiology↗

bioRxiv2020

Comprehensive characterization of protein-protein interaction network perturbations by human disease mutations

Technological and computational advances in genomics and interactomics have made it possible to identify rapidly how disease mutations perturb interaction networks within human cells. In this study, we investigate at large-scale the effects of network perturbations caused by disease mutations within the human three-dimensional (3D), structurally-resolved macromolecular interactome. We show that disease-associated germline mutations are significantly enriched in sequences encoding protein-protein interfaces compared to mutations identified in healthy subjects from the 1000 Genomes and ExAC projects; these interface mutations correspond to protein-protein interaction (PPI)-perturbing alleles including p.Ser127Arg in PCSK9 at the PCSK9-LDLR interface. In addition, somatic missense mutations are significantly enriched in PPI interfaces compared to non-interfaces in 10,861 human exomes across 33 cancer subtypes/types from The Cancer Genome Atlas. Using a binomial statistical model, we computationally identified 470 PPIs harboring a statistically significant excess number of missense mutations at protein-protein interfaces (termed putative oncoPPIs) in pan-cancer analysis. We demonstrate that the oncoPPIs, including histone H4 complex in individual cancer types, are highly correlated with patient survival and drug resistance/sensitivity in human cancer cell lines and patient-derived xenografts. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay. We further showed that ALOX5 p.Met146Lys at the ALOX5-MAD1L1 interface and RXRA p.Ser427Phe at the RXRA-PPARG interface promote significant tumor cell growth using cell line-based functional assays, providing a functional proof-of-concept. In summary, if broadly applied, this human 3D interactome network analysis offers a powerful tool for prioritizing alleles with mutations altering PPIs that may contribute to the pathobiology of human diseases, and may offer disease-specific targets for genotype-informed therapeutic discovery.

systems biology↗

bioRxiv2020

Hypoxia uncouples HIF gene transcription and metabolic flux in proliferating primary cells

Hypoxia requires metabolic adaptations to sustain energetically demanding cellular activities. While the metabolic consequences of hypoxia have been studied extensively in cancer cell models, comparatively little is known about the metabolic response of primary cells to hypoxia. We performed metabolic flux analyses of proliferating human lung fibroblasts and pulmonary artery smooth muscle cells in hypoxia. Unexpectedly, hypoxia decreased glycolytic flux despite activation of hypoxia-inducible factor (HIF) and increased glycolytic enzyme expression. Pharmacologic activation of HIF with the prolyl hydroxylase (PHD) inhibitor molidustat in normoxia did increase glycolytic flux, but hypoxia abrogated this effect. Multi-omic profiling revealed distinct molecular responses to hypoxia and pharmacologic PHD inhibition and suggested a critical role for MYC in modulating the HIF response in hypoxia. MYC knockdown in hypoxia increased lactate efflux, while MYC overexpression in normoxia blunted the effects of molidustat treatment. Together, these data suggest that other factors, notably MYC, supersede the anticipated effects of HIF-dependent up-regulation of glycolytic gene expression on glycolytic flux in hypoxic proliferating primary cells.

cell biology↗

bioRxiv2020

Predicting the Health Impact of Dietary Polyphenols Using a Network Medicine Framework

Polyphenols, natural products present in plant-based foods, play a protective role against several complex diseases through their antioxidant activity and by diverse molecular mechanisms. Here we developed a network medicine framework to uncover the mechanistic roles of polyphenols on health by considering the molecular interactions between polyphenol protein targets and proteins associated with diseases. We find that the protein targets of polyphenols cluster in specific neighborhoods of the human interactome, whose network proximity to disease proteins is predictive of the molecules known therapeutic effects. The methodology recovers known associations, such as the effect of epigallocatechin 3-O-gallate on type 2 diabetes, and predicts that rosmarinic acid (RA) has a direct impact on platelet function, representing a novel mechanism through which it could affect cardiovascular health. We experimentally confirm that RA inhibits platelet aggregation and alpha granule secretion through inhibition of protein tyrosine phosphorylation, offering direct support for the predicted molecular mechanism. Our framework represents a starting point for mechanistic interpretation of the health effects underlying food-related compounds, allowing us to integrate into a predictive framework knowledge on food metabolism, bioavailability, and drug interaction.

bioinformatics↗

bioRxiv2019

Robustness and lethality in multilayer biological molecular networks

Robustness is a prominent feature of most biological systems. In a cell, the structure of the interactions between genes, proteins, and metabolites has a crucial role in maintaining the cells functionality and viability in presence of external perturbations and noise. Despite advances in characterizing the robustness of biological systems, most of the current efforts have been focused on studying homogeneous molecular networks in isolation, such as protein-protein or gene regulatory networks, neglecting the interactions among different molecular substrates. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network. We integrate heterogeneous sources of data to construct a multilayer interaction network composed of a gene regulatory layer, and protein-protein interaction layer and a metabolic layer. We design a simulated perturbation process to characterize the contribution of each gene to the overall systems robustness, defined as its influence over the global network. We find that highly influential genes are enriched in essential and cancer genes, confirming the central role of these genes in critical cellular processes. Further, we determine that the metabolic layer is more vulnerable to perturbations involving genes associated to metabolic diseases. By comparing the robustness of the network to multiple randomized network models, we find that the real network is comparably or more robust than expected in the random realizations. Finally, we analytically derive the expected robustness of multilayer biological networks starting from the degree distributions within or between layers. These results provide new insights into the non-trivial dynamics occurring in the cell after a genetic perturbation is applied, confirming the importance of including the coupling between different layers of interaction in models of complex biological systems.

systems biology↗

bioRxiv2017

Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes

Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of 4 putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways relationship to T2D.

systems biology↗

bioRxiv2017

A systems approach to refine disease taxonomy by integrating phenotypic and molecular networks

The International Classification of Diseases (ICD) relies on clinical features and lags behind the current understanding of the molecular specificity of disease pathobiology, necessitating approaches that incorporate growing biomedical data for classifying diseases to meet the needs of precision medicine. Our analysis revealed that the heterogeneous molecular diversity of disease chapters and the blurred boundary between disease categories in ICD should be further investigated. Here, we propose a new classification of diseases (NCD) by developing an algorithm that predicts the additional categories of a disease by integrating multiple networks consisting of disease phenotypes and their molecular profiles. With statistical validations from phenotype-genotype associations and interactome networks, we demonstrate that NCD improves disease specificity owing to its overlapping categories and polyhierarchical structure. Furthermore, NCD captures the molecular diversity of diseases and defines clearer boundaries in terms of both phenotypic similarity and molecular associations, establishing a rational strategy to reform disease taxonomy.

bioinformatics↗