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At least 19 recordsLinked to original sources

PubMed2026

Structured robotic colorectal training in a non-tertiary NHS hospital: a 502-case consecutive cohort implementation study.

Robotic-assisted colorectal surgery has expanded rapidly across NHS practice in the UK. Structured unit-wide training pathways are essential for safe technology adoption, yet published outcome data from non-tertiary hospitals remain limited. This study describes the implementation and feasibility of a unit-wide robotic colorectal program at a high-volume non-tertiary hospital, reporting outcomes across 502 consecutive resections performed by eight consultant surgeons and presenting these in the context of nationally published benchmarks. A retrospective cohort study of 502 consecutive robotic colorectal resections performed at York Teaching Hospital between May 2022 and December 2025. Eight consultant surgeons (A-H) participated in a structured four-phase training pathway incorporating simulation training, proctored cases, complexity-based case progression, and formal credentialing. Primary outcomes were 30-day mortality, unplanned return to theatre (RTT), and anastomotic leak (AL). Anastomotic leak was calculated using only patients who underwent anastomosis as the denominator. Procedure-stratified and individual surgeon outcomes with 95% confidence intervals were reported. Risk-adjusted cumulative sum (RA-CUSUM) analysis was performed to evaluate learning curves. Outcomes are presented descriptively alongside nationally published reference data; no formal statistical comparison against national benchmarks was performed. 502 robotic colorectal resections were performed. Mean patient age was 70.0 ± 11.3 years; 58.4% were male. Median ASA grade was III. The indication was malignancy in 89.2% of cases. Length of stay was non-normally distributed and is therefore reported using median and interquartile range in the revised analysis. Key outcomes: - 30-day mortality: 1.0% (5/502; 95% CI 0.4-2.3%) - Unplanned return to theatre (RTT): 5.2% (26/502; 95% CI 3.6-7.5%) - Anastomotic leak (AL): 3.3% (15/450; 95% CI 2.0-5.5%; denominator = patients with anastomosis) - 30-day unplanned readmission: 5.0% (25/502; 95% CI 3.4-7.2%) - Conversion to open surgery: 3.6% (18/502; 95% CI 2.3-5.6%) - Lymph node yield ≥12: 91.3% of cancer resections - R0 resection rate: 95.1% of cancer resections All primary outcomes fell within or below the published reference ranges used for descriptive context. RA-CUSUM trajectories were heterogeneous: no surgeon crossed the predefined upper control limit, but several curves showed later upward movement. Accordingly, the analysis is interpreted as safety surveillance rather than evidence of uniform performance improvement. RA-CUSUM monitoring showed that no surgeon crossed the predefined upper control limit; however, heterogeneous trajectories precluded a claim of uniform performance improvement.

Humans↗

PubMed2026

Structured Visualization for Laparoscopic Skill Acquisition:A Randomized Controlled Study.

OBJECTIVE: To evaluate whether structured visualization can support acquisition of basic laparoscopic skills during simulation training and whether this approach can achieve outcomes comparable to repeated physical practice. DESIGN: Prospective randomized comparative study. SETTING: Simulation-based laparoscopic skills training at a university-affiliated teaching center. PARTICIPANTS: Fifty laparoscopy-naive medical students were randomly assigned to a laparoscopic practice group or a visualization group. The laparoscopic practice group performed a validated Gynecological Endoscopic Surgical Education and Assessment (GESEA) Laparoscopic Skills Training and Testing (LASTT) hand-eye coordination task 7 consecutive times. The visualization group performed the same task physically on attempts 1, 4, and 7, while attempts 2, 3, 5, and 6 consisted of guided visualization. Each attempt lasted up to 2 minutes, and performance was scored as the number of correctly placed rings (range 0-12). RESULTS: Baseline performance was comparable between groups (2.92 ± 2.14 vs 2.88 ± 1.72; p = 0.94). Both groups improved significantly over time (p < 0.001). No statistically significant between-group differences were found on the 4th attempt (6.52 ± 3.25 vs 5.76 ± 2.57; p = 0.48) or 7th attempt (8.24 ± 2.86 vs 7.44 ± 2.99; p = 0.39). The final physical performance of the visualization group was significantly better than the 3rd physical attempt of the laparoscopic practice group (p = 0.025). CONCLUSIONS: Structured visualization may support early laparoscopic skill acquisition and achieve short-term outcomes comparable to repeated hands-on simulator training. Visualization should be considered an adjunct, rather than a replacement, for physical practice in simulation-based laparoscopic education.

