Career trajectories, transition rates, and birthdate distributions: the rocky road from youth to senior level in men's European football.

This study aimed to assess youth-to-senior transition rates, quantify the magnitude of relative age effect (RAEs), and evaluate how RAEs affect these transitions in 9,527 men's national football players of England, France, Germany, Italy, and Spain. Regardless of national team, only -15%, 25%, and 40% of U17, U19, and U21 players successfully transitioned to the senior team, respectively, whilst -14%-24% progressed to senior level without being selected during youth. Data suggested a skewed birthdate distribution favouring relatively older players at U17, U19, and U21 levels across all countries, whereas RAEs were also present in England, Italy, and Spain at senior level. Youth-to-senior transition rates were modulated by birthdate at U17 and U19, whereby Q4 players were -2 and 1.5 times more likely to successfully transition at senior level than Q1 players, respectively. Selection at youth international level does not guarantee selection at senior level, but does make it more likely. Moreover, relatively younger athletes are disadvantaged in youth categories, although are more likely to transition to senior level once they have entered the pathway.

DHFS-ECM: Design of a Dual Heuristic Feature Selection-based Ensemble Classification Model for the Identification of Bamboo Species from Genomic Sequences.

Background: Analyzing genomic sequences plays a crucial role in understanding biological diversity and classifying Bamboo species. Existing methods for genomic sequence analysis suffer from limitations such as complexity, low accuracy, and the need for constant reconfiguration in response to evolving genomic datasets. Aim: This study addresses these limitations by introducing a novel Dual Heuristic Feature Selection-based Ensemble Classification Model (DHFS-ECM) for the precise identification of Bamboo species from genomic sequences. Methods: The proposed DHFS-ECM method employs a Genetic Algorithm to perform dual heuristic feature selection. This process maximizes inter-class variance, leading to the selection of informative N-gram feature sets. Subsequently, intra-class variance levels are used to create optimal training and validation sets, ensuring comprehensive coverage of class-specific features. The selected features are then processed through an ensemble classification layer, combining multiple stratification models for species-specific categorization. Results: Comparative analysis with state-of-the-art methods demonstrate that DHFS-ECM achieves remarkable improvements in accuracy (9.5%), precision (5.9%), recall (8.5%), and AUC performance (4.5%). Importantly, the model maintains its performance even with an increased number of species classes due to the continuous learning facilitated by the Dual Heuristic Genetic Algorithm Model. Conclusion: DHFS-ECM offers several key advantages, including efficient feature extraction, reduced model complexity, enhanced interpretability, and increased robustness and accuracy through the ensemble classification layer. These attributes make DHFS-ECM a promising tool for real-time clinical applications and a valuable contribution to the field of genomic sequence analysis.

Selection Bias Risk in Randomized Controlled Trials Rated as Low Bias Using Risk of Bias, Version 2 (RoB2) Tool.

Our study aimed to establish the risk of selection bias in randomized controlled trials (RCT) that were overall rated as having "low bias" risk according to Cochrane's Risk of Bias, version 2 (RoB 2) tool. A systematic literature search of current systematic reviews of RCTs was conducted. From the identified reviews, RCTs with overall "high bias" and "low bias" RoB 2 risk ratings were extracted. All RCTs were statistically tested for selection bias risk. From the test results, true positive, true negative, false positive, or false negative ratings were established, and the false omission rate (FOR) with a 95% confidence interval (CI) was computed. Subgroup analysis was conducted by computing the negative likelihood ratio (-LR) concerning RoB 2 domain 1 ratings: bias arising from the randomization process. A total of 1070 published RCTs (median publication year: 2018; interquartile range: 2013-2020) were identified and tested. We found that 7.61% of all "low bias" (RoB 2)-rated RCTs were of high selection bias risk (FOR 7.61%; 95% CI: 6.31%-9.14%) and that the likelihood for high selection bias risk in "low bias" (RoB 2 domain 1)-rated RCTs was 6% higher than that for low selection bias risk (-LR: 1.06; 95% CI: 0.98-1.15). These findings raise issues about the validity of "low bias" risk ratings using Cochrane's RoB 2 tool as well as about the validity of some of the results from recently published RCTs. Our results also suggest that the likelihood of a "low bias" risk-rated body of clinical evidence being actually bias-free is low, and that generalization based on a limited, pre-specified set of appraisal criteria may not justify a high level of confidence that such evidence reflects the true treatment effect.

