Biology subjects

Hoehndorf, R.

Publications and source records attributed to Hoehndorf, R..

At least 19 recordsLinked to original sources

bioRxiv2026

PhysioMap: an ontology-grounded causal knowledge graph of human physiology

Computational physiology needs representations that connect traits across biological scales while distinguishing causal, constitutive, and mathematical dependencies. We present PhysioMap, an ontology-grounded knowledge base of contextualized physiological traits and precisely defined relation types. A versioned projection maps entailed ontology patterns to a typed causal knowledge graph that constrains quantitative structural causal models. Derivative signs provide a separate qualitative abstraction, which the PhysioMap solver uses to analyze steady-state responses in the presence of feedback. A stratified expert review across all relation types supported most sampled relations and isolated a minority for correction or further investigation. In a rare metabolic disease application, nearly all determinate predictions agreed with the HPO-derived reference before post-hoc review; after the discordant reference directions were excluded, all remaining determinate predictions agreed. Shortest signed paths produced directional errors, particularly on cases for which the PhysioMap solver did not determine a direction, indicating that its abstentions concentrated difficult cases. Abduction usually narrowed the candidate set but often did not identify a unique cause. PhysioMap therefore connects ontology-grounded physiological content to interventional prediction and abduction under incomplete quantitative knowledge. Because PhysioMap curation and the HPO-derived reference may share supporting literature, and because abduction used a closed candidate pool, these analyses do not constitute independent clinical validation.

bioinformatics↗

bioRxiv2026

MicroRNA regulation of stress-survival signalling and protein quality control in human heatstroke

Heatstroke is a life-threatening condition in which heat-shock and unfolded-protein responses are strongly activated but fail to prevent proteostasis disruption and severe cellular injury. Whether post-transcriptional regulation contributes to this mismatch remains unknown. We integrated small RNA sequencing with mRNA profiling in peripheral blood mononuclear cells from patients with classical heatstroke and matched heat-exposed controls recruited during the Hajj pilgrimage. mRNA profiling was performed in 19 cases and 19 controls, and miRNA sequencing in 17 cases and 16 controls from the same cohort. Differentially expressed miRNAs were integrated with 4,462 differentially expressed mRNAs using high-confidence inverse-expression miRNA-mRNA pairs. Twenty-six miRNAs mapped to 376 mRNA targets, forming 414 regulatory pairs and two opposing programmes. Programme A, comprising 16 downregulated miRNAs, was associated with activation of PI3K-mTOR, NRF2 oxidative stress and HIF-1 signalling, consistent with stress-survival signalling. Programme B, comprising 10 upregulated miRNAs, was associated with suppression of stress-granule components and fatty-acid {beta}-oxidation genes, consistent with impaired protein quality control and reduced metabolic flexibility. miR-92a-3p emerged as a central regulatory node, and its target PIK3R3 connected 9 of the 10 enriched pathways. These findings suggest a post-transcriptional regulatory layer that could contribute to the limited protection afforded by activated stress defences in human heatstroke. FundingKing Abdullah International Medical Research Center (RC18/373/R). Key pointsO_LIHeatstroke can occur even when major cellular stress defences are activated, raising the question of why heat-shock and unfolded-protein responses are insufficient to prevent injury under extreme heat. C_LIO_LITo understand this mismatch, we extend our previous human heatstroke transcriptomic study by analysing post-transcriptional regulation by microRNAs (miRNAs). C_LIO_LIThe miRNA-regulated network showed activation of stress-survival signalling pathways, including PI3K-mTOR, NRF2 and HIF-1, while stress-granule and fatty-acid {beta}-oxidation pathways were attenuated. C_LIO_LITranslation arrest was active but stress-granule signalling was suppressed. Normally, translation arrest generates pools of non-translating mRNAs that stress granules sort, store or return to translation when conditions improve; their uncoupling therefore suggests impaired coordination of the cellular stress response. C_LIO_LIThese findings suggest a post-transcriptional trade-off in human heatstroke, in which short-term stress-survival signalling is favoured at the cost of protein quality control, metabolic flexibility and recovery capacity under extreme heat. C_LI

