Frontiers in Systems Biology

Mechanistic ordinary differential equation models are widely used in systems biology to represent biochemical networks, population dynamics, cell-state transitions, and other biological processes; however, their predictive value depends critically on accurate parameter estimation from noisy and often sparse experimental data. In this tutorial, we present the Weak-form Estimation of Nonlinear Dynamics (WENDy) method as a forward-solver-free approach that reformulates parameter estimation as a covariance-corrected weak-form regression problem by integrating the model equations against compactly supported test functions. We present the background on the methodology through the lens of the familiar logistic equation, and we demonstrate applications of the method on real experimental data through two systems biology examples: a glycolytic oscillator with relatively dense time-course data and a sparse epithelial-mesenchymal cellstate transition model with multiple experimental replicates. Ultimately, using WENDy, we estimate interpretable biological parameters with uncertainty for systems with noisy and sometimes sparse available experimental data.

Heterogeneous biomedical knowledge networks (hetnets) integrate disparate data types, drugs, genes, diseases, and pathways, across independent sources; Hetionet (https://het.io) is a widely used example. A standard approach for assessing connectivity significance is XSwap, which permutes the hetnet P times and fits a gamma-hurdle null model to the degree-weighted path count (DWPC), pooling permuted values across pairs with matching source and target degrees to increase the effective sample size. This permutation approach has been highly successful in practice, but it faces four practical constraints in large graphs: (1) a finite resolution for the smallest reportable p-values, (2) computational cost that grows prohibitive at path lengths L [≥] 4 or 5, (3) a variance model (Var {propto} {micro}2) that departs from the configuration-model form (1 +{kappa} ){micro}, and (4) O(P 10m L) runtime. To complement this approach, we present HetNetEX (Heterogeneous Network EXact inference), which computes the null DWPC distribution analytically from degree sequences using the configuration model in O(Ln) time. In simulations at P = 200 across L = 1-4, HetNetEX achieves Spearman{rho} > 0.96 concordance with XSwap rankings while being >10,000x faster and providing analytical p-values without a resolution ceiling. High-degree pairs show larger XSwap sampling error than low-degree pairs, reflecting the finite-sample nature of permutation that analytical computation avoids.

Approximate Bayesian computation sequential Monte Carlo (ABC-SMC) propagates its particles with a perturbation kernel, and with the standard Normal kernel it degrades sharply as the parameter dimension grows, a failure usually attributed to dimension itself. We show instead that it is governed by the quality of the summary statistics, with dimension entering only through a separate and milder mechanism, and that the two must act together for the Normal kernel to break. The first ingredient is covariance overinflation: the kernel covariance, estimated from the particle cloud, overshoots the true posterior covariance by a factor set by information loss in the summary statistics. We derive this overscaling factor in closed form for a Gaussian model with sufficient statistics and show that it stays modest at any dimension, shrinking toward its baseline value as the tolerance tightens; the extreme values seen in practice (of order 103) are a signature of insufficient summaries, not of dimension. The second ingredient is perturbation overconcentration: the normalised Normal step size concentrates around one as the dimension grows, so every proposal overshoots by the same factor. Either ingredient alone is harmless; only their combination breaks the Normal kernel. A Cauchy kernel (multivariate t with one degree of freedom) removes the concentration, keeping a positive acceptance rate under arbitrary overscaling at a bounded worst-case cost of 1.87x in expected squared jump distance. In a Metropolis-Hastings framework we derive closed-form acceptance rates for both kernels that illustrate the advantage of the Cauchy kernel in this limit. A series of full ABC-SMC computational experiments on five problems at d = 12, including a hierarchical gene-expression model, show the Cauchy reducing the sliced Wasserstein distance to the reference posterior by factors of up to 50 with the same simulation budget. Since the summary statistics are commonly insufficient for the models that require ABC, overinflation is structural and the Cauchy perturbation kernel is the right default for problems in higher dimensions.