Laparoscopy↗

PubMed2026

Impact of real-time respiratory function monitoring on neonatal mask ventilation training: a multicentre simulation-based crossover study.

OBJECTIVE: To evaluate whether visibility of respiratory function monitor (RFM) feedback improves mask ventilation performance and influences subsequent ventilation performance during neonatal resuscitation training. DESIGN: multicentre randomised crossover simulation study. PARTICIPANTS: Healthcare professionals involved in neonatal resuscitation training across participating centres. INTERVENTION: Participants performed positive pressure ventilation (PPV) on both term and premature manikins under two conditions: with visible RFM feedback and with the display masked. The order of feedback visibility was randomised. Each participant completed ventilation assessments in both conditions. MAIN OUTCOME MEASURES: Primary outcomes were expired tidal volume (Vte) and mask leak (%). The proportion of breaths within the target Vte range (4-8 mL/kg) was calculated as a performance indicator. Secondary outcomes included variability in Vte and mask leak to assess ventilation stability between conditions. RESULTS: Visible RFM feedback was associated with lower mask leak and improved ventilation stability in the preterm manikin and with tidal volumes entering the target range in the term manikin. Participants initially ventilated with visible feedback maintained performance after feedback removal, suggesting a carry-over learning effect. CONCLUSION: In this multicentre crossover simulation study, visible RFM feedback was associated with changes in mask ventilation performance and evidence of a carry-over learning effect following feedback removal. Real-time visibility of respiratory parameters may strengthen neonatal resuscitation training.

Humans↗

PubMed2026

Educational interventions to improve medical students' bad news communication skills: A systematic review and meta-analysis.

OBJECTIVES: This systematic review aimed to both determine whether educational interventions improve medical students' ability and/or confidence in Bad News Communication (BNC), as well as assess the relative efficacy of instructional formats. METHODS: Performed according to the PRISMA guidelines, four databases were searched for articles describing education-based interventions to improve medical student's BNC ability and/or confidence, published in English between 2001 and 2024. Data on students' self-reported or observer-assessed level of competence/ability in BNC (primary outcome), and students' self-assessed confidence in BNC skills (secondary outcomes), were analysed. Meta regression explained the influence of several categorical moderators on heterogeneity in relation to intervention effects on competence/ability. RESULTS: 27 studies met the criteria for inclusion in the systematic review and 17 studies for the meta-analysis. Interventions described in controlled studies were associated with a moderate and significant increase in BNC ability (13 data sets; standardized mean difference [SMD] = 1.09, 95% CI = 0.52 - 1.66). Interventions detailed in pre-post design studies were associated with a significant increase in BNC ability (20 data sets; SMD = 0.92, 95% CI = 0.52 - 1.32), and student confidence/comfort in their BNC skills (12 data sets; SMD = 1.16, 95% CI = 0.57 - 1.75). Subgroup analysis demonstrated better skills/competence outcomes in studies that included simulation-based training (SBT). CONCLUSIONS: Educational interventions improve the BNC ability and confidence of medical students. Interventions should include an SBT element as this leads to greater improvements in BNC ability. Further research is needed to determine to what extent these interventions translate to positive patient outcomes. PRACTICE IMPLICATIONS: Diverse educational programme, especially those including simulation-based training, are effective in improving BNC skills, although the longetivity of these improvements is at present unclear. Therefore, we recommend that refresher courses or practice opportunities should be scheduled throughout students' medical education to ensure retention of BNC skills.

Humans↗

PubMed2026

Immersive virtual reality-assisted anatomy training improves endotracheal intubation performance in simulation: a randomized controlled trial among Chinese non-anesthesiology residents.

