Point-of-care testing (POCT) has expanded rapidly across healthcare systems, increasing both program scale and operational complexity. However, quantitative benchmarking data describing how POCT programs are structured and supported across institutions remain limited. We conducted a cross-sectional survey to characterize POCT program support, including program scope, staffing, operational practices, governance, and challenges. The survey was distributed electronically through professional networks, and 93 institutional responses were analyzed. Descriptive analyses were performed, and derived metrics, including sites per point-of-care testing coordinator (POCC), were used to evaluate the relationship between program scale and staffing. POCT programs demonstrated substantial variability in scale, with wide ranges in the number of testing sites and operator populations. Adoption spanned multiple test categories, and many institutions reported use of multiple platforms for the same assays. Training, competency assessment, and quality management activities were primarily supported by laboratory staff and POCCs, with documentation frequently partially electronic. Median POCC staffing increased with program size; however, sites per POCC also increased significantly across program scale tiers (Kruskal-Wallis P < 0.001). Governance structures and operational responsibilities varied across institutions, and common challenges included staffing limitations, training and compliance burden, multisite coordination, and IT/connectivity constraints. POCT programs are expanding in scale and complexity, but workforce capacity and governance structures have not scaled proportionally. This mismatch creates a cumulative and multiplicative operational burden. These findings provide benchmarking data to inform scalable staffing models, governance strategies, and infrastructure development for sustainable POCT program management.

The potential energy surface (PES) of the Cl(2P3/2) + C2H5I reaction is described by highly-accurate electronic structure computations, covering both hydrogen- and iodine-abstraction pathways and several substitution routes proceeding through either Walden inversion or front-side attack, including both atom- (H, I) and group-exchange (CH2I, CH3) mechanisms. Geometries and harmonic vibrational frequencies of all stationary points are determined at the MP2/aug-cc-pVDZ and CCSD(T)-F12b/aug-cc-pVDZ levels of theory, and single-point energies are further refined at the most accurate geometries using the coupled-cluster method with aug-cc-pVTZ and aug-cc-pVQZ basis sets. To target chemical accuracy, five additional energy corrections - accounting for core correlation, scalar relativistic, spin-orbit, and post-CCSD(T) effects - are incorporated into the CCSD(T)-F12b/aug-cc-pVQZ single-point energies. The resulting benchmark data allow for the detailed mapping of the reaction pathways, including the identification of transition states and pre- and post-reaction minima, which guide the system from the reactants toward the various product channels on the PES. Rate coefficients are determined using transition-state theory, including the Wigner tunneling correction, and compared to literature theoretical values.

Reference MS/MS libraries remain incomplete due to the vast chemical diversity of metabolites, leaving many spectra from untargeted metabolomics experiments unannotated─the "dark matter" of metabolomics. Machine learning can extend metabolite annotation beyond direct library matches, but its success depends critically on how MS/MS spectra are converted into numerical representations that capture chemically meaningful features while reducing sparsity. Although numerous spectral representations exist, they have not been systematically compared. Using over 71,000 unique compounds with merged-energy MS/MS spectra, we benchmarked a broad set of spectral featurization methods, including fixed and adaptive binning, global-quantile variable-width bins, frequent-peaks representations, spectrum hashing, and learned embeddings such as Spec2Vec, MS2DeepScore, DreaMS, and SpecEmbedding. We further evaluated how vector dimensionality affects performance. A total of 105 neural network models were trained under 5-fold cross-validation to predict Mol2Vec molecular embeddings and retrieve correct structures from a 0.6-million-compound database. Retrieval was assessed at 0.1, 3, and 10 ppm mass tolerances, and a null ranking model was generated to determine expected Top-N accuracy under random candidate ordering. Adaptive binning, frequent-peaks, and DreaMS produced the most accurate embedding predictions. On the test data set, Top-1 retrieval reached 46%, 44%, and 38% for 0.1, 3, and 10 ppm, respectively, with Top-5 accuracies up to 77%. In the CASMI2022 data set, Top-1 performance remained similar at 0.1 ppm but dropped markedly at wider tolerances, reaching only 26% at 3 ppm and 23% at 10 ppm. To ensure reproducibility and broad community applicability, results were further validated on two fully open benchmark data sets, MassSpecGym and Spectraverse, with findings consistent across all three resources. These results underscore clear performance differences among featurization strategies, the strong dependence of retrieval accuracy on mass precision, and the need for evaluation metrics aligned with structure-level annotation tasks.