We present the BCCWJ-Brain dataset, a multi-modal neuroimaging resource comprising functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and electroencephalography (EEG) data recorded from native Japanese speakers reading newspaper articles from the Balanced Corpus of Contemporary Written Japanese (BCCWJ). Neural data were collected from 112 participants (36 fMRI, 35 MEG, and 41 EEG) as they read twenty newspaper articles presented in a Rapid Serial Visual Presentation (RSVP) paradigm. By providing three complementary neuroimaging modalities collected under identical naturalistic reading stimuli, this dataset provides a cognitive benchmark for computational models such as large language models. The dataset is publicly available on the OpenNeuro platform, offering a valuable resource for neuroscience, natural language processing, and related research fields.

Many biological characteristics arise by interactions between more than one biological organism or unit. Fertilization success in sexually reproducing species represents such an extended phenotype where both mates are required to be fertile for a successful outcome. Consequently, predictive models should account for the joint nature of reproductive performance while offering interpretable estimates for individual mate contributions. Recent advances in genomics and machine learning (ML) provide standardized, high-dimensional genetic information on one hand and computational tools capable of modeling complex biological systems on the other. Here, we construct and evaluate two-tower (TT) machine learning architectures for genomic prediction of binary reproductive outcomes and recovery of sex-specific fertility liabilities. Simulated datasets, generated under a range of genetic architectures, were utilized to compare multilayer perceptron (TT-MLP), convolutional neural network (TT-CNN), and L1-regularized linear (TT-LASSO) two-tower models. Simulation scenarios varied sex-specific heritabilities, genetic correlations, infertility prevalence, mating structure, and sex-specific infertility rates. Models were evaluated with regard to their ability to predict reproductive success at pair level and also recover true underlying genetic values for male and female fertility. Prediction accuracy increased with the underlying heritable component as expected, while sex-specific tower-scores successfully recovered latent fertility liabilities despite models being trained only on observed joint outcomes. TT-LASSO achieved the highest overall classification performance, whereas TT-MLP provided more balanced and consistent recovery of sex-specific genetic values across scenarios. An additional simulation, incorporating genotype-dependent mate compatibility demonstrated advantages of fully-connected neural networks for capturing non-additive interactions. These results indicate that two-tower frameworks provide a powerful approach for modeling reproductive traits, enabling simultaneous prediction of aggregate reproductive outcomes and sex-specific fertility liabilities from genotypic information.

Evaluating the potential applications of a medicine is a fundamental challenge in drug development. There is a lack of standardized, decision-oriented benchmarks that test whether computational models can generalize therapeutic hypotheses across diseases in ways that reflect real-world pharmaceutical investment decision making. To address this gap, we introduce two complementary resources: the Indication Expansion Investment Decision Network (IxIDN) and the Orphanet Rare Disease Ontology Negative-network (ORDON). IxIDN is a clinical-trial-derived positive benchmark constructed by projecting drug-disease associations from pharmaceutical clinical trials into a disease-disease network; each edge connects disease pairs that have entered clinical trials for the same drug, thereby capturing cases when concrete indication-expansion decisions have been made. The current release contains 574 rare diseases and 5,336 edges. In contrast, ORDON serves as a stringent, biology-aware negative benchmark derived from the authoritative Orphanet Rare Disease Ontology. It identifies maximally distant disease pairs according to curated hierarchical structure and genetics-linked inheritance patterns, providing 793 rare diseases and 5,000 edges that represent high-separation negative candidates across therapeutic areas. Together, IxIDN and ORDON enable rigorous cross-evidence generalization from clinical trials to disease ontology, testing for Disease-Disease Association Learning (DDAL), a core task for mechanism-centered drug repurposing and indication expansion. All data are publicly available with detailed metadata, enabling reproducible evaluation of models on transparent, decision-relevant benchmarks.

