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Results for “Performance benchmarking”

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PubMed2003

Mining gene expression data using a novel approach based on hidden Markov models.

In this work we have developed a new framework for microarray gene expression data analysis. This framework is based on hidden Markov models. We have benchmarked the performance of this probability model-based clustering algorithm on several gene expression datasets for which external evaluation criteria were available. The results showed that this approach could produce clusters of quality comparable to two prevalent clustering algorithms, but with the major advantage of determining the number of clusters. We have also applied this algorithm to analyze published data of yeast cell cycle gene expression and found it able to successfully dig out biologically meaningful gene groups. In addition, this algorithm can also find correlation between different functional groups and distinguish between function genes and regulation genes, which is helpful to construct a network describing particular biological associations. Currently, this method is limited to time series data. Supplementary materials are available at http://www.bioinfo.tsinghua.edu.cn/~rich/hmmgep_supp/.

Algorithms↗

PubMed1999

A novel learning algorithm which improves the partial fault tolerance of multilayer neural networks.

The paper deals with the problem of fault tolerance in a multilayer perceptron network. Although it already possesses a reasonable fault tolerance capability, it may be insufficient in particularly critical applications. Studies carried out by the authors have shown that the traditional backpropagation learning algorithm may entail the presence of a certain number of weights with a much higher absolute value than the others. Further studies have shown that faults in these weights is the main cause of deterioration in the performance of the neural network. In other words, the main cause of incorrect network functioning on the occurrence of a fault is the non-uniform distribution of absolute values of weights in each layer. The paper proposes a learning algorithm which updates the weights, distributing their absolute values as uniformly as possible in each layer. Tests performed on benchmark test sets have shown the considerable increase in fault tolerance obtainable with the proposed approach as compared with the traditional backpropagation algorithm and with some of the most efficient fault tolerance approaches to be found in literature.

Journal Article↗

PubMed2026

Towards efficient perturbation for the noncoding genome.

Deciphering the functionality of the noncoding genome, which includes important cis-regulatory elements (CREs) and transcribed noncoding RNA genes, remains technically challenging. Here, using massively parallel genetic screening, we systematically benchmark the performance of five representative loss-of-function perturbation tools, including single-guide RNA (gRNA) mediated SpCas9 cleavage or CRISPR interference, and paired gRNA (pgRNA) involved dual-SpCas9, Big Papi (paired SpCas9 and SaCas9) or dual-enAsCas12a fragment deletion methods, in decoding the roles of the noncoding genome. For targeting CREs such as enhancers, dual-SpCas9 outperforms other methods with superior efficiency in destroying functional genomic regions. For perturbing noncoding RNA genes, in addition to dual-SpCas9, other RNA-targeting methods such as RNA interference are recommended to discriminate transcript-dependent or -independent roles. A deep learning model, DeepDC, with an associated web server, is built to facilitate optimal dual-SpCas9 pgRNA design for efficiently deleting a genomic fragment. Together, our work provides practical guidance on selecting appropriate loss-of-function tools to resolve the functional complexity of the noncoding genome.

CRISPR-Cas Systems↗

PubMed2002

Development of a model for prediction of survival in pediatric trauma patients: comparison of artificial neural networks and logistic regression.

BACKGROUND/PURPOSE: There is a paucity of outcome prediction models for injured children. Using the National Pediatric Trauma Registry (NPTR), the authors developed an artificial neural network (ANN) to predict pediatric trauma death and compared it with logistic regression (LR). METHODS: Patients in the NPTR from 1996 through 1999 were included. Models were generated using LR and ANN. A data search engine was used to generate the ANN with the best fit for the data. Input variables included anatomic and physiologic characteristics. There was a single output variable: probability of death. Assessment of the models was for both discrimination (ROC area under the curve) and calibration (Lemeshow-Hosmer C-Statistic). RESULTS: There were 35,385 patients. The average age was 8.1 +/- 5.1 years, and there were 1,047 deaths (3.0%). Both modeling systems gave excellent discrimination (ROC A(z): LR = 0.964, ANN = 0.961). However, LR had only fair calibration, whereas the ANN model had excellent calibration (L/H C stat: LR = 36, ANN = 10.5). CONCLUSIONS: The authors were able to develop an ANN model for the prediction of pediatric trauma death, which yielded excellent discrimination and calibration exceeding that of logistic regression. This model can be used by trauma centers to benchmark their performance in treating the pediatric trauma population.