Wafer fabrication consists of hundreds of interrelated processing steps, where planning decisions directly affect equipment utilisation and work-in-process (WIP). Among these steps, photolithography is regarded as a bottleneck due to expensive equipment, re-entrant flows, and machine dedication across layers. Despite its importance, most photolithography planning studies do not explicitly model WIP flows, which are critical under machine dedication. This study addresses this gap by formulating a photolithography planning problem that models WIP flows under machine dedication. The problem is formulated as a mixed-integer linear programming (MILP) model, and its NP-hardness is established via a proof by restriction from the Partition problem. To solve large-scale instances, we develop an MILP-Guided Large Neighbourhood Search (MG-LNS) framework. The approach constructs an initial solution using a rolling-horizon scheme with heuristic rules and decomposed MILP models, and then improves it via structured destroy strategies and MILP-based repair operators. Computational experiments on nine generated benchmark data types show that MG-LNS achieves machine utilisation and target achievement rates exceeding 98%, while maintaining average overproduction and underproduction rates of 0.63% and 2.42%, respectively. Ablation and sensitivity analyses confirm the effectiveness of the proposed framework and demonstrate the impact of objective-weight selection on the contribution of individual objective terms.

Accurate prediction of RNA-protein interactions (RPIs) is crucial for understanding post-transcriptional regulation. Most existing models depend on implicit embeddings within large language models, which limits their physical interpretability. This study introduces ZHMolTopoRPI, a computational framework that integrates persistent commutative algebra with dual-tower neural networks. We employ the persistent Stanley-Reisner theory (PSRT) to extract multiscale mathematical features from RNA sequences. For feature fusion and RPI prediction, we use a contrastive learning-enhanced gated attention dual-tower network (CL-GADTN) to combine RNA features with protein semantic information from ESM2. Evaluation on six benchmark data sets (NPInter2, RPI7317, RPI488, RPI1807, RPI2241, and NPInter v2.0) demonstrated MCC scores of 92.29, 84.83, 81.55, 88.42, 89.23, and 92.63%, respectively. Additionally, the model's analysis of commutative algebra perturbations revealed motif changes caused by pathogenic single nucleotide polymorphisms (SNPs). We further validated ZHMolTopoRPI's practical utility in functional target screening of the human proteome. Overall, it offers a quantitative and interpretable approach for precise RPI prediction.

Accurate identification of DNA-binding proteins (DBPs) and their DNA-binding residue sites (DBSs) is essential for understanding gene regulatory processes. Despite recent progress achieved by protein language models, current methods still face fundamental limitations, including the quadratic computational burden of Transformer architectures, inadequate modeling of long-range dependencies, and reduced generalization on long or low-homology protein sequences. To address these challenges, we propose ESM2-BiMamba, a length-adaptive hybrid architecture for efficient and scalable protein-DNA interaction prediction. The model preserves the first 29 Transformer layers of the pretrained 33-layer ESM2 and replaces its top four layers with bidirectional Mamba state-space modules, enabling linear-time context propagation while maintaining rich sequence semantics. A sequence-length-adaptive dynamic chunking mechanism further reduces redundant computation and stabilizes long-range dependency modeling. To mitigate the distribution shift between pretraining and downstream tasks, a lightweight adapter is incorporated to enhance representation alignment. In addition, a dual-task prediction head comprising a protein-level DBP classifier and a residue-level DBS predictor allows the model to jointly capture global functional patterns and fine-grained binding-site signals. Extensive experiments on multiple standard benchmark data sets demonstrate that ESM2-BiMamba achieves superior performance in both DNA-binding protein identification and DNA-binding residue prediction, with notable advantages in processing long sequences and generalizing to low-homology targets.