Endometriosis, a common inflammatory gynecological disorder affecting up to 10% of women worldwide, is characterized by the presence of endometrium-like tissue outside the uterus. Current diagnostic methods, such as ultrasound and MRI, effectively detect ovarian and deep endometriosis but fail to detect more common peritoneal type. Diagnosing peritoneal endometriosis currently necessitates invasive laparoscopy and histological confirmation. Despite numerous efforts, no new reliable biomarkers have successfully transitioned into routine clinical use. This study aimed to investigate the use of targeted metabolomics to discover metabolite ratios capable of identifying endometriosis in plasma samples. We analyzed a discovery population of 235 patients and a validation population of 278 patients. All cases and controls in both populations were diagnosed by laparoscopy. Control subjects included individuals presenting with symptoms such as pain, dysmenorrhea, infertility, or other benign conditions, but who had no laparoscopic evidence of endometriosis. Using generalized linear models (GLMs) and machine learning, the study identified specific metabolite ratios as potential biomarkers that can distinguish different types of endometriosis and enable mass spectrometry-based diagnostics for peritoneal endometriosis. The best-validated GLM, derived from the concentration ratios of amino acids, acylcarnitines, sphingomyelins, and phosphatidylcholines, consisted of Thr/SM(OH) C22:2 + PC aa C40:5/SFA_PC + lysoPC a C16:0/SM(OH) C16:1. This model yielded an AUC of 0.82 (95% CI 0.619-0.891, with 76% sensitivity and 81% specificity) for peritoneal endometriosis. This innovative approach offers a robust diagnostic model, addressing an unmet medical need by facilitating earlier detection of peritoneal endometriosis and improving overall clinical management.

Optimizing dose and schedule remains a central challenge in oncology drug development, particularly for immunotherapies where fixed dosing regimens often fail to account for patient specific heterogeneity in tumor-immune dynamics. Here, we present a hybrid quantitative systems pharmacology-reinforcement learning-Monte Carlo Tree Search (QSP-RL-MCTS) framework for personalized immunotherapy dosing that formulates dose selection as a sequential decision-making problem. The approach integrates a mechanistic QSP model of prostate cancer immunotherapy, transcriptomics informed virtual patient populations and data driven AI system comprising reinforcement learning and Monte Carlo tree search. Reinforcement learning is used to learn adaptive generalized dosing policies that optimize treatment outcomes across the population, while Monte Carlo Tree Search provides forward-looking evaluation of RL predicted dosing trajectories to refine patient-specific decisions. On benchmarking against fixed dosing regimens of ipilimumab, the remission rate of the proposed model (95.2%) was comparable to the highest fixed dosing regimen of 10 mg/kg per dose while the median total dose (72 mg/kg) of the proposed model designed regimen was comparable to the lowest fixed dosing regimen of 3 mg/kg per dose. The model is generalizable across different dosing protocols and can be extended to predict optimal dose under different therapeutic scenarios. Analysis of the learned dosing trajectories enables stratification of patients into distinct response groups and identifies drug activity rate as the dominant determinant of long-term treatment outcome. These results demonstrate how mechanistically guided artificial intelligence can transform population-level dose optimization into patient-specific, biologically interpretable treatment strategies for precision immuno-oncology.

Background/ObjectivesMetabolism-level interpretation of metabolomics datasets requires aggregation analyses across metabolites. One highlyused aggregation analysis is pathway enrichment analysis (PEA), which involves detecting pathways enriched with metabolites that are differential between experimental groups. Annotating metabolites with pathway associations is a prerequisite for PEA. While several knowledgebases define pathways and include metabolite-pathway annotations, these definitions are often partially or even grossly incomplete due to limitations in current metabolic knowledge and its curation, which greatly limits the effectiveness of PEA. MethodsIn this work, we used a novel multitask classification, graph convolutional-like neural network to generate high-quality metabolite-pathway annotations for pathways defined across KEGG, MetaCyc, and Reactome. We then included these predicted metabolite-pathway annotations when performing PEA on 990 datasets deposited in Metabolomics Workbench. ResultsWe demonstrate an 8-fold increase in the median number of enriched pathways detected across these datasets compared to using only knowledgebase-derived annotations. ConclusionsThe significant increase in enriched pathways substantially improves the biological and biomedical interpretability of metabolomics datasets.