physiology↗

bioRxiv2026

METEOR: joint genome-scale reconstruction and enzyme prediction

MotivationCurrent machine learning methods for enzyme function prediction primarily treat proteins as independent entities, ignoring the metabolic context in which they operate. This reductionist approach often generates biologically implausible annotations that fail to satisfy stoichiometric or thermodynamic constraints. While genome-scale metabolic models (GEMs) enforce systemic coherence, traditional reconstruction workflows decouple functional annotation from network assembly, resulting in information loss during the discretization of enzymatic evidence. ResultsWe developed METEOR, a framework that integrates continuous confidence scores from deep learning models directly into a Mixed-Integer Linear Programming (MILP) formulation. By conditioning predictions on global metabolic requirements, METEOR resolves functional ambiguities where sequence signals are weak. Evaluation on the Price-149 dataset shows that our method yields physiologically viable networks capable of sustaining growth and improve protein-level predictions over baseline methods. Validation on 6,894 bacterial genomes reveals that system-aware refinement increases the recall of experimentally observed phenotypes substantially. Furthermore, we show that constraint-driven selection can still improve annotation performance in highly incomplete genomes. Our results suggest that functional annotation should be treated as a unified inference problem where global system constraints supervise local predictions. AvailabilityMETEOR is freely available at https://github.com/bio-ontology-research-group/Meteor Contactrobert.hoehndorf@kaust.edu.sa Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2025

Beyond protein functions: evaluating completeness, coherence, and consistency of genome-scale function annotations

Protein function annotation has traditionally followed a reductionist approach, assigning functions to individual proteins acting in isolation. This paradigm treats each annotation as an independent fact, disconnected from the broader biological system. However, proteins operate within integrated cellular networks where their functions depend on genomic context and the presence of interacting partners. Here, we develop a genome-scale evaluation framework that assesses whether annotated protein functions could plausibly coexist within a living organism. We formalize three criteria grounded in systems biology principles: completeness (presence of essential functions), coherence (satisfaction of functional dependencies), and consistency (absence of mutually exclusive functions). Applying this framework to bacterial genomes, we evaluated manually curated annotations from six model organisms and computational predictions from six methods. While model organism annotations largely satisfied our constraints -- with violations primarily reflecting host- pathogen interactions -- all computational prediction methods systematically failed to produce biologically plausible genome-scale annotations. Methods achieved high accuracy for individual proteins yet produced incomplete metabolic pathways, incoherent protein complexes, and taxonomically impossible function combinations. These results reveal a fundamental disconnect between the reductionist annotation model and the systems-level requirements of biological organisms. Current computational methods amplify this disconnect as they are optimized for protein-level accuracy while ignoring genome-scale constraints. Our framework provides quantitative metrics for evaluating biological plausibility and establishes a foundation for developing system-aware annotation approaches. The shift from reductionist to systems-level perspectives will be essential for annotating the rapidly growing collection of sequenced genomes and metagenomes. Significance StatementProtein function prediction methods are evaluated by their accuracy on individual proteins, but proteins operate within integrated biological systems with strict functional requirements. We developed a framework that evaluates whether predicted protein functions could plausibly coexist in a living organism by checking for completeness of essential functions, coherence of functional dependencies, and consistency with biological constraints. While manually curated annotations largely satisfy these requirements, all computational prediction methods systematically fail to produce biologically viable genome-scale annotations. This reveals a fundamental disconnect between current evaluation paradigms and the systems-level requirements of biology, highlighting the need for prediction methods that consider genome-scale constraints rather than optimizing for individual protein accuracy.

bioinformatics↗

bioRxiv2025

Molecular Basis and Cellular Effects of Janus-Class-Driven Cytoplasmic PYK2 Coacervates