Reconstructing gene regulatory networks (GRNs) with directionality and regulatory types is an important challenge in computational biology. Existing methods often struggle to effectively capture complex topological structures in highly skewed GRNs due to imbalances between local and global information and to the collapse of representation dimensionality. To address these challenges, we propose BMGRN, a unified framework that reconstructs directional and GRNs with regulation types by integrating bidirectional state space modeling with dual contrastive representation learning. Drawing inspiration from sequence modeling, BMGRN employs an enhanced bidirectional Mamba2 architecture to capture long-range dependencies and asymmetric regulatory interactions between genes efficiently. This design enables global information propagation while maintaining directional specificity. Furthermore, a dual contrastive learning mechanism is introduced to alleviate oversmoothing and dimensional collapse, enforcing representation uniformity and discriminability in low-connectivity scenarios. By coupling these representations with a KAN-based convolutional predictor, BMGRN adaptively learns nonlinear dependencies and regulatory modes, thereby improving its modeling capacity for the GRN inference. Experiments on multiple benchmark data sets show that BMGRN attains superior performance, demonstrating great potential for large-scale GRN inference. The code is available at https://github.com/KanZh/BMGRN.

Predicting drug synergy is important for accelerating the discovery of effective anticancer combination therapies. Synergy intrinsically depends on the precise interactions of key chemical substructures within specific cellular environments. However, current molecular graph-based computational methods typically rely on implicit atom-level feature aggregation, which may obscure the topological representation of critical chemical substructures and limit structural traceability. Therefore, we present Graphlet Fingerprint-based Synergy prediction (GraFSyn), a deep learning framework for anticancer drug synergy prediction that uses graphlet fingerprints to encode drugs as explicit connected substructure units, preserving predefined chemical substructures and their topological identity. We further introduce a Dynamic Multi-Scale Convolution (DMSC) module to learn informative representations from high-dimensional and sparse graphlet features. The framework also includes an interaction module to capture context-dependent interactions between drug substructures and cell line gene expression. On the Merck and AstraZeneca benchmark data sets, GraFSyn achieved ROC-AUC/PR-AUC values of 0.972/0.912 and 0.823/0.906, respectively, outperforming representative baseline methods. In addition, attributed signals can be mapped back to specific pharmacophoric regions, supporting substructure-level analysis of synergistic interactions. In general, GraFSyn provides an accurate and structurally traceable approach for anticancer drug combination screening.

Drug-target interaction (DTI) prediction is critical for candidate compound screening and elucidation of mechanisms of action in drug discovery and repurposing. However, existing methods often rely on unimodal representations, global fusion, or shallow cross-modal fusion, making it difficult to adequately model the heterogeneous and fine-grained dependencies between drug structures and protein sequences. To address this issue, we propose CM-MTL-DTI, a DTI-oriented collaborative alignment framework, rather than a simple combination of auxiliary modules. The framework employs two independent one-dimensional convolutional neural networks as main-task encoders to extract backbone sequential representations from drug SMILES sequences and protein sequences, respectively, and introduces a GIN-based graph encoder to provide a complementary structural perspective for the drug modality. On this basis, we design an asymmetric bidirectional cross-modal attention mechanism to explicitly model direction-sensitive dependencies between drug substructures and protein residues. Meanwhile, three collaborative objectives─cross-modal masked reconstruction (XMR), graph-sequence consistency learning (GSC), and supervised contrastive learning (SupCon)─are introduced to achieve local semantic recovery, multiview semantic alignment within the drug modality, and discriminative enhancement of interaction representations, respectively. Experimental results on three benchmark data sets show that CM-MTL-DTI delivers stable and competitive performance under both standard and challenging settings, validating the effectiveness of the proposed DTI-oriented collaborative design.

Predictions of pKa values provide insights into key aspects of molecular behavior, including solubility, lipophilicity, and binding affinity. Despite their importance, experimental microscopic pKa data remain scarce, creating a bottleneck in the training of accurate prediction models. In addition, inconsistent terminology across commonly used data sets hinders effective model development and benchmarking. While recent advances have been driven largely by graph-based neural networks, the potential of sequence-based deep learning for pKa prediction remains underexplored. T5Chem, a sequence-based multitask chemical reaction model, offers an attractive way to cast molecular protonation/deprotonation as a language modeling task and to couple microstate generation with subsequent pKa estimation. To pursue this direction, we introduce pKaCHU (pKa data that are combined, honed, and updated), a curated data set comprising 9000 experimentally derived microscopic pKa entries with ionization-state annotations. We also present T5pKa, a text-based transformer model for small-molecule pKa prediction built on T5Chem. T5pKa leverages multitask learning to enumerate microstates, enabling both protonation and deprotonation to be predicted by a single sequence-to-sequence model, and then predicts microscopic pKa values from the resulting microstate pairs using a separate regression model. Across benchmark data sets, T5pKa achieves performance comparable to established pKa prediction tools and published models while offering the advantage of a unified multitasking framework for microstate enumeration and microscopic pKa prediction.