Predicting gene essentiality across cellular contexts is a central challenge in computational biology, with implications for identifying cancer vulnerabilities. Graph neural networks (GNNs) integrate molecular interaction networks with gene-level features, but it remains unclear whether their performance gains arise from biologically meaningful connectivity or generic graph structure. Here, we systematically evaluate the role of network information in gene essentiality prediction using 2,741 genes across three tissues. We compare GNNs to feature-only baselines, including multilayer perceptron (MLP) and random forest (RF) methods, under a strict gene-level 5-fold cross-validation scheme to prevent information leakage. To isolate the role of network information, we assess models on the STRING protein-protein interaction network, a degree-preserving shuffled network, and a fully randomized network, with and without network-derived features. GNNs outperform feature-only models, reducing mean squared error and improving Matthews correlation coefficient across all tissues. However, these gains depend critically on biologically structured connectivity: performance degrades substantially under randomized topology and is not preserved by degree-constrained rewiring. Network features are largely redundant when using biologically meaningful graphs, as their information is recovered through message passing, but become important when topology is uninformative. Per-gene analyses reveal uniformly low correlations across models, highlighting intrinsic limits imposed by data variability. Graph Transformer models incorporating global attention do not outperform standard GNNs, indicating that predictive signals are predominantly local. Together, these results show that predictive gains arise from biologically structured connectivity rather than generic graph topology.

Mathematical models of enzyme cycles form the basis of quantifying key features of metabolism and membrane transport. These models are often integrated into more comprehensive models such as whole-cell models to understand emergent behaviours between interacting components. However, it is currently computationally infeasible to simulate the full dynamical behaviour of every enzyme at a network scale. Model reduction is frequently used to improve computational efficiency, but in general, these approaches do not preserve physical and thermodynamic consistency. Here, we outline a general method for simplifying enzyme kinetics models while retaining mass, charge and energy balance. We base our approach on the bond graph, which is a general methodology for modelling biological systems from fundamental physical laws. This approach ensures that key physical constraints are enforced in every model, regardless of their complexity. Our thermodynamic model reduction framework is readily extended to electrogenic transporters through the coupling of chemical and electrical processes. Through the application of our approach to both hypothetical enzyme cycles and real data from the Na+/K+ ATPase, we show that it can rapidly screen for plausible network structures in circumstances where enzyme catalytic mechanisms may not be fully characterised, facilitating biological discovery and drug development.

Metabolites from Plantago lanceolata (plantain) biomass have been linked with biological nitrification inhibition (BNI) in soil. After grazing, leaf metabolite chemistry is altered via digestion, and a suite of secondary metabolites are then delivered onto soil via dung and urine. The purpose of this study was to establish if urine from sheep grazed on plantain had BNI activity when added to pasture soil, and to identify the metabolite profile(s) that most likely contribute to the BNI effects observed. Groups of sheep (n=5) were grazed on one of nine different plantain cultivars in autumn and spring with analysis of leaf material, urine, soil incubation and BNI bioassay data used to identify potential metabolite candidates implicated with BNI. The urinary nitrogen and metabolite composition of sheep fed plantain varied significantly between cultivars and season. After 28 days of incubation, all soil microcosms treated with plantain-derived urine had up to 35% less nitrate than comparative ryegrass urine controls in both seasons, except one in autumn. The key phytochemistry associated with lower soil nitrate concentrations was phenylethanoid and iridoid glycosides resulting in a higher output of glucuronidated, methylated and sulfated secondary metabolites in the urine. Among 19 secondary metabolites identified in the urine, hydroxytyrosol-related metabolites as well as catechol glucuronide, 2-methoxyphenyl sulfate and guaiacol-{beta}-D-glucuronide appear to be the most likely target compounds with respect to the BNI effects observed. Variation in metabolites from different plantain cultivars affected the ratio of metabolite derivatives in urine, which ultimately affected soil nitrification rates. Cultivar phytochemistry is therefore an important consideration with respect to BNI under urine patches. HighlightsO_LISheep grazing different plantain cultivars had different urine compositions C_LIO_LIUrines elicited biological nitrification inhibition (BNI) in soil and in vitro C_LIO_LIDifferent BNI response was related to differential expression of urine metabolites C_LIO_LIKey urine metabolites associated with BNI are derived from glycosidic compounds C_LI

