Discovering Diagnostic Gene Targets and Early Diagnosis of Acute GVHD Using Methods of Computational Intelligence over Gene Expression Data

Fiasché, Maurizio; Verma, Anju; Cuzzola, Maria; Iacopino, Pasquale; Kasabov, Nikola; Morabito, Francesco Carlo

doi:10.1007/978-3-642-04277-5_2

Cited by 8 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used weighted-weighted distance K-nearest neighbor (WWKNN) as the classification algorithm 15 given that the simple weighted distance K-nearest neighbor (WKNN) algorithm had returned better results than other classification techniques in previous experiments (an interesting comparison is reported by Fiasché's group). 11,19,20 Finally, a compact genetic algorithm 22 was used to optimize the learning function during the training process making framework performances useful in clinical applications.…”

Section: Personalized Modeling For Identification Of Target Genes In mentioning

confidence: 99%

A molecular and computational diagnostic approach identifies FOXP3, ICOS, CD52 and CASP1 as the most informative biomarkers in acute graft-versus-host disease

Cuzzola¹,

Fiasché²,

Iacopino

et al. 2012

Haematologica

View full text Add to dashboard Cite

The online version of this article has a Supplementary Appendix. BackgroundAcute graft-versus-host disease is a severe complication of allogeneic stem cell transplantation in which the functional immune cells of the donor recognize the recipient as foreign and mount an immunological attack. There is an urgent need for better diagnostic instruments for the assessment of acute graft-versus-host disease. In the present study, a novel bioinformatics framework was used to identify gene expression patterns associated with acute graft-versushost disease in patients undergoing allogeneic hematopoietic stem cell transplantation. Design and MethodsPeripheral blood cells were collected prospectively from patients who did develop acute graftversus-host disease (YES) and from those who did not (NO). Gene expression profiling was performed using a panel of 47 candidate genes potentially involved in alloreactive responses. The entire population of YES/NO acute graft-versus-host disease patients formed the experimental validation set. Personalized modeling based on a gene selection technique was applied to identify the most significant mRNA transcripts, which were then used to profile individual data samples for training and testing the classification/prediction framework. ResultsA leave-one-out cross-validation procedure was performed to investigate the robustness of the classification framework producing the following results: 100% on the training dataset and 97% on the testing dataset. According to our integrated methodology, transcripts for FOXP3, ICOS, CD52 and CASP1, genes involved in immune alloreactive responses and participating in immune cell interactions, were identified as the most informative biomarkers in allogeneic stem cell transplant recipients experiencing acute graft-versus-host disease. ConclusionsThis study demonstrates that the integrated methodology proposed is useful for the selection of valid gene targets for the diagnosis of acute graft-versus-host disease, producing satisfactory accuracy over independent clinical features of the allogeneic transplanted population.

show abstract

Section: Personalized Modeling For Identification Of Target Genes In mentioning

confidence: 99%

A molecular and computational diagnostic approach identifies FOXP3, ICOS, CD52 and CASP1 as the most informative biomarkers in acute graft-versus-host disease

Cuzzola¹,

Fiasché²,

Iacopino

et al. 2012

Haematologica

View full text Add to dashboard Cite

show abstract

“…As a filter approach, CFS was proposed by Hall [8] [9]. The rationale behind this algorithm is "a good feature subset is one that contains features highly correlated with the class, yet uncorrelated with each other."…”

Section: Gene Selection Methodsmentioning

confidence: 99%

“…As a clinical trial, tissue biopsies were performed to confirm the above diagnostic results. In conclusion, our models may prevent the need for an invasive procedure as already discussed in [1][5] [9] and it is possible to extract knowledge and rules after features selection task with wrappers and filters combined with a suitable classifier. This study demonstrated, for the first time, that the proposed incremental-adaptive-knowledge based learning procedure used for integrating the framework tool for diagnosis of aGvHD [1][5] [18] confirms a satisfactory 97% accuracy over independent test data set of HSCT population and return rules for individuating gene profiles for this complex disease.…”