Calibration↗

PubMed2001

Model-based clustering and data transformations for gene expression data.

MOTIVATION: Clustering is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in this context. Clustering algorithms based on probability models offer a principled alternative to heuristic algorithms. In particular, model-based clustering assumes that the data is generated by a finite mixture of underlying probability distributions such as multivariate normal distributions. The issues of selecting a 'good' clustering method and determining the 'correct' number of clusters are reduced to model selection problems in the probability framework. Gaussian mixture models have been shown to be a powerful tool for clustering in many applications. RESULTS: We benchmarked the performance of model-based clustering on several synthetic and real gene expression data sets for which external evaluation criteria were available. The model-based approach has superior performance on our synthetic data sets, consistently selecting the correct model and the number of clusters. On real expression data, the model-based approach produced clusters of quality comparable to a leading heuristic clustering algorithm, but with the key advantage of suggesting the number of clusters and an appropriate model. We also explored the validity of the Gaussian mixture assumption on different transformations of real data. We also assessed the degree to which these real gene expression data sets fit multivariate Gaussian distributions both before and after subjecting them to commonly used data transformations. Suitably chosen transformations seem to result in reasonable fits. AVAILABILITY: MCLUST is available at http://www.stat.washington.edu/fraley/mclust. The software for the diagonal model is under development. CONTACT: kayee@cs.washington.edu. SUPPLEMENTARY INFORMATION: http://www.cs.washington.edu/homes/kayee/model.

Algorithms↗

PubMed2026

Comprehensive Evaluation and Explainable Interpretation of Peptide-HLA Binding Prediction Tools.

Accurate prediction of peptide binding to human leukocyte antigen class I (HLA-I) molecules is critical for advancing immunological research, particularly in vaccine design and immunotherapy. However, limitations in model performance, interpretability, and dataset quality impede the widespread adoption of existing predictive tools. Here, we present a comprehensive evaluation of 17 HLA-I peptide binding prediction models, utilizing a meticulously curated dataset comprising over 290,000 peptides spanning 44 HLA-I alleles. We assessed model accuracy, robustness, and interpretability, employing explainability techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to elucidate underlying prediction mechanisms. Our results reveal substantial performance disparities, with self-attention-based models, including STMHCpan and BigMHC, exhibiting superior accuracy. Notably, the capsule network model CapsNet-MHC_AN demonstrated robust performance. Models trained on eluted ligand datasets outperformed those relying on binding affinity data, underscoring the critical role of high-quality training data. Ensemble and multi-algorithm approaches further improved prediction reliability. These findings highlight the need for ongoing innovation in model architecture, integration of diverse and high-quality datasets, and incorporation of structural predictors to develop more accurate, interpretable, and clinically applicable HLA-I peptide binding prediction tools.

HLA-I binding↗

PubMed2026

Genome-resolved assessment of archaeal diversity in full-scale anaerobic digesters reveals variability in mcrA primer coverage.

AIMS: Methanogenic archaea are key players in anaerobic digestion, driving methane production in biogas reactors. This study aimed to assess the diversity of methanogenic archaea in full-scale anaerobic digesters using genome-resolved metagenomics and to systematically evaluate the taxonomic coverage of commonly used mcrA-targeted qPCR primer sets against this genomic framework. METHODS AND RESULTS: Methanogenic diversity was assessed using 113 dereplicated archaeal metagenome-assembled genomes (MAGs) recovered from 109 full-scale anaerobic digesters treating diverse substrates. Genome-resolved analyses revealed a diverse archaeal community spanning multiple phyla, dominated by Halobacteriota and Methanobacteriota, with additional representatives from Methanobacteriota_B, Thermoplasmatota, and Thermoproteota. The presence of the mcrA gene was identified in a subset 55 MAGs, which were subsequently used as the genomic framework to evaluate six commonly used mcrA qPCR primer sets in silico. This subset clustered into nine phylogenetic groups and formed the basis for the primer coverage analysis. The evaluation revealed marked differences in taxonomic coverage among primer sets. Most primers preferentially detected Methanobacteriales and Methanosarcinales, while underrepresenting or excluding other methanogenic lineages, including H₂-dependent methylotrophic Methanomassiliicoccaceae. CONCLUSIONS: Commonly used mcrA primer sets differ substantially in their ability to capture methanogenic diversity, with some showing broad representation of reactor-associated methanogens and others exhibiting strong lineage-specific biases. Genome-resolved metagenomics provides an effective framework for benchmarking primer performance and supports the selection and improvement of molecular tools for more accurate monitoring of anaerobic digestion systems.

