PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 15208193

Evolutionary fuzzy modeling human diagnostic decisions.

Carlos Andrés Peña-Reyes
View original source ↗Export metadata ↓

Abstract

Fuzzy CoCo is a methodology, combining fuzzy logic and evolutionary computation, for constructing systems able to accurately predict the outcome of a human decision-making process, while providing an understandable explanation of the underlying reasoning. Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (accuracy) and linguistic representation (interpretability). However, fuzzy modeling--meaning the construction of fuzzy systems--is an arduous task, demanding the identification of many parameters. To solve it, we use evolutionary computation techniques (specifically cooperative coevolution), which are widely used to search for adequate solutions in complex spaces. We have successfully applied the algorithm to model the decision processes involved in two breast cancer diagnostic problems, the WBCD problem and the Catalonia mammography interpretation problem, obtaining systems both of high performance and high interpretability. For the Catalonia problem, an evolved system was embedded within a Web-based tool-called COBRA-for aiding radiologists in mammography interpretation.

Explore related subjects

Breast NeoplasmsComputational BiologyDiagnosis, Computer-AssistedFemaleFuzzy LogicHumansMammographyRisk AssessmentJournal ArticleReview

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Carlos Andrés Peña-Reyes. 2004. Evolutionary fuzzy modeling human diagnostic decisions.. https://doi.org/10.1196/annals.1310.017

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Hypernetwork-guided fusion with intra-class MixUp for breast cancer subtyping.

Accurate breast cancer subtyping guides treatment selection, yet histopathology captures morphology without molecular state, while genomic profiling captures molecular signatures without spatial context. Existing fusion methods rely on concatenation, or on attention applied only after each modality is encoded independently. This work identifies a scale-dependent asymmetry in the direction of cross-modal conditioning: the direction that performs best under limited samples is not the one that holds at scale, and the reversal is traced to the capacity of the modulation pathway rather than to the fusion principle. The comparison is carried out within a hypernetwork-guided framework in which an auxiliary network maps one modality to conditioning parameters that modulate the other's feature representation, shaping features at the parametric level rather than the decision stage; modulation is patient-specific rather than patch-specific. Both directions are instantiated-gene-to-image (HyperG2I) and image-to-gene (HyperI2G) - and trained under a label-aware MixUp strategy that interpolates within-class samples across both modalities, preserving the hard binary labels clinical decisions require. The framework is evaluated on two paired TCGA-BRCA cohorts-one limited-sample, one independently assembled at scale-under a single protocol spanning two whole-slide representations, multiple visual backbones, and both conditioning directions. On the limited-sample cohort, gene-to-image conditioning at its optimal augmentation setting exceeds early fusion and both unimodal baselines, giving the highest recall on the aggressive Basal/HER2 class of any configuration evaluated, and an ablation favours intra-class over inter-class mixing. At scale this ordering does not hold: image-to-gene conditioning sustains its performance whereas gene-to-image does not, recovering only partially under the full tissue bag and isolating the capacity of the modulation pathway as the binding constraint. Direction and capacity of cross-modal conditioning, rather than fusion depth alone, therefore govern how such frameworks scale.

Breast Neoplasms↗
PubMed2025

Metagenomic analyses reveal E. coli-derived siderophores as potential signatures for breast cancer.

BACKGROUND: Breast cancer remains a leading cause of cancer-related mortality in women. Recent evidence implicates the gut microbiome and metabolites in breast cancer pathogenesis. This study explores associations between gut microbial species, their predicted metabolites, and breast cancer to uncover potential mechanistic insights. METHODS: Comprehensive metagenomic analyses were conducted on the gut microbiome of pre- and postmenopausal breast cancer patients, where microbial species were profiled through AMPHORA2 and metabolites were predicted through antiSMASH. Multivariate association analysis was used to identify significant associations between specific microbial species, predicted metabolites, and breast cancer status. A custom ensemble machine learning classifier was developed to classify pre- and postmenopausal breast cancer cases and controls based on microbial and predicted metabolite features. Additionally, a synthetic microbiome dataset was generated through MIDASim to validate the reproducibility of the ML results. Using our results, we explored the underlying dynamics of identified taxa and metabolite in breast cancer through literature and statistical support. RESULTS: Our analysis identified 471 microbial species and predicted 40 key metabolites in the metagenomic data. Multivariate analysis identified significant positive associations (p-value&#x2009;<&#x2009;0.05) of E. coli, siderophore, and thiopeptide with breast cancer. The custom ensemble model achieved accuracy and AUC as high as 78% and 90%, respectively, in classifying pre- and postmenopausal cases and controls. The high-ranking features i.e., E. coli, siderophore, and thiopeptide were consistent with the results of the multivariate association analysis, thereby substantiating their biological significance. Using these findings, we propose a mechanistic model in which E. coli secretes siderophores under iron-limited conditions in breast cancer patients, for iron sequestration from the host, which can potentially promote angiogenesis and tumor progression. CONCLUSION: Our findings suggest that microbial iron acquisition mechanisms may play a critical role in breast cancer pathophysiology. Functional validation of these mechanisms is needed to assess therapeutic potential. This study highlights gut microbiota and their metabolites as promising targets for breast cancer research and intervention.

Breast Neoplasms↗
PubMed2025

Foundation model based multimodal transformer framework for survival analysis in HER2 stratified breast cancer.

Objective. To improve survival prediction for HER2-positive breast cancer by integrating histopathological, molecular, and clinical data using a multimodal transformer framework.Approach. We propose a multimodal transformer framework for breast cancer survival prediction using HER2 stratified (SurvMBC), a foundation model-enhanced architecture that fuses three data modalities: whole-slide images, clinical narratives, and molecular features. Tumor microenvironment features are extracted using a pathology language and image pre-training (PLIP), clinical narratives are processed with BioBERT, and miRNA expression plus DNA methylation data are embedded using Gen2Vec. These representations are integrated through a cross-modal transformer with attention mechanisms for survival prediction.Main results. The model was evaluated on 1,095 HER2-positive breast cancer patients from The Cancer Genome Atlas. SurvMBC achieved a concordance index (C-index) of 0.857 (95% CI: 0.834, 0.880), a low integrated Brier score, and a strong inverse negative binomial log-likelihood. Risk stratification based on model outputs significantly separated high- and low-risk groups (log-rankp< 0.01) and showed strong associations with tumor stage, grade, and hormone receptor status (allp< 0.05).Significance. SurvMBC demonstrates the effectiveness of multimodal fusion in addressing tumor heterogeneity and improving prognostic accuracy. The attention-based integration enables context-aware learning of survival-relevant features across modalities, supporting individualized risk stratification and risk-adaptive treatment planning for HER2 stratified breast cancer patients.

Breast Neoplasms↗