Antifungal drug-resistant Candidozyma auris (C. auris) is a threat to human health worldwide. Combination antifungal drug therapy has emerged as a promising approach to combat drug-resistant C. auris because some drugs interact synergistically to increase fungal clearance when co-administered. Moreover, combination regimens that either rapidly act or completely kill C. auris could mitigate development of on-treatment resistance. However, traditional checkerboard methods to identify synergistic drug combinations only inspect fungal growth at a single timepoint. As a result, they cannot be used to estimate the rate of drug-action or to hypothesise on fungicidal or fungistatic drug-action. Mechanistic modelling would allow us to quantify time-dependent drug-action and infer killing or inhibitory action, but these models are usually fit to direct measurements of fungal growth whose collection is currently not scalable to many time-points and drug combinations. In this paper, we propose a Bayesian mechanistic modelling approach that could detect drug-synergy, estimate drug-action over time and investigate fungicidal or fungistatic drug-activity from optical density (OD600) data alone. OD600 is quicker and easier to collect than direct measurements of fungal growth and therefore more amenable to high-throughput susceptibility testing. By fitting our model to time-course OD600 data of a multi-drug-resistant C. auris isolate growing in mono- and combination drug regimens, we successfully inferred synergy between previously confirmed synergistic antifungal drugs (anidulafungin with manogepix or with 5-flucytosine) and linked our models inferred kinetic parameters to fungicidal and fungistatic action on C. auris growth, which matched drug-activity reported in literature where known. We validated that our model outperformed baseline logistic and Gompertz models using cross validation stratified by OD600 replicates. Our results represent the much-needed groundwork for identifying drug combinations for subsequent experimental testing for use in clinics based on their synergy, temporal drug-action and fungicidal or fungistatic activities inferred from OD600 data alone. Author SummaryThere is an urgent need to locate novel treatments to better treat antifungal drug-resistant Candidozyma auris infections. Combination therapy is a promising approach where two or more antifungal drugs are administered and interact synergistically to enhance fungal clearance. If these combinations are fast acting or eradicate fungi through killing, then they could also reduce the chance of resistance developing during treatment. The synergy of antifungal drug combinations is currently assessed by checkerboard methodologies that compare fungal growth under drug combinations to that under a single drug. However, checkerboard methodologies record only one time-point. Hence, they cannot evaluate drug combinations timeframe of action and follow-up studies are required to determine which combinations could optimally enhance killing. We developed a Bayesian mechanistic model that could detect synergy between drugs, estimate rates of drug-action and investigate killing and inhibition drug-action using only optical density (OD600) data of C. auris. OD600-based measurement of fungal growth is more amenable to large-scale drug testing than data typically used for mechanistic modelling, such as microscopy data. This work serves as a foundation for more targeted drug testing that identifies promising drug combinations based on their inferred drug-synergy and hypothesised killing (or inhibition) rates.

Systematic reviews in computational biology require screening large heterogeneous bibliographic sets, especially when topics span computational methods, cancer genomics and statistical modelling. This paper presents a reproducible semantic triage pipeline that combines SPECTER scientific-document embeddings, research-question similarity, proposal-summary similarity and domain keyword coverage to rank candidate studies for systematic review screening. The pipeline was evaluated on 2,231 Covidence records, including 120 final included studies (prevalence = 5.38%), against keyword-only, TF-IDF, BM25, MiniLM, PubMedBERT and SPECTER-only baselines. SPECTER-hybrid achieved the highest average precision (AP = 0.546), recovered 50% of included studies after screening 4.48% of records, and produced an 11.16-fold enrichment over prevalence. Ablation analysis showed that semantic-keyword combinations consistently outperformed single-signal variants. These findings suggest that citation-informed hybrid ranking can support literature triage while retaining human reviewers as final decision-makers.