Kinase activity is increasingly associated with biomolecular phase separation. Focal adhesion kinase (FAK) forms membrane-associated condensates with paxillin to promote adhesion. Here we show that its paralogue, proline-rich tyrosine kinase 2 (PYK2), undergoes phase separation via a distinct mechanism. PYK2 forms cytoplasmic condensates primarily driven by its kinase- FAT linker (KFL) region. Overexpression of PYK2 induces condensates enriched in its autophosphorylated form, which sequester paxillin from focal adhesions and impair cell adhesion. We uncover an autoregulatory mechanism involving the KFL, linking self-association, autophosphorylation, and condensation. Uncommon among known phase separation drivers, KFL condensation is phosphorylation-independent and its sequence belongs to the "Janus" class. Using a transformer-based protein language model, we identified non-homologous sequences with similar features, many from adhesion and cytoskeletal regulators. We validated the phase-separating potential of several of these sequences in cells. These findings reveal a novel mechanism linking phase separation with kinase activation, and demonstrate distinct condensation behavior in close homologues. Our results also highlight how protein concentration modulates condensate function, with implications for disease, and expand the landscape of phase separation drivers.

cell biology↗

bioRxiv2025

An inductive, supervised approach for predicting gene--disease associations using phenotype ontologies

MotivationPredicting gene-disease associations (GDAs) is the problem to determine which gene is associated with a disease. The problem can be framed as a ranking problem where genes are ranked based on a set of phenotypes using a measure of phenotype similarity. When phenotypes are described using phenotype ontologies, ontology-based semantic similarity measures are used. Traditional semantic similarity measures use only the ontology taxonomy. Recent methods based on ontology embeddings compare phenotypes in latent space; these methods can use all ontology axioms as well as a supervised signal, but are inherently transductive, i.e., queries must already be known at the time of learning embeddings, and therefore these methods do not generalize to novel diseases (sets of phenotypes) at inference time. ResultsWe developed an inductive method for ranking genes based on a set of phenotypes. Our method first uses a graph projection to map axioms from phenotype ontologies to a graph structure, and then uses ontology embeddings to create latent representations of phenotypes. We use an explicit aggregation strategy to combine phenotype embeddings into representations of genes or diseases, allowing us to generalize to novel sets of phenotypes. We also develop a method to make the phenotype embeddings and the similarity measure task-specific by including a supervised signal from known gene-disease associations. We apply our method to mouse models of human disease and demonstrate that we can significantly improve over inductive baseline measures, and reach a performance similar to transductive methods for predicting gene-disease associations while being more general. Availability and Implementationhttps://github.com/bio-ontology-research-group/ISGDA Contactrobert.hoehndorf@kaust.edu.sa

bioinformatics↗

bioRxiv2025

NanoDesigner: Resolving the complex-CDR interdependency with iterative refinement

Camelid heavy-chain only antibodies consist of two heavy chains and single variable domains (VHHs), which retain antigen-binding functionality even when isolated. The term "nanobody" is now more generally used for describing small, single-domain antibodies. Several antibody generative models have been developed for the sequence and structure co-design of the complementary-determining regions (CDRs) based on the binding interface with a target antigen. However, these models are not tailored for nanobodies and are often constrained by their reliance on experimentally determined antigen-antibody structures, which are labor-intensive to obtain. Here, we introduce NanoDesigner, a tool for nanobody design and optimization based on generative AI methods. NanoDesigner integrates key stages -- structure prediction, docking, CDR generation, and side-chain packing -- into an iterative framework based on an expectation maximization (EM) algorithm. The algorithm effectively tackles an interdependency challenge where accurate docking presupposes a priori knowledge of the CDR conformation, while effective CDR generation relies on accurate docking outputs to guide its design. NanoDesigner approximately doubles the success rate of de novo nanobody designs through continuous refinement of docking and CDR generation.

bioinformatics↗

bioRxiv2024

Phased genome assemblies and pangenome graphs of human populations of Japan and Saudi Arabia

