Revealing strengths and weaknesses of methods for gene network inference.
Ontology highlight
ABSTRACT:
SUBMITTER: Marbach D
PROVIDER: S-EPMC2851985 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.
| S-EPMC9997554 | biostudies-literature
Project description:I present here an in-depth, although non-exhaustive, review of two topics in molecular dating. Clock models, which describe the evolution of the rate of evolution, are considered first. Some of the shortcomings of popular approaches-uncorrelated clock models in particular-are presented and discussed. Autocorrelated models are shown to be more reasonable from a biological perspective. Some of the most recent autocorrelated models also rely on a coherent treatment of instantaneous and average substitution rates while previous models are based on implicit approximations. Second, I provide a brief overview of the processes involved in collecting and preparing fossil data. I then review the main techniques that use this data for calibrating the molecular clock. I argue that, in its current form, the fossilized birth-death process relies on assumptions about the mechanisms underlying fossilization and the data collection process that may negatively impact the date estimates. Node-dating approaches make better use of the data available, even though they rest on paleontologists' intervention to prepare raw fossil data. Altogether, this study provides indications that may help practitioners in selecting appropriate methods for molecular dating. It will also hopefully participate in defining the contour of future methodological developments in the field.
| S-EPMC7267027 | biostudies-literature
Project description:BackgroundThe Extended Strengths and Weaknesses Assessment of Normal Behavior (E-SWAN) reconceptualizes each diagnostic criterion for selected DSM-5 disorders as a behavior, which can range from high (strengths) to low (weaknesses). Initial development focused on Panic Disorder, Social Anxiety, Major Depression, and Disruptive Mood Dysregulation Disorder.MethodsData were collected from 523 participants (ages 6-17). Parents completed each of the four E-SWAN scales and traditional unidirectional scales addressing the same disorders. Distributional properties, Item Response Theory Analysis (IRT), and Receiver Operating Characteristic (ROC) curves were used to assess and compare the performance of E-SWAN and traditional scales.ResultsIn contrast to the traditional scales, which exhibited truncated distributions, all four E-SWAN scales had symmetric distributions. IRT analyses indicate the E-SWAN subscales provided reliable information about respondents throughout the population distribution; traditional scales only provided reliable information about respondents at the high end of the distribution. Predictive value for DSM-5 diagnoses was comparable to prior scales.ConclusionsE-SWAN bidirectional scales can capture the full spectrum of the population distribution of behavior underlying DSM disorders. The additional information provided can better inform examination of inter-individual variation in population studies, as well as facilitate the identification of factors related to resiliency in clinical samples.
| S-EPMC6916607 | biostudies-literature
Project description:Multivalent vaccines addressing an increasing number of Streptococcus pneumoniae types (7-, 10-, 13-, 15-, 20-valent) have been licensed over the last 22 years. The use of polysaccharide-protein conjugate vaccines has been pivotal in reducing the incidence of invasive pneumococcal disease despite the emergence of non-vaccine serotypes. Notwithstanding its undoubtable success, some weaknesses have called for continuous improvement of pneumococcal vaccination. For instance, despite their inclusion in pneumococcal conjugate vaccines, there are challenges associated with some serotypes. In particular, Streptococcus pneumoniae type 3 remains a major cause of invasive pneumococcal disease in several countries.Here a deep revision of the strengths and weaknesses of the licensed pneumococcal conjugate vaccines and other vaccine candidates currently in clinical development is reported.
| S-EPMC10027807 | biostudies-literature
Project description:Food labeling is increasingly expanding and adding more information to the food package. There is strong evidence about nutrition labeling effectiveness in driving food choice, especially if displayed in the front of package (FoP). Despite the growing attention to nutrition and sustainable diets, few countries have implemented sustainable labels or eco-labels that could address economic, social and/or environmental concerns. Implementing new techniques of eco-labeling emerges as a consumer-focused solution. However, evidence of the effectiveness of eco-labeling in driving consumers' choices is heterogeneous and not univocal. Thus, this review aims to summarize the evidence about the effectiveness of FoP eco-labeling in driving food choice and provide a reference framework of the eco-labeling initiatives relative to food package labeling. This narrative review addresses both the potential benefits as well as the main concerns that arise from the use of eco-labels. Although eco-labeling seems to provide a series of sustainability benefits for producers and consumers, the implementation of such policies should take into consideration potential trade-offs and inter-sectorial coordination to obtain bigger impacts, assuming that a policy itself cannot transform the whole food system. Eco-labeling could be encouraged and implemented within a set of policies shaping sustainable food systems.
| S-EPMC11005915 | biostudies-literature
Project description:BACKGROUND:Relatively small changes to gene expression data dramatically affect co-expression networks inferred from that data which, in turn, can significantly alter the subsequent biological interpretation. This error propagation is an underappreciated problem that, while hinted at in the literature, has not yet been thoroughly explored. Resampling methods (e.g. bootstrap aggregation, random subspace method) are hypothesized to alleviate variability in network inference methods by minimizing outlier effects and distilling persistent associations in the data. But the efficacy of the approach assumes the generalization from statistical theory holds true in biological network inference applications. RESULTS:We evaluated the effect of bootstrap aggregation on inferred networks using commonly applied network inference methods in terms of stability, or resilience to perturbations in the underlying expression data, a metric for accuracy, and functional enrichment of edge interactions. CONCLUSION:Bootstrap aggregation results in improved stability and, depending on the size of the input dataset, a marginal improvement to accuracy assessed by each method's ability to link genes in the same functional pathway.