Archaea↗

PubMed2001

Governing the borderlands: decoding the power of aid.

This article examines aid practice, that is, the public-private contractual networks that link donor governments, UN agencies, military establishments, NGOs, private companies and others, as a relation of global liberal governance. In order to fulfil this function, such networks embody what could be called the 'securitisation' of international assistance. Based upon ideas of human security and ameliorating the effects of poverty and vulnerability reduction, aid is now seen as playing a direct security role. Rather than being concerned with relations between states, the primary aim of this security paradigm is to modulate and change the behaviour of populations within them. In doing so, it is able to exploit the opportunities afforded by privatisation. At the same time, however, aid as security is confronted by its own particular problem of 'governing at a distance'; how can calculations made by leading states be transformed into actions at the global edge when a multitude of private and non-government implementors now intervene? The article concludes by examining the contribution of risk analysis to solving this problem and, especially, the development of new contractual regimes based around technical standardisation, benchmarking and performance auditing. Through such technologies, metropolitan states are learning how to manage the public-private networks of aid practice and, as a result, to govern the borderlands in new ways.

Humans↗

PubMed2000

Tuning diversity in bagged ensembles.

In this paper, we investigate how the level of diversity amongst individual neural networks in a bagged ensemble can significantly influence overall ensemble generalization performance. We propose a new technique that tunes this diversity so that ensemble generalization performance is optimized and evaluate its performance on benchmark regression data-sets.

Algorithms↗

PubMed2026

Computed tomography-guided precision biopsy combined with metagenomic next-generation sequencing for etiological diagnosis in patients with blood culture-negative systemic infections.

ObjectiveTo evaluate the diagnostic efficacy of computed tomography-guided percutaneous biopsy combined with metagenomic next-generation sequencing in patients with blood culture-negative systemic infections and to assess the clinical impact of using this combined strategy for etiological confirmation and guidance of targeted antimicrobial therapy.MethodsThis single-center retrospective observational cohort study enrolled 78 patients who met the Sepsis-3 consensus criteria for suspected systemic infection and had negative conventional microbiological work-ups (at least two sets of blood cultures) between April 2022 and March 2025. All patients underwent computed tomography-guided biopsy of radiologically identified infectious foci, with specimens processed concurrently for conventional culture and metagenomic next-generation sequencing. Diagnostic performance was benchmarked against the final comprehensive clinical diagnosis, and the influence of metagenomic next-generation sequencing findings on antimicrobial therapy modification was analyzed. Sample size calculation, based on a prior study estimating an metagenomic next-generation sequencing detection rate of 85% (α = 0.05, β = 0.2), indicated a minimum of 68 cases; accordingly, 78 patients were enrolled.ResultsComputed tomography-guided biopsy was technically successful in all 78 patients (100%). The pathogen detection rate of metagenomic next-generation sequencing (91.0%, 71/78) was significantly higher than that of conventional culture (55.1%, 43/78; p < 0.001). Using the final clinical diagnosis as the reference standard, metagenomic next-generation sequencing achieved a sensitivity of 94.7% (95% confidence interval: 86.9-98.5), specificity of 100.0% (95% confidence interval: 29.2-100.0), positive predictive value of 100.0% (95% confidence interval: 94.9-100.0), and negative predictive value of 42.9% (95% confidence interval: 9.9-81.6). Among the 35 culture-negative specimens, metagenomic next-generation sequencing established a definitive microbiological diagnosis in 28 cases (80.0%) and detected polymicrobial infections in 11 cases (14.1% of the cohort). Antimicrobial therapy was rationally adjusted based on metagenomic next-generation sequencing results in 69.2% (54/78) of the patients.ConclusionsThe integration of computed tomography-guided precision biopsy with metagenomic next-generation sequencing offers a highly effective diagnostic approach for blood culture-negative systemic infections. This synergistic strategy improves etiological diagnosis by providing high-yield target specimens that enable comprehensive, unbiased pathogen screening, facilitates differentiation between infectious and non-infectious etiologies, and supplies critical evidence for guiding precision antimicrobial therapy. These findings highlight the growing role of interventional radiology in the contemporary framework of precision infectious disease management.