Modelling glioblastoma resistance to temozolomide. A mathematical model to simulate cellular adaptation in vitro.

Drug resistance is one of the biggest challenges in the fight against cancer. In particular, in the case of glioblastoma, the most lethal brain tumour, resistance to temozolomide (the standard of care drug for chemotherapy in this tumour) is one of the main reasons behind treatment failure and hence responsible for the poor prognosis of patients diagnosed with this disease. In this work, we combine the power of three-dimensional in vitro experiments of treated glioblastoma spheroids with mathematical models of tumour evolution and adaptation. We use a novel approach based on internal variables for modelling the acquisition of resistance to temozolomide that was observed in experiments for a group of treated spheroids. These internal variables describe the cell's phenotypic state, which depends on the history of drug exposure and affects cell behaviour. We use model selection to determine the most parsimonious model and calibrate it to reproduce the experimental data, obtaining a high level of agreement between the in vitro and in silico outcomes. A sensitivity analysis is carried out to investigate the impact of each model parameter in the predictions. More importantly, we show how the model is useful for answering biological questions, such as what is the intrinsic adaptation mechanism, or for separating the sensitive and resistant populations. We conclude that the proposed in silico framework, in combination with experiments, can be useful to improve our understanding of the mechanisms behind drug resistance in glioblastoma and to eventually set some guidelines for the design of new treatment schemes.

Predicting Conversion Time from Mild Cognitive Impairment to Dementia with Interval-Censored Models.

Background: Mild cognitive impairment (MCI) patients are at a high risk of developing Alzheimer's disease and related dementias (ADRD) at an estimated annual rate above 10%. It is clinically and practically important to accurately predict MCI-to-dementia conversion time. Objective: It is clinically and practically important to accurately predict MCI-to-dementia conversion time by using easily available clinical data. Methods: The dementia diagnosis often falls between two clinical visits, and such survival outcome is known as interval-censored data. We utilized the semi-parametric model and the random forest model for interval-censored data in conjunction with a variable selection approach to select important measures for predicting the conversion time from MCI to dementia. Two large AD cohort data sets were used to build, validate, and test the predictive model. Results: We found that the semi-parametric model can improve the prediction of the conversion time for patients with MCI-to-dementia conversion, and it also has good predictive performance for all patients. Conclusions: Interval-censored data should be analyzed by using the models that were developed for interval- censored data to improve the model performance.

Improving selection procedures in health professions education from the applicant perspective: an interview study.

INTRODUCTION: Applicant perceptions of selection impact motivation and performance during selection, and student diversity. However, in-depth insight into which values underly these perceptions is lacking, creating challenges for aligning selection procedures with applicant perceptions. This qualitative interview study aimed to identify values applicants believe should underlie selection, and how, according to applicants, these values should be used to make specific improvements to selection procedures in undergraduate health professions education (HPE). METHODS: Thirty-one applicants to five undergraduate HPE programs in the Netherlands participated in semi-structured interviews using Appreciative Inquiry, an approach that focuses on what goes well to create vision for improvement, to guide the interviews. Transcriptions were analyzed using thematic analysis, adopting a constructivist approach. RESULTS: Applicants' values related to the aims of selection, the content of selection, and the treatment of applicants. Applicants believed that selection procedures should aim to identify students who best fit the training and profession, and generate diverse student populations to fulfill societal needs. According to applicants, the content of selection should be relevant for the curriculum and profession, assess a comprehensive set of attributes, be of high quality, allow applicants to show who they are, and be adapted to applicants' current developmental state. Regarding treatment, applicants believed that selection should be a two-way process that fosters reflection on study choice, be transparent about what applicants can expect, safeguard applicants' well-being, treat all applicants equally, and employ an equitable approach by taking personal circumstances into account. Applicants mentioned specific improvements regarding each value. DISCUSSION: Applicants' values offer novel insights into what they consider important preconditions for the design of selection procedures. Their suggested improvements can support selection committees in better meeting applicants' needs.