Accurately identifying somatic variants from genomic sequencing is crucial for understanding and treating cancer. Previously, methods based on statistics and heuristics, as well as methods based on machine learning were proposed for somatic single nucleotide variant (SNV) calling from matched tumor-normal data, but they suffer from low sensitivity especially in certain genomic regions. Here we present VariantMedium, a somatic variant caller that combines a tree-based classifier with a 3D densely connected convolutional network (DenseNet) architecture. We trained and evaluated our model on experimentally confirmed variant data and improved it with an active learning strategy by experimentally confirming the predicted variants via targeted deep sequencing experiments. Overall, we used 336,839 variants from 2,956 samples with whole exome or genome sequencing for training and validation, and 118,887 variants from two independent studies with deep sequencing data for evaluation and benchmarking. VariantMedium shows highest sensitivity amongst benchmarked callers and achieves similar or better F1 scores in SNV calling. Its performance is particularly pronounced on genomic regions characterized by high sequencing error rates, achieving higher F1 scores than Mutect2 and Strelka2. Our results demonstrate the strength of combining machine learning with high-quality experimental confirmation, enabling accurate somatic mutation detection even in low-mappability regions. We provide VariantMedium ( https://github.com/TRON-Bioinformatics/VariantMedium ) as an end-to-end pipeline to advance somatic mutation calling for precision medicine.

New drugs, devices, and other tools are essential to improving children's orthopaedic care. An often underappreciated aspect of new products is the amount of time and money needed to bring them to the bedside. While grants and other seed money have a role very early in the development of technology by start-ups, the bulk of the expense of such development is borne by private investors. We examined a quarter century of early-stage investments in pediatric orthopaedic start-ups and compared them with similar investments in adult-focused orthopaedic companies. Investor backing of pediatric enterprises was much less common, representing only 10% of investments in the field of orthopaedics. Yet, when pediatric companies were supported, the rate at which they acquired subsequent capital and the total amount of capital they raised were comparable with those of adult-focused companies. Investments in new pediatric orthopaedic innovations from 2000 to 2024 were far less common than adult orthopaedic investments. Our data underscore the unmet challenges of backing start-ups in the field and provide benchmark data against which founders and investors can judge their financial performance. The availability of new orthopaedic tools is tied to capital investment in the field's youngest companies. A more complete understanding of long-term trends in the private financing of pediatric orthopaedic start-ups is essential for founders, investors, and policymakers.

RNA-protein interactions play key roles in many life processes, and their study is significant for understanding gene regulation, revealing disease pathogenesis, and developing novel RNA-targeted drugs. However, traditional RPI prediction methods are time-consuming and difficult to satisfy the needs of high-throughput studies. Additionally, existing methods rely solely on a manual feature extraction approach and fail to fully leverage the advantages of the pretrained large language models. In this paper, we propose RPI-PLMGNN, an innovative RPI prediction method that integrates multimodal feature fusion with a Graph Neural Networks framework. First, we adopt linear graph topology to characterize the RNA-protein interaction network. Second, RNAErnie and ESM2 are employed to extract sequence features for RNA and proteins, respectively. Structural features of RNA and proteins are then extracted from RNAFold and SOPMA, respectively, and concatenated with their corresponding sequence features to construct node representations for each modality. Finally, the graph topology features and node features are jointly processed by a hybrid Graph Neural Network architecture that integrates both Graph Attention Network and Gated Graph Convolutional Network modules to generate the final interaction predictions. Experimental results show that RPI-PLMGNN exhibits superior prediction performance on multiple benchmark data sets. Particularly noteworthy is that in cross-species validation, RPI-PLMGNN achieves 94.2, 92.8, 94.5, 97.5, 98.2, and 97.1% accuracy on six species test sets (RPI_C, RPI_D, RPI_E, RPI_H, RPI_M, and RPI_S), demonstrating excellent generalization ability. Extensive experiments show that RPI-PLMGNN is an efficient and accurate method for RPI prediction, offering a valuable tool for studying RNA-protein interaction mechanisms and related drug development.