Humans↗

PubMed1998

Size characteristics of larger academic human environmental health programs in the United States.

We have performed a benchmark exercise evaluating larger academic programs in human environmental health sciences. These programs are located at schools of public health and at other institutions that have NIEHS Centers of Excellence. The largest programs were those in which there was both an NIEHS center and a public health graduate education program. This suggests that there is synergy between environmental health sciences research and involvement in public and community health.

Data Collection↗

PubMedDate not supplied

Quality of care for coronary heart disease in two countries.

Coronary heart disease is the leading cause of death in the United States and England, and each country devotes substantial resources to its prevention and treatment. We review recent strategies for improving quality of care for coronary heart disease in each country, including clinical guidelines; national standards; performance reports; benchmarking, feedback, and professional leadership; and market-oriented approaches. These strategies highlight the importance of information systems, organizational culture, and incentives to improve the quality of care in both the decentralized health care system of the United States and England's more centralized system.

Benchmarking↗

PubMed2026

Optimized Hot Phenol-Based RNA Extraction from Mycobacteria: A Robust Approach for Reliable Gene Expression Analysis.

Mycobacterium tuberculosis (Mtb) remains a major global health threat, underscoring the need for reliable transcriptomic studies to understand its biology and drug resistance mechanisms. Such analyses depend on obtaining high-quality, high-yield RNA. Although several RNA extraction methods are available, many require expensive reagents, large culture volumes, or specialized equipment, limiting their suitability for large-scale studies, particularly in resource-constrained settings. Here, an optimized Hot Phenol based RNA extraction method specifically tailored for mycobacteria is presented. The method uses minimal culture volume and commonly available reagents to consistently yield high-quality RNA suitable for high-throughput transcriptomic applications. RNA quantity and integrity were assessed by gel electrophoresis and RNA integrity analysis (RIN), and its suitability for downstream applications was confirmed by qPCR and Qubit 4. To benchmark the performance of the optimized method, a parallel RNA extraction using TRIzol and RNeasy under identical experimental conditions was carried out, including the same Mycobacterium species, culture volume, growth phase (logarithmic and stationary), and lysis conditions. This allowed a direct comparison of yield, quality, feasibility, and cost. The optimized Hot Phenol method demonstrated comparable or improved RNA yield and quality while significantly reducing reagent cost and dependence on specialized equipment. Owing to its efficiency, reproducibility, and affordability, this protocol provides a practical alternative for large-scale gene expression and transcriptomic studies in Mtb and other mycobacterial species.

RNA, Bacterial↗

PubMed1998

Panel discussion. Data needs in cancer.

A prospective, comprehensive outcomes database was recently initiated by the National Comprehensive Cancer Network (NCCN) after a 2-year study to test data collection methods and systems. It started with data on 400 patients with newly diagnosed breast cancer at five NCCN sites, and over the next 3 years is projected to grow to include more than 12,000 patients with common cancers treated at all eligible NCCN sites. Among the goals of the database are: 1) to establish the capability to select, analyze, and report patterns of care and outcomes; 2) to allow NCCN members to assess their compliance with NCCN clinical practice guidelines and benchmark their performance against the rest of the NCCN; 3) to establish a true databased continuous quality improvement program; 4) to support clinical disease-oriented research and methodologic studies; and 5) to provide the NCCN with a vehicle for forging partnerships with others in the health-care field, such as the pharmaceutical industry, regulatory agencies, and accrediting bodies. Many of those potential partners were represented on this panel. Panelists discussed the data needs of their organizations, what they are doing to meet those needs, and how a comprehensive database will ultimately help improve patient care.