The selection of a reference sequence in genome analysis is critical, as it serves as the foundation for all downstream analyses. Recently, the pangenome graph has been proposed as a data model that incorporates haplotypes from multiple individuals. Here we present JaSaPaGe, a pangenome graph reference for Saudi Arabian and Japanese populations, both of which have been significantly underrepresented in previous genomic studies. We constructed JaSaPaGe from high-quality phased diploid assemblies which were made utilizing PacBio high-fidelity long reads, Nanopore long reads, and Hi-C short reads of 9 Saudi and 10 Japanese individuals. Quality evaluation of the pangenome graph by variant calling showed that our pangenome outperformed earlier linear reference genomes (GRCh38 and T2T-CHM13) and showed comparable performance to the pangenome graph provided by the Human Pangenome Reference Consortium (HPRC), with more variants found in Japanese and Saudi samples using their population-specific pangenomes. This pangenome reference will serve as a valuable resource for both the research and clinical communities in Japan and Saudi Arabia.

genomics↗

bioRxiv2024

Context-based protein function prediction in bacterial genomes

MotivationThe rapid growth of sequencing data from high-throughput technologies has emphasized the need to uncover the functions of unannotated genes. Recent advancements in deep learning algorithms have enabled researchers to utilize various features to predict protein functions. Traditionally, these algorithms treat proteins as independent functional units or consider interactions only at the protein level. However, prokaryotes often preserve specific genomic neighborhoods over evolutionary time, providing valuable context for predicting protein functions. This context can arise from genes near the gene of interest or synteny regions, where the conserved order of genes on chromosomes results from common ancestry. ResultsWe developed a transformer-based model to pre-train representations of proteins based on their genomic context, and use this model for predicting protein functions. Our results show that context-based protein representations capture context-specific functional semantics and can effectively predict protein functions. We use our model to investigate the influence of phylogenetic distance and homology on the performance of context-dependent function prediction, and find that synteny affects the prediction performance substantially, except for some functions where the function is determined by the genomic context. Our experiments allow us to gain insights into the factors affecting the performance and applicability of context-based function prediction methods across diverse prokaryotic genomes and meta-genomes. Availability and implementationThe generated model, including all training code and generated data, is freely available at https://github.com/bio-ontology-research-group/Genomic_context. Contactrobert.hoehndorf@kaust.edu.sa

bioinformatics↗

bioRxiv2024

Predicting protein functions using positive-unlabeled ranking with ontology-based priors

Automated protein function prediction is a crucial and widely studied problem in bioinformatics. Computationally, protein function is a multilabel classification problem where only positive samples are defined and there is a large number of unlabeled annotations. Most existing methods rely on the assumption that the unlabeled set of protein function annotations are negatives, inducing the false negative issue, where potential positive samples are trained as negatives. We introduce a novel approach named PU-GO, wherein we address function prediction as a positive-unlabeled ranking problem. We apply empirical risk minimization, i.e., we minimize the classification risk of a classifier where class priors are obtained from the Gene Ontology hierarchical structure. We show that our approach is more robust than other state-of-the-art methods on similarity-based and time-based benchmark datasets. Data and code are available at https://github.com/bio-ontology-research-group/PU-GO.

bioinformatics↗

bioRxiv2024

DeepGOMeta: Predicting functions for microbes}

Analyzing microbial samples remains computationally challenging due to their diversity and complexity. The lack of robust de novo protein function prediction methods exacerbates the difficulty in deriving functional insights from these samples. Traditional prediction methods, dependent on homology and sequence similarity, often fail to predict functions for novel proteins and proteins without known homologs. Moreover, most of these methods have been trained on largely eukaryotic data, and have not been evaluated or applied to microbial datasets. This research introduces DeepGOMeta, a deep learning model designed for protein function prediction, as Gene Ontology (GO) terms, trained on a dataset relevant to microbes. The model is validated using novel evaluation strategies and applied to diverse microbial datasets. Data and code are available at https://github.com/bio-ontology-research-group/deepgometa