Among dermatological diseases, skin cancer is among the most life-threatening. Early and accurate diagnosis is important for improving a patient's prognosis. Nevertheless, traditional AI-based diagnostic methods face several challenges, including privacy concerns, limited interpretability, and a severe class imbalance in multi-class skin lesion datasets. To overcome these challenges, the proposed paper proposes a privacy-aware, explainable multimodal skin lesion classification model that combines complex deep learning models and an ensemble modeling approach with explainable artificial intelligence methods. Experimental evaluation is conducted using publicly available HAM10000 benchmark data on multi-class skin lesion classification that can be accessed by means of Kaggle Hub, distributed over seven clinically significant lesion classes (akiec, bcc, bkl, df, mel, nv, vasc). To balance the data, a class-balancing technique is used to boost the minority classes. The EfficientNet B4, DenseNet201, and MobileNetv2 are used to extract deep feature representations, afterward combined with salient clinical metadata to create a robust multimodal feature space. These multimodal features are used to train XGBoost, LightGBM, Deep Neural Classifier (DNC) that resulted classification accuracies of 92%, 90% with 94% respectively. A stacked ensemble strategy is applied to combine the outputs of XGBoost, LightGBM, and Deep Neural Classifier (DNC), which leads to an improvement in accuracy of 96%. Model interpretability techniques provide feature-level explanations that increase transparency. The experimental findings proved the practicality of the suggested framework in terms of efficiency with clinically relevant real-life classification of skin lesions.

Accurately predicting the functional attributes of enzyme mutants is crucial for accelerating enzyme engineering and optimizing biocatalytic systems. Most existing methods focus on enzyme information or a limited set of properties while overlooking key interactions between enzyme mutants and their substrates. To address this limitation, we propose EZPro-Multi, a unified deep learning framework for predicting multiple biochemical properties, including catalytic efficiency (kcat), stability (ΔΔG), and solubility (ΔSol). EZPro-Multi integrates ProtT5-based protein representations with Molformer-based substrate representations through a cross-attention module to capture mutant-substrate interactions. The framework further incorporates supervised contrastive learning to improve feature discriminability by contrasting mutant-substrate pairs with similar or distinct catalytic changes measured on the same substrate. In addition, an auxiliary classification head is introduced to provide extra supervision and enhance the performance of the primary regression task. We evaluate EZPro-Multi using a curated kcat data set comprising diverse enzyme-substrate pairs, achieving state-of-the-art results. Comparative experiments show that EZPro-Multi outperforms existing methods in both regression accuracy and classification consistency. The framework also demonstrates promising performance in predicting ΔΔG and ΔSol across multiple benchmark data sets. Notably, on the deep mutational scanning (DMS) data set, integrating kcat, ΔΔG, and ΔSol significantly improves the hit rate for the top 10% high-activity mutants compared with single-property prediction, further highlighting the value of multi-property integration. Overall, EZPro-Multi provides a unified computational framework for multi-property assessment of enzyme variants and offers practical value for candidate prioritization in enzyme engineering.

We present vL27, a benchmark data set of 27 large noncovalent complexes with sizes up to 205 atoms, designed to probe nanoscale interaction effects. Reference binding energies were computed using local coupled cluster with single, double, and perturbative triple [CCSD(T)] extrapolated to the complete basis set (CBS) limit with VeryTightPNO thresholds and complete pair natural orbital space (CPS) extrapolation to minimize errors arising from local approximations. The MP2/CBS scheme was validated against MP2-F12, and the local CCSD(T)/CPS protocol was benchmarked against canonical CCSD(T), confirming the robustness of both CBS and CPS extrapolation strategies for nanoscale systems. Symmetry-adapted perturbation theory (SAPT) analysis reveals that most complexes are dispersion-dominated or exhibit mixed interaction character, even for hydrogen-bonded systems lacking π-π stacking, underscoring the central role of dispersion and many-body effects in stabilizing large assemblies. Using these benchmark data, we evaluate a broad range of electronic structure methods, semiempirical approaches, and machine learning potentials. MP2+D3-ML, B97M-D4, ωB97M-D4, and HF-3c offer the best balance of accuracy and transferability, consistently reproducing both absolute interaction energies and relative binding trends. The vL27 data set thus provides a rigorous and chemically realistic foundation for evaluating and guiding the development of computationally efficient methods for nanoscale noncovalent systems.