COMSE: analysis of single-cell RNA-seq data using community detection-based feature selection.

BACKGROUND: Single-cell RNA sequencing enables studying cells individually, yet high gene dimensions and low cell numbers challenge analysis. And only a subset of the genes detected are involved in the biological processes underlying cell-type specific functions. RESULT: In this study, we present COMSE, an unsupervised feature selection framework using community detection to capture informative genes from scRNA-seq data. COMSE identified homogenous cell substates with high resolution, as demonstrated by distinguishing different cell cycle stages. Evaluations based on real and simulated scRNA-seq datasets showed COMSE outperformed methods even with high dropout rates in cell clustering assignment. We also demonstrate that by identifying communities of genes associated with batch effects, COMSE parses signals reflecting biological difference from noise arising due to differences in sequencing protocols, thereby enabling integrated analysis of scRNA-seq datasets of different sources. CONCLUSIONS: COMSE provides an efficient unsupervised framework that selects highly informative genes in scRNA-seq data improving cell sub-states identification and cell clustering. It identifies gene subsets that reveal biological and technical heterogeneity, supporting applications like batch effect correction and pathway analysis. It also provides robust results for bulk RNA-seq data analysis.

Revisiting Dominance in Population Genetics.

Dominance refers to the effect of a heterozygous genotype relative to that of the two homozygous genotypes. The degree of dominance of mutations for fitness can have a profound impact on how deleterious and beneficial mutations change in frequency over time as well as on the patterns of linked neutral genetic variation surrounding such selected alleles. Since dominance is such a fundamental concept, it has received immense attention throughout the history of population genetics. Early work from Fisher, Wright, and Haldane focused on understanding the conceptual basis for why dominance exists. More recent work has attempted to test these theories and conceptual models by estimating dominance effects of mutations. However, estimating dominance coefficients has been notoriously challenging and has only been done in a few species in a limited number of studies. In this review, we first describe some of the early theoretical and conceptual models for understanding the mechanisms for the existence of dominance. Second, we discuss several approaches used to estimate dominance coefficients and summarize estimates of dominance coefficients. We note trends that have been observed across species, types of mutations, and functional categories of genes. By comparing estimates of dominance coefficients for different types of genes, we test several hypotheses for the existence of dominance. Lastly, we discuss how dominance influences the dynamics of beneficial and deleterious mutations in populations and how the degree of dominance of deleterious mutations influences the impact of inbreeding on fitness.

GRU-powered sleep stage classification with permutation-based EEG channel selection.

We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU's ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels.

Spatial and seasonal variability in benthic impact of mussel farms: Predicting and mitigating impacts using ambient oxygen conditions.

Environmental impacts are a cause for concern when developing and expanding aquaculture and to be sustainable potential negative effects need to be addressed. The intensity and extent of these impacts likely vary among sites and seasons, depending on multiple factors including the physical and biological setting and operational aspects. Using a combination of sampling techniques, we investigated the spatial variability in epibenthic impacts in eleven commercial mussel farms, on the Swedish west coast. We found increased levels of organic content, changes in epibenthic macrofauna and increased cover of Beggiatoa sp., a documented indicator of hypoxia. The extent of these impacts was generally limited to the extent of the farms. Because the cover of Beggiatoa sp. was particularly clear and because oxygen conditions in the sediment is of great importance to the structure and function of these habitats, we analysed spatial patterns using an index of the benthic footprint (BFI) accounting for both intensity and extent of impacts. In the summer, the BFI varied strongly among farm-sites and subsequent analyses showed that it highly correlated with ambient bottom oxygen concentration. Repeated sampling during early spring, however, showed that impacts were quickly reversible also in the most impacted sites. Thus, we conclude that in Swedish coastal waters the benthic footprint calculated on the % cover of Beggiatoa sp. is highly dependent on ambient oxygen concentration. We suggest that knowledge about spatial and temporal patterns of oxygen in the bottom water can be used to predict the severity of impacts and provide an important criterion in a site-selection process aimed at developing a sustainable food industry.