bioinformatics↗

bioRxiv2023

Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning

MotivationWhole-exome and genome sequencing have become common tools in diagnosing patients with rare diseases. Despite their success, this approach leaves many patients undiagnosed. A common argument is that more disease variants still await discovery, or the novelty of disease phenotypes results from a combination of variants in multiple disease-related genes. Interpreting the phenotypic consequences of genomic variants relies on information about gene functions, gene expression, physiology, and other genomic features. Phenotype-based methods to identify variants involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been successfully applied to prioritizing variants, such methods are based on known gene-disease or gene-phenotype associations as training data and are applicable to genes that have phenotypes associated, thereby limiting their scope. In addition, phenotypes are not assigned uniformly by different clinicians, and phenotype-based methods need to account for this variability. ResultsWe developed an Embedding-based Phenotype Variant Predictor (EmbedPVP), a computational method to prioritize variants involved in genetic diseases by combining genomic information and clinical phenotypes. EmbedPVP leverages a large amount of background knowledge from human and model organisms about molecular mechanisms through which abnormal phenotypes may arise. Specifically, EmbedPVP incorporates phenotypes linked to genes, functions of gene products, and the anatomical site of gene expression, and systematically relates them to their phenotypic effects through neuro-symbolic, knowledge-enhanced machine learning. We demonstrate EmbedPVPs efficacy on a large set of synthetic genomes and genomes matched with clinical information. AvailabilityEmbedPVP and all evaluation experiments are freely available at https://github.com/bio-ontology-research-group/EmbedPVP. Contactrobert.hoehndorf@kaust.edu.sa Supplementary informationSupplementary data are available at Bioinformatics.

bioinformatics↗

bioRxiv2023

DeepGO-SE: Protein function prediction as Approximate Semantic Entailment

The Gene Ontology (GO) is one of the most successful ontologies in the biological domain. GO is a formal theory with over 100,000 axioms that describe the molecular functions, biological processes, and cellular locations of proteins in three sub-ontologies. Many methods have been developed to automatically predict protein functions. However, only few of them use the background knowledge provided in the axioms of GO for knowledge-enhanced machine learning, or adjust and evaluate the model for the differences between the sub-ontologies. We have developed DeepGO-SE, a novel method which predicts GO functions from protein sequences using a pretrained large language model combined with a neuro-symbolic model that exploits GO axioms and performs protein function prediction as a form of approximate semantic entailment. We specifically evaluate DeepGO-SE on proteins that have no significant similarity with training proteins and demonstrate that DeepGO-SE can improve function prediction for those proteins.

bioinformatics↗

bioRxiv2023

LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactions

AO_SCPLOWBSTRACTC_SCPLOWPredicting drug-target interactions is a tremendous challenge for drug development and lead optimization. Recent advances include training algorithms to learn drug-target interactions from data and molecular simulations. Here we utilize Evolutionary Scale Modeling (ESM-2) models to establish a Transformer protein language model for drug-target interaction predictions. Our architecture, LEP-AD, combines pre-trained ESM-2 and Transformer-GCN models predicting binding affinity values. We report new best-in-class state-of-the-art results compared to competing methods such as SimBoost, DeepCPI, Attention-DTA, GraphDTA, and more using multiple datasets, including Davis, KIBA, DTC, Metz, ToxCast, and STITCH. Finally, we find that a pre-trained model with embedding of proteins (the LED-AD) outperforms a model using an explicit alpha-fold 3D representation of proteins (e.g., LEP-AD supervised by Alphafold). The LEP-AD model scales favorably in performance with the size of training data. Code available at https://github.com/adaga06/LEP-AD

bioinformatics↗

bioRxiv2023

BORD: A Biomedical Ontology based method for concept Recognition using Distant supervision: Application to Phenotypes and Diseases

MotivationConcept recognition in biomedical text is an important yet challenging task. The two main approaches to recognize concepts in text are dictionary-based approaches and supervised machine learning approaches. While dictionary-based approaches fail in recognising new concepts and variations of existing concepts, supervised methods require sufficiently large annotated datasets which are expensive to obtain. Methods based on distant supervision have been developed to use machine learning without large annotated corpora. However, for biomedical concept recognition, these approaches do not yet exploit the context in which a concept occurs in literature, and they do not make use of prior knowledge about dependencies between concepts. ResultsWe developed BORD, a Biomedical Ontology-based method for concept Recognition using Distant supervision. BORD utilises context from corpora which are lexically annotated using labels and synonyms from the classes of a biomedical ontology for model training. Furthermore, BORD utilises the ontology hierarchy for normalising the recognised mentions to their concept identifiers. We show how our method improves the performance of state of the art methods for recognising disease and phenotype concepts in biomedical literature. Our method is generic, does not require manually annotated corpora, and is robust to identify mentions of ontology classes in text. Moreover, to the best of our knowledge, this is the first approach utilising the ontology hierarchy for concept recognition. AvailabilityBORD is publicly available from https://github.com/bio-ontology-research-group/BORD Contactrobert.hoehndorf@kaust.edu.sa