INTRODUCTION: This study aimed to compare immersive virtual reality (IVR)-assisted versus conventional anatomy training for teaching endotracheal intubation (ETI) to novice non-anesthesiology residents enrolled in China's Standardized Residency Training program. METHODS: A total of 90 non-anesthesiology residents without prior ETI experience were randomly assigned to either an IVR group receiving IVR-assisted anatomy training (n = 45) or a control group receiving conventional anatomy training (n = 45). All participants underwent a standardized teaching protocol. The primary endpoint was residents' ETI performance on a simulator, assessed using both the Global Rating Scale (GRS) and a task-specific checklist. The secondary endpoints included changes in written multiple-choice question (MCQ) scores and residents' evaluations of the course. RESULTS: In practical ETI assessments on a manikin, the IVR group achieved significantly higher scores on the task-specific checklist than the control group (90.34 ± 2.89 vs. 87.20 ± 3.29; p < 0.001), whereas GRS scores were comparable between groups. Both groups showed significant post-training improvement in knowledge scores (p < 0.001), with the IVR group showing a greater gain in theoretical knowledge (54.0% vs. 36.3%; p < 0.001). Participants in the IVR group also expressed a stronger preference for their training method (80.8%) and reported higher levels of motivation, confidence, and enjoyment (all p < 0.05). CONCLUSION: IVR-assisted anatomy training enhances the effectiveness of ETI training for novice non-anesthesiology residents, offering an interactive, engaging, and reproducible approach within China's Standardized Residency Training framework.

Humans↗

PubMed2026

Standardized visual overlays enhance laparoscopic instruction: A mixed-methods evaluation.

Effective communication during laparoscopic procedures is frequently undermined by spatial disorientation and inconsistent terminology between instructors and trainees. This study examined whether standardized visual overlays on endoscopic monitors could enhance communication and learning. We conducted a three-phase mixed-methods study: qualitative observation of 20 laparoscopic teaching cases; a randomized trial of 63 second-year medical students assigned to control, clock, or alphanumeric grid (AG) overlays during three trials of a standardized transfer task; and intraoperative implementation in 44 cases (30 AG, 14 clock) with post-case surveys and qualitative feedback. In simulation, the clock overlay produced the fastest completion times, whereas the AG yielded the lowest error scores, and both overlays outperformed the control. Intraoperatively, the AG was rated higher than the clock for communication clarity, spatial orientation, perceived operative efficiency, and trainee confidence. Standardized visual overlays, particularly the AG, appear to support intraoperative teaching by providing a shared spatial frame of reference.

Laparoscopy↗

PubMed2026

Nondominant Hand Training in Laparoscopy for Surgical Interns: Feasibility and Impact.

OBJECTIVE: Laparoscopy requires bimanual proficiency, yet early trainees demonstrate underdeveloped nondominant hand (NDH) performance. Although deliberate practice of NDH skill contributes to overall performance, NDH training is rarely incorporated into residency simulation curricula and has not been formally evaluated in surgical trainees. We assessed feasibility and impact of integrating structured NDH training with established laparoscopic curriculum for surgery interns. DESIGN: Prospective, single-institution randomized pilot study. Interns were assigned the standard 4-week curriculum of laparoscopic dominant hand and bimanual tasks (Control) or completed assigned NDH tasks in addition to the standard curriculum (Intervention). Feasibility was determined by assigned task completion, daily standard and NDH-specific self-reported practice time, and improvement in bimanual task performance. Performance was video recorded weekly and assessed by blinded evaluators using MISTELS and GOALS scoring. Cognitive workload during laparoscopic tasks was measured via NASA-TLX. Exploratory analyses were conducted within a Bayesian framework. SETTING: A single academic institution with a surgical simulation training program. PARTICIPANTS: General surgery interns on their 4-week simulation rotation. RESULTS: Eleven general surgery interns (6 intervention, 5 controls; all right-hand dominant) completed the study with 100% task completion and practice log compliance. Both groups improved in bimanual performance and perceived cognitive load. Reduction in cognitive workload during bimanual task performance was greater in the NDH group. Time spent on NDH practice over 4 weeks was associated with improved bimanual performance, independent of time spent on standard curriculum tasks. CONCLUSIONS: Structured NDH training is feasible to implement within an existing curriculum and reduces perceived cognitive workload during bimanual laparoscopic tasks. NDH practice demonstrates a beneficial dose-response relationship with performance, supporting its integration into early laparoscopic training.