The evolutionary fate of Neanderthal DNA in 30,780 admixed genomes with recent African-like ancestry.

Following introgression, Neanderthal DNA was initially purged from non-African genomes, but the evolutionary fate of remaining introgressed DNA has not been explored yet. To fill this gap, we analyzed 30,780 admixed genomes with African-like ancestry from the All of Us research program, in which Neanderthal alleles encountered novel genetic backgrounds during the last 15 generations. Observed amounts of Neanderthal DNA approximately match expectations based on ancestry proportions, suggesting neutral evolution. Nevertheless, we identified genomic regions that have significantly less or more Neanderthal ancestry than expected and are associated with spermatogenesis, innate immunity, and other biological processes. We also identified three novel introgression desert-like regions in recently admixed genomes, whose genetic features are compatible with hybrid incompatibilities and intrinsic negative selection. Overall, we find that much of the remaining Neanderthal DNA in human genomes is not under strong selection, and complex evolutionary dynamics have shaped introgression landscapes in our species.

Into the Blue: Exploring genetic mechanisms behind the evolution of baleen whales.

Marine ecosystems are ideal for studying evolutionary adaptations involved in lineage diversification due to few physical barriers and reduced opportunities for strict allopatry compared to terrestrial ecosystems. Cetaceans (whales, dolphins, and porpoises) are a diverse group of mammals that successfully adapted to various habitats within the aquatic environment around 50 million years ago. While the overall adaptive transition from terrestrial to fully aquatic species is relatively well understood, the radiation of modern whales is still unclear. Here high-quality genomes derived from previously published data were used to identify genomic regions that potentially underpinned the diversification of baleen whales (Balaenopteridae). A robust molecular phylogeny was reconstructed based on 10,159 single copy and complete genes for eight mysticetes, seven odontocetes and two cetacean outgroups. Analysis of positive selection across 3,150 genes revealed that balaenopterids have undergone numerous idiosyncratic and convergent genomic variations that may explain their diversification. Genes associated with aging, survival and homeostasis were enriched in all species. Additionally, positive selection on genes involved in the immune system were disclosed for the two largest species, blue and fin whales. Such genes can potentially be ascribed to their morphological evolution, allowing them to attain greater length and increased cell number. Further evidence is presented about gene regions that might have contributed to the extensive anatomical changes shown by cetaceans, including adaptation to distinct environments and diets. This study contributes to our understanding of the genomic basis of diversification in baleen whales and the molecular changes linked to their adaptive radiation, thereby enhancing our understanding of cetacean evolution.

Convergent dwarfism consequences of minipigs under independent artificial selections.

BACKGROUND: Currently, diverse minipigs have acquired a common dwarfism phenotype through independent artificial selections. Characterizing the population and genetic diversity in minipigs is important to unveil genetic mechanisms regulating their body sizes and effects of independent artificial selections on those genetic mechanisms. However, full understanding for the genetic mechanisms and phenotypic consequences in minipigs still lag behind. RESULTS: Here, using whole genome sequencing data of 41 pig breeds, including eight minipigs, we identified a large genomic diversity in a minipig population compared to other pig populations in terms of population structure, demographic signatures, and selective signatures. Selective signatures reveal diverse biological mechanisms related to body size in minipigs. We also found evidence for neural development mechanism as a minipig-specific body size regulator. Interestingly, selection signatures within those mechanisms containing neural development are also highly different among minipig breeds. Despite those large genetic variances, PLAG1, CHM, and ESR1 are candidate key genes regulating body size which experience different differentiation directions in different pig populations. CONCLUSIONS: These findings present large variances of genetic structures, demographic signatures, and selective signatures in the minipig population. They also highlight how different artificial selections with large genomic diversity have shaped the convergent dwarfism.