bioinformatics↗

bioRxiv2022

A personal, reference quality, fully annotated genome from a Saudi individual

We have used multiple sequencing approaches to sequence the genome of a volunteer from Saudi Arabia. We use the resulting data to generate a de novo assembly of the genome, and use different computational approaches to refine the assembly. As a consequence, we provide a contiguous assembly of the complete genome of an individual from Saudi Arabia for all chromosomes except chromosome Y, and label this assembly KSA001. We transferred genome annotations from reference genomes and predicted genome features using methods from Artificial Intelligence to fully annotate KSA001, and we make all primary sequencing data, the assembly, and the genome annotations freely available in public databases using the FAIR data principles.

bioinformatics↗

bioRxiv2022

FrameRate: learning the coding potential of unassembled metagenomic reads

MotivationMetagenomic assembly is a slow and computationally intensive process and despite needing iterative rounds for improvement and completeness the resulting assembly often fails to incorporate many of the input sequencing reads. This is further complicated when there is reduced read-depth and/or artefacts which result in chimeric assemblies both of which are especially prominent in the assembly of metagenomic datasets. Many of these limitations could potentially be overcome by exploiting the information content stored in the reads directly and thus eliminating the need for assembly in a number of situations. ResultsWe explored the prediction of coding potential of DNA reads by training a machine learning model on existing protein sequences. Named FrameRate, this model can predict the coding frame(s) from unassembled DNA sequencing reads directly, thus greatly reducing the computational resources required for genome assembly and similarity-based inference to pre-computed databases. Using the eggNOG-mapper function annotation tool, the predicted coding frames from FrameRate were functionally verified by comparing to the results from full-length protein sequences reconstructed with an established metagenome assembly and gene prediction pipeline from the same metagenomic sample. FrameRate captured equivalent functional profiles from the coding frames while reducing the required storage and time resources significantly. FrameRate was also able to annotate reads that were not represented in the assembly, capturing this missing information. As an ultra-fast read-level assembly-free coding profiler, FrameRate enables rapid characterisation of almost every sequencing read directly, whether it can be assembled or not, and thus circumvent many of the problems caused by contemporary assembly workflows. Availabilityhttps://github.com/NickJD/FrameRate Contactliuwei.wang@fu-berlin.de and nicholas@dimonaco.co.uk

bioinformatics↗

bioRxiv2022

DeepGOZero: Improving protein function prediction from sequence and zero-shot learning based on ontology axioms

MotivationProtein functions are often described using the Gene Ontology (GO) which is an ontology consisting of over 50,000 classes and a large set of formal axioms. Predicting the functions of proteins is one of the key challenges in computational biology and a variety of machine learning methods have been developed for this purpose. However, these methods usually require significant amount of training data and cannot make predictions for GO classes which have only few or no experimental annotations. ResultsWe developed DeepGOZero, a machine learning model which improves predictions for functions with no or only a small number of annotations. To achieve this goal, we rely on a model-theoretic approach for learning ontology embeddings and combine it with neural networks for protein function prediction. DeepGOZero can exploit formal axioms in the GO to make zero-shot predictions, i.e., predict protein functions even if not a single protein in the training phase was associated with that function. Furthermore, the zero-shot prediction method employed by DeepGOZero is generic and can be applied whenever associations with ontology classes need to be predicted. Availabilityhttp://github.com/bio-ontology-research-group/deepgozero Contactrobert.hoehndorf@kaust.edu.sa

bioinformatics↗