Drug repurposing offers a practical strategy to identify new therapeutic uses for approved drugs, potentially reducing the time and cost associated with conventional drug development. We present a novel three-stage drug repurposing pipeline that integrates knowledge graph-based gene prediction, network-based drug-disease association analysis, and systematic classification of candidate drugs by therapeutic class. The pipeline integrates DGLinker to predict novel disease-associated genes, SAveRUNNER to identify drug repurposing candidates, and ATC Category Enrichment Analysis (ATCEA) to prioritise candidates by pharmacological class. We benchmarked the pipeline across twelve diseases using DrugBank and MEDI2-HPS as validation resources. Utilising DGLinker-expanded disease-gene sets as input increased the number of predicted repurposed drugs, while overall discriminative performance remained stable across diseases (AUROC 0.71-0.77). Application of ATCEA consistently improved precision, F1-score, and specificity, while reducing recall, reflecting a conservative prioritisation strategy that contracts the candidate space while retaining pharmacologically coherent drug-disease candidates. We further applied the pipeline to amyotrophic lateral sclerosis (ALS), a neurodegenerative disease with limited therapeutic options, and performed a deeper literature-based validation of the results. Incorporation of DGLinker-predicted genes substantially increased the number of significant candidate drugs and uncovered enriched ATC categories not identified using known ALS genes alone, including antidepressants and antipsychotics. Moreover, several drugs with supporting evidence available in the literature were identified only when DGLinker-predicted genes were used. Overall, 77 candidate drugs were prioritised within significantly enriched ATC categories, several of which are supported by previously published studies. To provide exploratory real-world support for these findings, we further evaluated candidate drugs in a longitudinal electronic health record (EHR) dataset of 2361 patients with ALS from King's College Hospital. Although the number of evaluable drugs was limited due to sample size, the EHR analysis provided additional clinically relevant context for selected prioritised drugs and pharmacological classes. Our pipeline demonstrates potential to accelerate drug repurposing by integrating complementary computational approaches to each step of the process, providing an end-to-end framework that showed robust performance across benchmarking experiments and use cases.

Motivation: Human liver microsome (HLM)-based metabolic stability assays are fundamental in early drug discovery, shaping pharmacokinetic profiles and oral bioavailability. However, these experimental assays are labor-intensive and time-consuming, limiting their application in large-scale virtual screening. Computational models can prioritize compounds at scale, yet most are classification-based, leaving quantitative and interpretable prediction of HLM half-life limited. Results: In this study, we developed a quantitative machine learning model for the direct prediction of HLM half-life (T1/2) by integrating 11,790 compounds combining in-house and curated public data. Among various combinations of molecular features and learning algorithms, the XGBoost model with RDKit 2D descriptors achieved the best predictive performance, with an RMSE of 0.507 and an R2 of 0.431 on an independent test set. Shapley Additive Explanations (SHAP) analysis identified lipophilicity and known metabolic soft-spot features as the primary contributors to the predictions. These results suggest that this quantitative approach provides a practical framework for defining metabolic stability margins, thereby supporting rapid Go/No-go decisions in preclinical drug discovery. Availability: The source code, data, and trained model are available at https://github.com/joshua-416/PredHLM.

Cav1.2 is an L-type voltage-gated Ca2+ channel (VGCC) that supports Ca2+ influx in response to membrane depolarization. Ca2+ entering via Cav1.2 alters gene expression, activates Ca2+-dependent enzymes and has been implicated in synaptic plasticity. ORL-1 is a Gi/o-coupled G protein-coupled receptor (GPCR) that is expressed in the peripheral and central nervous systems. Both Cav1.2 and ORL-1 are expressed in the hippocampus, where they have been implicated in learning and memory. It is well-documented that ORL-1 interacts with another VGCC, Cav2.2. However, less is known about potential interactions between Cav1.2 and ORL-1. Here, we examine the interplay between Cav1.2 (Cav1c, Cav2{delta}-1, Cav{beta}1) and ORL-1 co-expressed in tsA-201 cells by using biochemical, electrophysiological and confocal imaging analysis. Co-immunoprecipitations revealed that ORL-1 independently interacts with Cav1c and Cav2{delta}-1 subunits of the Cav1.2 channel complex. Electrophysiological recordings revealed that co-expression with ORL-1 reduced Cav1.2 peak current density without altering its biophysical properties. Acute perfusion with the ORL-1 receptor agonist nociceptin (1 M) did not alter Cav1.2 current density. Confocal imaging experiments revealed that ORL-1 significantly decreases Cav1.2 plasma membrane expression by disrupting forward trafficking. Interestingly, ORL-1 did not affect Cav1.2 endocytosis. Overall, our results demonstrate a previously unrecognized interaction between ORL-1 and Cav1.2 that alters Cav1.2 membrane expression without affecting biophysical properties.