Laparoscopy↗

PubMed2026

Effectiveness of artificial intelligence in nursing simulation education: A systematic review, meta-analysis and bibliometric visualization analysis.

OBJECTIVES: To synthesize the roles and core functions of AI in nursing simulation education for nursing students via systematic review, quantitatively evaluate its effects on students' knowledge and skill outcomes through meta-analysis, and map the research landscape and development trends of this field through bibliometric visualization analysis. DESIGN: Systematic review, meta-analysis and bibliometric visualization analysis. DATA SOURCES: Eight electronic databases: PubMed, Web of Science, MEDLINE, ERIC, Academic Search Complete, China National Knowledge Infrastructure (CNKI), Wanfang Database, VIP Chinese Science and Technology Journal Database (VIP) were employed to search studies from the time of construction to 16 December 2025. REVIEW METHODS: Studies meeting the inclusion criteria were screened. The revised Cochrane Risk of Bias tool (ROB 2) and Joanna Briggs Institute (JBI) critical appraisal checklists were used for quality assessment. Meta-analysis was performed with Review Manager 5.4, and bibliometric visualization analysis was conducted using VOSviewer 1.6.20 and Bibliometrix (based on R4.4.3). RESULTS: A total of 61 studies were included. AI primarily played two roles in nursing simulation education: peer-type new subject (n = 24) and direct mediator (n = 22). Meta-analysis showed that AI interventions significantly improved nursing students' knowledge (SMD = 1.49, 95% CI [0.55,2.43], p = 0.002) and skills (SMD = 0.66, 95% CI [0.02,1.31], p = 0.04). Bibliometric analysis identified that the United States of America and China were the two main contributing countries in this field, and the key motor themes included generative artificial intelligence, virtual patients, and geriatric care. CONCLUSIONS: AI exerts positive effects on nursing students' knowledge acquisition and skill enhancement in simulation education, with peer-type new subject and direct mediator as the dominant roles. Future research should focus on expanding AI applications in multi-specialty simulation scenarios, activating the data-driven value of machine learning, and strengthening international collaboration and standardization construction, so as to promote the sustainable development of AI-integrated nursing simulation education.

Humans↗

PubMed2025

Phylogenetic Methods Meet Deep Learning.

Deep learning (DL) has been widely used in various scientific fields, but its integration into phylogenetics has been slower, primarily due to the complex nature of phylogenetic data. The studies that apply DL to sequencing data often limit analyses to four-taxon trees. Many of these studies serve as "proof of principle" and perform similarly to traditional phylogeny reconstruction methods. New ways of using training data, such as encoding with compact bijective ladderized vectors or transformers, enable the handling of much larger trees and genomic data sets. This short perspective focuses on the application of DL in phylogenetics, introducing prevalent DL architectures. We highlight potential problems in the field by discussing the risks of using simulation-based training data and emphasize the importance of reproducibility and robustness in computational estimates. Finally, we explore promising research areas, including the combination of phylogenetics and population genetics in DL, the analysis of neighbor dependencies, and the potential to significantly reduce computational cost compared to traditional methods. This perspective illustrates the potential of DL in complementing traditional phylogeny reconstruction methods and aiding the advancement of phylogenetic analysis, especially in performing computationally demanding tasks such as model selection or estimating branch support values.

Humans↗

PubMed2026

Comparison of summative assessments between simulated electronic health records versus traditional paper-based patient cases: A non-inferiority randomized controlled trial.