Quantitative selection of sample structures in small-angle scattering using Bayesian methods.

Small-angle scattering (SAS) is a key experimental technique for analyzing nanoscale structures in various materials. In SAS data analysis, selecting an appropriate mathematical model for the scattering intensity is critical, as it generates a hypothesis of the structure of the experimental sample. Traditional model selection methods either rely on qualitative approaches or are prone to overfitting. This paper introduces an analytical method that applies Bayesian model selection to SAS measurement data, enabling a quantitative evaluation of the validity of mathematical models. The performance of the method is assessed through numerical experiments using artificial data for multicomponent spherical materials, demonstrating that this proposed analysis approach yields highly accurate and interpretable results. The ability of the method to analyze a range of mixing ratios and particle size ratios for mixed components is also discussed, along with its precision in model evaluation by the degree of fitting. The proposed method effectively facilitates quantitative analysis of nanoscale sample structures in SAS, which has traditionally been challenging, and is expected to contribute significantly to advancements in a wide range of fields.

Assessing the carotenoid profiles and allelic diversity of yellow maize inbred lines adapted to mid-altitude subhumid maize agroecology in Ethiopia.

Biofortification of provitamin A in maize is an attractive and sustainable remedy to the problem of vitamin A deficiency in developing countries. The utilization of molecular markers represents a promising avenue to facilitate the development of provitamin A (PVA)-enriched maize varieties. We screened 752 diverse tropical yellow/orange maize lines using kompetitive allele-specific PCR (KASP) makers to validate the use of KASP markers in PVA maize breeding. To this end, a total of 161 yellow/orange inbred lines, selected from among the 752 lines, were evaluated for their endosperm PVA and other carotenoid compounds levels in two separate trials composed of 63 and 98 inbred lines in 2020 and 2021, respectively. Significant differences (p < 0.001) were observed among the yellow maize inbred lines studied for all carotenoid profiles. An inbred line TZMI1017, introduced by the International Institute of Tropical Agriculture (IITA) showed the highest level of PVA (12.99 µg/g) and ß-carotene (12.08 µg/g). The molecular screening showed 43 yellow maize inbred lines carrying at least three of the favorable alleles of the KASP markers. TZMI1017 inbred line also carried the favorable alleles of almost all markers. In addition, nine locally developed inbred lines had medium to high PVA concentrations varying from 5.11 µg/g to 10.76 µg/g and harbored the favorable alleles of all the KASP PVA markers. Association analysis between molecular markers and PVA content variation in the yellow/orange maize inbred lines did not reveal a significant, predictable correlation. Further investigation is warranted to elucidate the underlying genetic architecture of the PVA content in this germplasm. However, we recommend strategic utilization of the maize-inbred lines with higher PVA content to enhance the PVA profile of the breeding program's germplasm.

Sparse testing designs for optimizing predictive ability in sugarcane populations.

Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane's yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6×186=1,116 phenotypes). Our study employed three predictive models, including environment, genotype, and genomic markers as main effects, as well as the G×E to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for G×E pattern prediction. Results demonstrate that maximum accuracy for SA ( ρ = 0.611 ) and for TCH ( ρ=0.341 ) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72=12×6) being the most convenient cost-benefit combination.

Genomic prediction for rust resistance in pea.