To describe mortality, surgical treatment, and hospitalization patterns in children with congenital diaphragmatic hernia (CDH) using population-based data, linked to congenital anomaly registries across Europe. This cohort study used nine EUROCAT registries in five countries (Denmark, Finland, Italy, Spain, and the United Kingdom) linked to routinely collected hospital and mortality data. Children born alive with CDH between 2005 and 2014 were included and followed until age 5 in hospital data or age 10 in death data, with the final follow-up the end of 2015. Analyses were conducted for all CDH cases and separately for isolated CDH (CDH without additional major congenital anomalies). Standardized data processing and meta-analysis methods were used to generate pooled estimates of mortality, surgical interventions, and hospital stays. Among 567 children with CDH, most were isolated CDH. First year survival was 74.5% for isolated CDH, ranging from 63% to 83% between registries. Similar survival was noted for all children with CDH. Most deaths occurred within the first week. Mortality rates plateaued after infancy, with no deaths recorded after age 5. The median age at surgery was approximately 2 weeks, although this varied by region. Median hospital length of stay in infancy varied from 14 to 29 days between regions. In children with CDH, mortality is highest in the neonatal period, with long-term survival stabilizing after infancy. No deaths occurred after age 5 years. Regional differences in mortality and surgical timing highlight the value of population-based, harmonized data for benchmarking and international comparisons.

Acute medicine and the urgent care crisis: From pressure point to system solution.

Operating rooms contribute disproportionately to healthcare-related greenhouse gas emissions and waste generation. Total Hip Arthroplasty (THA) and Total Knee Arthroplasty (TKA) are high-volume procedures with increasing global incidence, yet pooled data on their environmental impact are lacking. A systematic review and pooled analysis were conducted in accordance with PRISMA guidelines (PROSPERO: CRD420261297449). PubMed, Embase, and Scopus were searched through October 31, 2025, for studies reporting total waste, recyclable waste, and carbon dioxide equivalent (CO₂e) emissions associated with primary THA and TKA. Seventeen studies, including 394 procedures, were included. Data extraction covered waste quantity, recyclable proportion, and carbon footprint. Random-effects models with inverse variance weighting were used to calculate pooled mean estimates. Standard deviations were estimated from reported ranges when not provided. Heterogeneity was assessed using I2 statistics. Pooled mean total waste per arthroplasty was 12.27kg (95% CI, 10.88-13.66). Recyclable waste averaged 1.97kg per procedure (95% CI, 1.64-2.31), representing 14.5% of total waste (95% CI, 11.99-17.02), and indicating substantial unrealized recycling potential. Carbon footprint estimates varied substantially by accounting methodology. Studies measuring waste-disposal emissions alone reported a pooled mean of 13.7kg CO₂e per case (95% CI, 11.32-16.08), whereas comprehensive life-cycle assessment (LCA) studies reported a pooled mean of 135.37kg CO₂e per case (95% CI, 74.91-195.83). Considerable inter-study heterogeneity reflected differences in waste segregation, recycling infrastructure, and carbon accounting methodologies. Primary THA and TKA generate substantial waste and carbon emissions, with low recycling rates across institutions. These findings provide benchmark data to inform sustainability initiatives, optimize resource use, and guide standardized environmental assessment frameworks in arthroplasty.

Accurate protein function prediction is fundamental to advancing drug discovery and precision medicine and understanding complex biological systems. Although Gene Ontology (GO) provides a standardized framework for protein annotation, a critical challenge persists: the imbalance between low-specificity GO terms and high-specificity GO terms. This imbalance creates blind spots in our understanding of protein function landscapes, particularly in clinically relevant pathways. Here, we present ProGO-PSL, a novel large graph architecture designed to resolve this imbalance. ProGO-PSL simultaneously leverages explicit domain identifiers from InterPro and implicit evolutionary contexts from multiple sequence alignments, fusing these complementary data sources within a powerful imbalance learning framework. Our model consistently outperforms state-of-the-art methods by 5%-15% across all specificity levels and on both a benchmark data set and an independent test set, demonstrating robust generalization. Furthermore, ProGO-PSL generates interpretable representations that clarify relationships between low- and high-specificity GO terms, enabling a more complete functional characterization of the proteome. This work accelerates the identification of therapeutic targets in previously uncharacterized biological pathways.