INTRODUCTION: Electronic health records are fundamental to contemporary pharmacy practice, yet evidence supporting their use in pharmacy education is lacking. This single-center, non-inferiority randomized controlled trial with blinded outcome assessment evaluated whether delivering patient cases via a simulated academic EHR (aEHR) was non-inferior to a traditional paper-based format in student exam performance. METHODS: 53 third-year PharmD students at the University of British Columbia were randomized 1:1 to complete a mock summative examination using either the aEHR or paper-based case delivery, stratified by self-reported EHR comfort level. The primary outcome was mean written exam score (%). Non-inferiority was pre-specified at a margin of 14%. Adjusted linear regression was used for the primary analysis, with a multiple imputation sensitivity analysis. Student perceptions were explored through post-exam focus groups analyzed using inductive thematic analysis. RESULTS: 42 students (21 per group) completed the exam and were included in the primary analysis. Mean scores were 66% (SD 11) in the aEHR group and 68% (SD 10) in the paper group. The adjusted mean difference (paper minus aEHR) was -2.2% (95% CI -9.2% to +4.8%), satisfying non-inferiority but not superiority. Sensitivity analysis (n = 53) yielded consistent results (-2.3%; 95% CI -7.1% to +4.1%). Focus groups revealed initial student anxiety with the aEHR but recognized its alignment with clinical practice. DISCUSSION: These findings support the feasibility of integrating simulated EHRs into summative pharmacy assessments without compromising performance. CONCLUSION: Simulated EHRs are a non-inferior assessment medium compared with paper-based formats and represent a viable step toward technology-driven pharmacy practice environments.

Humans↗

PubMed2026

Virtual Reality Mastoidectomy as Precadaver Training for Novices: A Randomized Crossover Study.

OBJECTIVES: To compare cognitive load during virtual reality (VR) simulation and cadaveric dissection (CD) mastoidectomy training in novice learners. To determine whether training order influences cognitive load, characterize cognitive load progression during the procedure, and assess whether VR training improves subsequent cadaveric performance. METHODS: In this randomized crossover study, 24 core surgical trainees with no prior mastoidectomy experience performed a cortical mastoidectomy in both VR and CD settings. Participants were randomized to either VR-first or CD-first training sequences. Cognitive load was measured using a bespoke auditory reaction-time device at baseline and 10, 30, and 50 min. Relative reaction time (RRT) served as an objective index of cognitive load. Cadaveric performance was assessed using the Modified Welling Scale by two blinded otologists. RESULTS: Cognitive load was significantly lower during VR than CD, with mean RRT rising 26% from baseline in VR versus 60% in CD (p < 0.001). Training order did not affect cognitive load in either modality, and RRT increased progressively throughout mastoidectomy in both VR and CD. Participants who began with VR achieved significantly higher cadaveric performance scores than those who began with CD (mean 9.50 vs. 4.96; p < 0.001), and inter-rater reliability for performance scoring was high. CONCLUSION: VR mastoidectomy reduces cognitive load and enhances subsequent cadaveric performance in novice trainees, supporting its role as a cognitively optimized precadaver training modality that complements, rather than replaces, cadaveric dissection. These findings suggest VR enhances early learning efficiency and resource utilization in novice otolaryngology training. LEVEL OF EVIDENCE: N/A.

Humans↗

PubMed2026

IQ-NET: fast and accurate quartet phylogenetic inference using deep learning trained on empirical DNA alignments.

Phylogenetic inference is fundamental to modern biology, with many applications including evolutionary biology, epidemiology, and comparative genomics. While maximum likelihood and Bayesian methods remain the gold standard for phylogenetic analysis, they rely on simplifying assumptions and are computationally intensive. Recent machine learning approaches for phylogenetics offer speed advantages, but have several limitations: exclusive reliance on simulated data for training, inadequate handling of gaps, and sensitivity to input sequence order. Here, we introduce IQ-NET (Intelligent Quartet NETwork), a deep learning framework that solves these limitations to infer four-taxon trees. IQ-NET estimates both tree topology and branch lengths directly from gapped alignments. IQ-NET outperforms existing machine learning methods in terms of accuracy, and obtained a 24-fold speedup compared with the widely used maximum likelihood software, IQ-TREE. We finally introduce a pipeline using IQ-NET and the ASTRAL software to reconstruct a larger species tree, i.e., with more than four taxa.

Empirical data training↗

PubMed2025

Chromatin structures from integrated AI and polymer physics model.