Section: Biomedical Conclusion and Future Workmentioning

confidence: 99%

“…We have already published a review paper about classification and predictive strategies for validating a novel and not invasive method to confirm the diagnosis of aGvHD in HSCT patients at onset of clinical symptoms [1]. In previous works we have employed global and local models [9][18] and obtained good results using personalized modelling for selecting genes and other important informative biomarkers from clinical, biological and genetic data collected [1] [5]. In this paper we want to focus onto another aspect of aGvHD scenario analysis not still approached from anyone.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Incremental – Adaptive – Knowledge Based – Learning for Informative Rules Extraction in Classification Analysis of aGvHD

Fiasché

Verma²,

Cuzzola³

et al. 2011

Engineering Applications of Neural Networks

Self Cite

View full text Add to dashboard Cite

Abstract. Acute graft-versus-host disease (aGvHD) is a serious systemic complication of allogeneic hematopoietic stem cell transplantation (HSCT) that occurs when alloreactive donor-derived T cells recognize host-recipient antigens as foreign. The early events leading to GvHD seem to occur very soon, presumably within hours from the graft infusion. Therefore, when the first signs of aGvHD clinically manifest, the disease has been ongoing for several days at the cellular level, and the inflammatory cytokine cascade is fully activated. So, it comes as no surprise that to identify biomarker signatures for approaching this very complex task is a critical issue. In the past, we have already approached it through joint molecular and computational analyses of gene expression data proposing a computational framework for this disease. Notwithstanding this, there aren't in literature quantitative measurements able to identify patterns or rules from these biomarkers or from aGvHD data, thus this is the first work about the issue. In this paper first we have applied different feature selection techniques, combined with different classifiers to detect the aGvHD at onset of clinical signs, then we have focused on the aGvHD scenario and in the knowledge discovery issue of the classification techniques used in the computational framework.

show abstract

“…The section 3 we have described how a model can be built using clinical and genetic variables. For personalized modeling, different methods such as WWKNN and TWNFI were used and compared [27,28,29,30]. It has been found that TWNFI gives highest accuracy along with importance of each gene and variable by optimizing each variable and weight which can be used for better prediction and recommendations.…”

Section: Conclusion and Future Plansmentioning

confidence: 99%

Knowledge Discovery and Risk Prediction for Chronic Diseases: An Integrated Approach

Verma¹,

Fiasché

Cuzzola³

et al. 2011

Engineering Applications of Neural Networks

Self Cite

View full text Add to dashboard Cite

Abstract.A novel ontology based type 2 diabetes risk analysis system framework is described, which allows the creation of global knowledge representation (ontology) and personalized modeling for a decision support system. A computerized model focusing on organizing knowledge related to three chronic diseases and genes has been developed in an ontological representation that is able to identify interrelationships for the ontology-based personalized risk evaluation for chronic diseases. The personalized modeling is a process of model creation for a single person, based on their personal data and the information available in the ontology. A transductive neuro-fuzzy inference system with weighted data normalization is used to evaluate personalized risk for chronic disease. This approach aims to provide support for further discovery through the integration of the ontological representation to build an expert system in order to pinpoint genes of interest and relevant diet components.

show abstract

Discovering Diagnostic Gene Targets and Early Diagnosis of Acute GVHD Using Methods of Computational Intelligence over Gene Expression Data

Cited by 8 publications

References 15 publications

A molecular and computational diagnostic approach identifies FOXP3, ICOS, CD52 and CASP1 as the most informative biomarkers in acute graft-versus-host disease

A molecular and computational diagnostic approach identifies FOXP3, ICOS, CD52 and CASP1 as the most informative biomarkers in acute graft-versus-host disease

Incremental – Adaptive – Knowledge Based – Learning for Informative Rules Extraction in Classification Analysis of aGvHD

Knowledge Discovery and Risk Prediction for Chronic Diseases: An Integrated Approach

Contact Info

Product

Resources

About