Genomic selection (GS) has become an indispensable tool in modern plant breeding, particularly for complex traits. This study aimed to assess the efficacy of GS in predicting rust (Uromyces pisi) resistance in pea (Pisum sativum), using a panel of 320 pea accessions and a set of 26,045 Silico-Diversity Arrays Technology (Silico-DArT) markers. We compared the prediction abilities of different GS models and explored the impact of incorporating marker × environment (M×E) interaction as a covariate in the GBLUP (genomic best linear unbiased prediction) model. The analysis included phenotyping data from both field and controlled conditions. We assessed the predictive accuracies of different cross-validation strategies and compared the efficiency of using single traits versus a multi-trait index, based on factor analysis and ideotype-design (FAI-BLUP), which combines traits from controlled conditions. The GBLUP model, particularly when modified to include M×E interactions, consistently outperformed other models, demonstrating its suitability for traits affected by complex genotype-environment interactions (GEI). The best predictive ability (0.635) was achieved using the FAI-BLUP approach within the Bayesian Lasso (BL) model. The inclusion of M×E interactions significantly enhanced prediction accuracy across diverse environments in GBLUP models, although it did not markedly improve predictions for non-phenotyped lines. These findings underscore the variability of predictive abilities due to GEI and the effectiveness of multi-trait approaches in addressing complex traits. Overall, our study illustrates the potential of GS, especially when employing a multi-trait index like FAI-BLUP and accounting for M×E interactions, in pea breeding programs focused on rust resistance.

The proactive and reactive mechanisms of learned spatial suppression.

Selection history refers to the notion that previous allocations of attention or suppression have the potential to elicit lingering and enduring selection biases that are isolated from goal-driven or stimulus-driven attention. However, in the singleton detection mode task, manipulating the selection history of distractors cannot give rise to pure proactive inhibition. Therefore, we employed a combination of a working memory task and a feature search mode task, simultaneously recording cortical activity using EEG, to investigate the mechanisms of suppression guided by selection history. The results from event-related potential and reaction times showed an enhanced inhibitory performance when the distractor was presented at the high-probability location, along with instances where the target appeared at the high-probability location of distractors. These findings demonstrate that a generalized proactive inhibition bias is learned and processed independent of cognitive resources, which is supported by selection history. In contrast, reactive rejection toward the low-probability location was evident through the Pd component under varying cognitive resource conditions. Taken together, our findings indicated that participants learned proactive inhibition when the distractor was at the high-probability location, whereas reactive rejection was involved at low-probability location.

Maternal manipulation of offspring size can trigger the evolution of eusociality in promiscuous species.

Eusocial organisms typically live in colonies with one reproductive queen supported by thousands of sterile workers. It is widely believed that monogamous mating is a precondition for the evolution of eusociality. Here, we present a theoretical model that simulates a realistic scenario for the evolution of eusociality. In the model, mothers can evolve control over resource allocation to offspring, affecting offspring's body size. The offspring can evolve body-size-dependent dispersal, by which they disperse to breed or stay at the nest as helpers. We demonstrate that eusociality can evolve even if mothers are not strictly monogamous, provided that they can constrain their offspring's reproduction through manipulation. We also observe the evolution of social polymorphism with small individuals that help and larger individuals that disperse to breed. Our model unifies the traditional kin selection and maternal manipulation explanations for the evolution of eusociality and demonstrates that-contrary to current consensus belief-eusociality can evolve despite highly promiscuous mating.

Ensemble of machine learning techniques to predict survival in kidney transplant recipients.

Kidney transplant recipients face a high cardiovascular risk, which is a leading cause of death in this patient group. This article proposes the application of clustering techniques and feature selection to predict the survival outcomes of kidney transplant recipients based on machine learning techniques and mainstream statistical methods. First, feature selection techniques (Boruta, Random Survival Forest and Elastic Net) are used to detect the most relevant variables. Subsequently, each set of variables obtained by each feature selection technique is used as input for the clustering algorithms used (Consensus Clustering, Self-Organizing Map and Agglomerative Clustering) to determine which combination of feature selection, clustering algorithm and number of clusters maximizes intercluster variability. Next, the mechanism called False Clustering Discovery Reduction is applied to obtain the minimum number of statistically differentiable populations after applying a control metric. This metric is based on a variance test to confirm that reducing the number of clusters does not generate significant losses in the heterogeneity obtained. This approach was applied to the Organ Procurement and Transplantation Network medical dataset (n = 11,332). The combination of Random Survival Forest and consensus clustering yielded the optimal result of 4 clusters starting from 8 initial ones. Finally, for each population, Kaplan-Meier survival curves are generated to predict the survival of new patients based on the predictions of the XGBoost classifier, with an overall multi-class AUC of 98.11%.