The physical organization of the genome in three-dimensional space regulates many biological processes, including gene expression and cell differentiation. Three-dimensional characterization of genome structure is critical to understanding these biological processes. Direct experimental measurements of genome structure are challenging; computational models of chromatin structure are therefore necessary. We develop an approach that combines a particle-based chromatin polymer model, molecular simulation, and machine learning to efficiently and accurately estimate chromatin structure from indirect measures of genome structure. More specifically, we introduce a new approach where the interaction parameters of the polymer model are extracted from experimental Hi-C data using a graph neural network (GNN). We train the GNN on simulated data from the underlying polymer model, avoiding the need for large quantities of experimental data. The resulting approach accurately estimates chromatin structures across all chromosomes and across several experimental cell lines despite being trained almost exclusively on simulated data. The proposed approach can be viewed as a general framework for combining physical modeling with machine learning, and it could be extended to integrate additional biological data modalities. Ultimately, we achieve accurate and high-throughput estimations of chromatin structure from Hi-C data, which will be necessary as experimental methodologies, such as single-cell Hi-C, improve.

Chromatin↗

PubMed2026

Virtual, Augmented, and Mixed Reality Technologies in Neurosurgical Training: Enhancing Skills and Surgical Outcomes: A Systematic Review.

OBJECTIVE: To systematically review the role of virtual reality (VR), augmented reality (AR), and mixed reality (MR) in neurosurgical education and training. DESIGN: Systematic review conducted in accordance with the PRISMA guidelines. SETTING: A comprehensive search was performed across PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar for English-language studies published between 1 January 2020 and 30 April 2026. PARTICIPANTS: Studies involving neurosurgeons, fellows, residents, and medical students (maximum sample size: n = 48) were included. RESULTS: Of 7,204 initially identified studies, 25 met the inclusion criteria. VR was primarily used for surgical simulation (100% of VR studies) and anatomical education (62.5%). AR demonstrated broader applications, including preoperative planning (40%) and intraoperative support (30%). MR was evenly distributed across simulation, planning, and intraoperative support (40% each). The most frequently improved outcomes were training effectiveness (52%) and technical proficiency (44%). Methodological quality scores, assessed using the Modified Medical Education Research Study Quality Instrument (MMERSQI), ranged from 39.5 to 84.5, indicating varied rigor. CONCLUSION: VR, AR, and MR technologies show potential to enhance surgical precision, technical skills, and educational outcomes in neurosurgical training. However, standardization of methodologies and cost-effective solutions remain essential. Future research should focus on long-term clinical impact and integration of AI-driven training models.

Virtual Reality↗

PubMed2026

Clinical Performance in Critical Care Simulation under Sleep Deprivation: Effects of Power Napping in the Recovery Napping Protocol for Anesthesiologist Performance (R-NAP) Randomized Controlled Trial.

BACKGROUND: Sleep deprivation is common among anesthesia residents and impairs both technical and nontechnical skills such as leadership. Napping is recommended in fatigue management across healthcare and other safety-sensitive sectors, yet its effectiveness for healthcare providers remains underexplored. This study evaluated whether a 30-min nap opportunity improved simulated crisis performance after a 24-h shift. METHODS: Residents were tested twice: once rested and once using a 24-h shift to induce partial sleep deprivation. Between sessions, they were trained in fatigue management. In the sleep-deprived condition, they were randomized to a nap opportunity or a control condition. Actigraphy objectively assessed sleep and nap duration. The primary endpoint was overall simulated clinical performance (0 to 200; combined technical and nontechnical scores). Secondary endpoints were technical and nontechnical subscales. Group effects were primarily tested using intention-to-treat regression models adjusted for rested performance, previous sleep, and critical care experience. RESULTS: Thirty-five residents were enrolled (nap opportunity, n = 19; control, n = 16). In the primary analysis sample (n = 27), clinical performance was 14.8 points higher after the nap opportunity compared with controls (95% CI, 2.8 to 26.9; P = 0.018), corresponding to a 7.4% improvement. Technical skills did not differ significantly between groups, although more sleep was associated with better technical performance. Nontechnical skills were higher in the nap opportunity condition (+11.0 points; 95% CI, 2.2 to 19.8; P = 0.016), including significant effects of leadership and resource utilization. Exploratory analyses suggested associations between longer nap duration and multiple performance domains, strongest for technical skills ( P = 0.010). CONCLUSIONS: Napping appears to enhance clinical performance, while the nap opportunity, nap duration, and previous sleep deprivation each influenced technical and nontechnical performance in distinct ways. These findings support integrating napping and recovery into medical education and scheduling.