| S-EPMC6186128 | biostudies-other
Project description:Uveal melanoma is the most common primary malignancy of the eye, and a number of discoveries in the last decade have led to a more thorough molecular characterization of this cancer. However, the prognosis remains dismal for patients with metastases, and there is an urgent need to identify treatments that are effective for this stage of disease. Animal models are important tools for preclinical studies of uveal melanoma. A variety of models exist, and they have specific advantages, disadvantages, and applications. In this review article, these differences are explored in detail, and ideas for new models that might overcome current challenges are proposed.
| S-EPMC7065156 | biostudies-literature
Project description:Abstract Motivation Biological processes are regulated by underlying genes and their interactions that form gene regulatory networks (GRNs). Dysregulation of these GRNs can cause complex diseases such as cancer, Alzheimer’s and diabetes. Hence, accurate GRN inference is critical for elucidating gene function, allowing for the faster identification and prioritization of candidate genes for functional investigation. Several statistical and machine learning-based methods have been developed to infer GRNs based on biological and synthetic datasets. Here, we developed a method named AGRN that infers GRNs by employing an ensemble of machine learning algorithms. Results From the idea that a single method may not perform well on all datasets, we calculate the gene importance scores using three machine learning methods—random forest, extra tree and support vector regressors. We calculate the importance scores from Shapley Additive Explanations, a recently published method to explain machine learning models. We have found that the importance scores from Shapley values perform better than the traditional importance scoring methods based on almost all the benchmark datasets. We have analyzed the performance of AGRN using the datasets from the DREAM4 and DREAM5 challenges for GRN inference. The proposed method, AGRN—an ensemble machine learning method with Shapley values, outperforms the existing methods both in the DREAM4 and DREAM5 datasets. With improved accuracy, we believe that AGRN inferred GRNs would enhance our mechanistic understanding of biological processes in health and disease. Availabilityand implementation https://github.com/DuaaAlawad/AGRN. Supplementary information Supplementary data are available at Bioinformatics online.
| S-EPMC10082608 | biostudies-literature
Project description:Although the challenge of gene regulatory network inference has been studied for more than a decade, it is still unclear how well network inference methods work when applied to real data. Attempts to benchmark these methods on experimental data have yielded mixed results, in which sometimes even the best methods fail to outperform random guessing, and in other cases they perform reasonably well. So, one of the most valuable contributions one can currently make to the field of network inference is to benchmark methods on experimental data for which the true underlying network is already known, and report the results so that we can get a clearer picture of their efficacy. In this paper, we report results from the first, to our knowledge, benchmarking of network inference methods on single cell E. coli transcriptomic data. We report a moderate level of accuracy for the methods, better than random chance but still far from perfect. We also find that some methods that were quite strong and accurate on microarray and bulk RNA-seq data did not perform as well on the single cell data. Additionally, we benchmark a simple network inference method (Pearson correlation), on data generated through computer simulations in order to draw conclusions about general best practices in network inference studies. We predict that network inference would be more accurate using proteomic data rather than transcriptomic data, which could become relevant if highthroughput proteomic experimental methods are developed in the future. We also show through simulations that using a simplified model of gene expression that skips the mRNA step tends to substantially overestimate the accuracy of network inference methods, and advise against using this model for future in silico benchmarking studies.
| S-EPMC10197678 | biostudies-literature
Project description:Using the WHO pharmacovigilance (PV) indicators as a framework, this study aimed to explore the structures, processes, and outcomes of three Arab countries' (Jordan, Oman, and Kuwait) PV systems to inform recommendations for countries with nascent PV systems. A mixed-methods design involving document review, semi-structured interviews, and a questionnaire was employed. Fifty-six key informants from the three countries' national PV centres (NPVCs) and pharmaceutical industry were interviewed. The questionnaire collecting quantitative measures was only completed by Oman and Kuwait's NPVCs. Using the framework, system strengths were attributed to the presence of "core" structural indicators, including a dedicated and officially recognised NPVC, PV legislation, and a national PV advisory committee, as well as "complementary" structural indicators, e.g., a computerised case-report management system. Contrastingly, weaknesses were attributed to the absence of these indicators plus other "core" structural indicators, namely, regular financial provision and adequate staff. Other weaknesses were attributed to low performance in "core" process and outcome indicators including reporting rates, reporter awareness, and signal detection. Greater governmental prioritisation through the provision of legislative enforcements, resources, and expertise as part of a well-structured system is required. More regional coordination efforts are needed to allow for sharing of expertise in order to bolster nascent systems.
| S-EPMC8909061 | biostudies-literature