Genome mining is a powerful technique in natural product discovery, where biosynthetic gene clusters that are likely to produce novel or desirable natural products are identified through bioinformatic analysis. There are many more predicted biosynthetic gene clusters than can easily be experimentally characterized. Additional computational methods to prioritize biosynthetic gene clusters by the bioactivity, structural properties, or novelty of the product would make genome mining more efficient. Multiple machine learning/artificial intelligence models have been developed to predict product properties from biosynthetic gene cluster sequence, but they are limited by small quantities of training data. Model pretraining with unlabeled data is a powerful technique to develop models that can learn on a limited amount of labeled training data. Biosynthetic gene clusters are well suited to this strategy because there are many predicted clusters with only a small percentage being characterized. This paper reports BGC-MLM, a foundation model that is pretrained with a masked language task on predicted biosynthetic gene clusters and then fine-tuned for downstream applications including prediction of product structural class, bioactivity, chemical properties, counts of functional groups, and chemical fingerprint. Comparison to a model trained without pretraining shows that pretraining generally improves performance. BGC-MLM shows better or similar performance to existing specialized methods for these tasks, demonstrating its utility as a foundation model for natural product genome mining.

Objective: Pediatric epilepsy patients undergoing stereo-electroencephalography (sEEG) for ictal onset evaluation provide a rare window to study the developing brain. While methodological frameworks for task-based sEEG research are well-established in adults, pediatric-specific guidance remains underdeveloped. Furthermore, many pediatric epilepsy patients have comorbidities that might typically exclude them from participating in research. We examine factors that influence research participation and discuss considerations for conducting sEEG research in children. Methods: Here, we present a retrospective analysis of task-based research participation patterns from an NIH-funded study of speech and language representations (1R01DC018579) in 66 patients (ages 4-24) undergoing sEEG monitoring at Texas Children's Hospital to determine whether specific comorbidities influenced research participation. Results: Eighty-nine percent (n=66) of patients approached for consent agreed to participate in the study. Despite high rates of comorbidities including neurocognitive disorder (66.67%), language delay (31.75%), global developmental delay (23.81%), mood disorders (33.33%), ADHD (46.03%), autism spectrum disorder (14.29%) or other cognitive/intellectual disabilities (36.51%), all participants engaged in at least one task. While the majority of these diagnoses did not appear to influence subject participation, global developmental delay was associated with a significant reduction in time spent on active tasks. Discussion: Despite high prevalence of neuropsychological comorbidities among participants, our evidence suggests that these participants contribute meaningfully to studies investigating important developmental questions. We suggest strategies for tailoring task-based research to accommodate the unique needs of individuals in this population. Such practices are important for ensuring that research studies reflect the true diversity of the population.

Background and objectives: In recent years, the need to develop analytical strategies for healthy aging has assumed great importance. In this study, we introduce DyViA, a generative artificial intelligence (genAI) platform that can construct personalized trajectories capable of predicting the plausible progression of selected phenotypes with advancing age. Research design and methods: DyViA presents a suite of deep learning models covering two major GenAI approaches: DyViA-Diff, a new diffusion model; and DyViA-mGAN, an improved version of a recent Generative Adversarial Network model. It demonstrated the dynamic progression of femoral neck bone mineral density (BMD) using data from a longitudinal cohort study of women in the U.S. of age 65 years or above. Results: Using very few initial measurements, DyViA generated individual-specific continuous trajectories of BMD, with a corresponding region of acceptable predictions, from 66 to 89 years. The results were subjected to rigorous quality-control and comparative analysis across multiple methods. While DyViA-Diff is the superior model with more coherent and accurate predictions, DyViA-mGAN allows for encoding population- and individual-level effects with a better control. Discussion and implications: Given the prevalence of osteoporosis in the aging population, the main impact of DyViAs genAI-driven contribution in the form of personalized, plausible models of BMD progression with age lies in the systematic yet rigorous transition from otherwise static models of inference about a clearly dynamic phenomenon to a continuous one. The foresight offered by DyViAs outputs empowers an individual by conferring a certain degree of strategic preparedness in the course of aging.