Adult↗

PubMed2025

insilicoSV: a flexible grammar-based framework for structural variant simulation and placement.

SUMMARY: Structural variants (SVs) are key drivers of genetic variation and disease in the genome. Their discovery remains challenging, however, in large part due to the scarcity of validated SV callsets and comprehensive benchmarks, which are essential for method development and evaluation. The growing number of data-driven learning-based approaches for SV discovery, in particular, requires large, diverse, and well-balanced training datasets to achieve reliable performance. To address this need, SV simulation has served as a key tool for assessing method performance and training SV models. However, existing SV simulators only support a fixed and limited set of SV classes and do not provide fine-grained control over the placement of SVs within specific contexts of the genome. Here we present insilicoSV, a versatile framework for SV simulation, which models SVs using a simple and flexible grammar, allowing users to easily define standard and custom arbitrary genome rearrangements, as well as encode genome placement constraints. This design allows insilicoSV to naturally support new and bespoke SV types, such as the complex rearrangements of cancer genomes. In addition to grammar-based modeling, insilicoSV provides built-in support for 26 predefined SV types, placement of user-provided SVs, small variant simulation, streamlined workflows for the simulation of genome evolution and genome mixtures, read simulation, alignment, and visualization. These features enable the creation of comprehensive genomic datasets for a variety of downstream applications, such as in-depth benchmarking of alignment and variant calling methods, as well as training of data-driven learning-based approaches for SV detection. AVAILABILITY AND IMPLEMENTATION: insilicoSV is available under the MIT license at https://github.com/PopicLab/insilicoSV and https://doi.org/10.5281/zenodo.17402009.

Software↗

PubMed2026

ARGformer: learning on ancestral recombination graphs with transformers.

MOTIVATION: Recent advances in inference of the ancestral recombination graph (ARG), which describes how segments of chromosomes trace back through recombination and shared lineages, have made it possible to reconstruct genome-wide genealogies for large cohorts, but it remains difficult to summarize and use this information for population genetic analyses. RESULTS: We present ARGformer, an encoder-only transformer that learns context-dependent embeddings with a self-supervised masked objective finetuned with contrastive learning for downstream retrieval tasks. We train ARGformer on genealogies from coalescent simulations and on genealogies inferred from ancient and present-day Homo sapiens genomes. Using only these learned embeddings, without access to genotype matrices, ARGformer captures patterns of global population structure and supports ancestry inference through clustering and nearest-neighbor retrieval. On genealogies that include archaic hominins, ARGformer can highlight Denisovan-derived segments in Oceanian genomes and reveals Oceanian-like ancestry in South American Indigenous populations. AVAILABILITY AND IMPLEMENTATION: ARGformer is available at https://github.com/AI-sandbox/ARGformer.

Humans↗

PubMed2025

Detecting Interspecific Positive Selection Using Convolutional Neural Networks.

Traditional statistical methods using maximum likelihood and Bayesian inference can detect positive selection from an interspecific phylogeny and a codon sequence alignment based on model assumptions, but they are prone to false positives due to alignment errors and can lack power. These problems are particularly pronounced when faced with high levels of indels and divergence. To address these issues, we trained and tested convolutional neural network models on simulated data and achieved higher accuracy in detecting selection across a specific range of phylogenetic scenarios and evolutionary modes. This advantage is particularly evident when performing inference on noisy data prone to misalignments. Our method shows some ability to account for these errors, where most statistical frameworks fail to do so in a tractable manner. We explore the generalizability of our convolutional neural network models to unseen evolutionary scenarios and identify future avenues to achieve broader utility. Once trained, our convolutional neural network model is faster at test time, making it a scalable alternative to traditional statistical methods for large-scale, multigene analyses. In addition to binary classification (inference of the presence or absence of positive selection during the evolution of the sequences), we use saliency maps to understand what the model learns and observe how this could be leveraged for sitewise inference of positive selection.

Neural Networks, Computer↗