Aged↗

PubMed2000

Broader range of skills distinguishes successful CFOs.

In recent years, healthcare CFOs have seen their role expand significantly beyond traditional financial duties. A series of trended surveys on CFO roles and responsibilities reveals that today's healthcare CFO requires a broad new range of traits and skills in the areas of leadership, operations, and healthcare strategy. CFOs regard strategic thinking and the ability to communicate clearly as the most important of their essential leadership traits and skills, respectively. Among operational and strategic skills, CFOs most often cite the importance of being able to improve organizational performance and benchmark. Healthcare CFOs can enhance their chances of success by focusing self-improvement efforts on five key areas: implementing the organization's vision; developing tactics that stimulate change; enhancing communication skills; focusing on managing and leading; and strengthening relationships.

Benchmarking↗

PubMed2002

Demonstrating quality at the physician-office level.

A multidisciplinary task force consisting of administration, quality management, directors of service, and network development staff of a nonprofit organization developed and implemented a comprehensive report card process for its 50 physician-office sites. The goals were to develop and implement a process that would provide a comprehensive summary of the administrative and clinical activity of all network sites, to develop a framework for identifying best practices within the network, and to enhance motivation to improve performance through benchmarking with physician offices within the network. This initiative also sought to transform the voluminous data collected into meaningful information that could be used to improve the process of operations and outcomes.

Benchmarking↗

PubMedDate not supplied

[External quality assessment of clinical services in Europe].

All countries and clinical specialties have some elements of systems for quality improvement, but their aims, configurations, models standards and assessments are often not formally recognised or integrated. External programmes to assess service delivery in Europe include the International Standardisation Organisation (ISO) and Excellence (EFQM - European Foundation Quality Management) models (industry based, management focus), peer review and accreditation (health care based, professional focus) and inspection (regulatory, safety-focused). Patient surveys and disease registers also contribute to assessment and benchmarking best performance. There are legal, cultural, professional and commercial reasons to adopt common core standards but there is little legislative framework to allow formal harmonisation within and between countries and clinical specialties. Examples are given of various approaches to the definition, assessment and improvement of standards for clinical services in Europe in order to encourage specialist associations to develop self-regulations based on the experience of others. Clinical practice and clinical services could be more efficiently and effectively harmonised by the professions than by their respective governments.

Accreditation↗

PubMed2003

Off the shelf or recalibrate? customizing a risk index for assessing mortality.

BACKGROUND: Public "report cards" for cardiac surgery have been freely available from a variety of sources. These risk-adjusted indices serve as a means of benchmarking outcomes performances, allowing comparisons of outcomes between surgical programs, and quantifying quality improvement programs. We examined two alternative strategies for using previously developed risk-adjusted mortality models in a community hospital: (1) using the model "off the shelf" (OTS) and (2) recalibrating the existing model (RM) to fit the institution-specific population. METHODS: Six OTS models were used: Parsonnet (PA), Canadian (CA), Cleveland (CL), Northern New England (NNE), New York (NY), and New Jersey (NJ). The RM models were created by each model's independent variables and definitions and adjusting the weighting with logistic regression methods. The accuracy, the C statistic, and the precision of each model were assessed for in-hospital mortality. We compared the OTS version of each model to the RM version with methods detailed by Hanley and McNeil. RESULTS: The RM C statistic was improved for all risk-adjusted models, most notably in the statistical improvement seen in the PA (0.053 improvement) and NJ (0.052 improvement) indices. Statistical gains in precision were also seen in the RM models for the PA, CL, and NNE indices. Conversely, one model, the CA model, was more poorly calibrated in the RM model compared with the OTS model, despite an improved C statistic (0.062). CONCLUSIONS: The RM strategy provides institution-explicit models that demonstrate a higher degree of accuracy and precision than the OTS models.

Age Factors↗