A neuro-fuzzy approach for functional genomics data interpretation and analysis

Neagu, Daniel; Palade, Vasile

doi:10.1007/s00521-003-0388-6

Cited by 15 publications

(9 citation statements)

References 17 publications

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“…We applied our algorithm to the Automated protein Function Prediction (AFP) problem, a challenging and central problem in computational biology [53,50]. In this setting, nodes represent proteins and connections their pairwise relationships deriving from different sources of information, including gene co-expression, genetic and physical interactions, protein ontologies and phenotype annotations.…”

Section: Methodsmentioning

confidence: 99%

Learning node labels with multi-category Hopfield networks

Frasca

Bassis

Valentini

2015

Neural Comput & Applic

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In several real-world node-label prediction problems on graphs, in fields ranging from computational biology to World-Wide-Web analysis, nodes can be partitioned into categories different from the classes to be predicted, on the basis of their characteristics or their common properties. Such partitions may provide further information about node classification that classical machine learning algorithms do not take into account. We introduce a novel family of parametric Hopfield networks (m-Category Hopfield Networks) and a novel algorithm (Hopfield Multi-Category -HoMCat), designed to appropriately exploit the presence of propertybased partitions of nodes into multiple categories. Moreover, the proposed model adopts a cost-sensitive learning strategy to prevent the remarkable decay in performance usually observed when instance labels are unbalanced, that is when one class of labels is highly under-represented than the other one. We validate the proposed model on both synthetic and real-world data, in the context of multi-species function prediction, where the classes to be predicted are the Gene Ontology terms and the categories the different species in the multi-species protein network. We carried out an intensive experimental validation, which on the one hand compares HoMCat with several state-of-the-art graph-based algorithms, and on the other hand reveals that exploiting meaningful prior partitions of input data can substantially improve classification performances.

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Section: Methodsmentioning

confidence: 99%

Learning node labels with multi-category Hopfield networks

Frasca

Bassis

Valentini

2015

Neural Comput & Applic

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“…One such tool utilized for this issue is the fuzzy logic [4], and the Neuro-fuzzy [5], that can fulfill the requirement for a DNA sequencing analysis procedure and give a deliberate and fair-minded approach to prefer this topic. More importantly that the Neuro-fuzzy approach is efficient to apply in bio system and in genomic [6], the Neuro-fuzzy can be used as a method to identify the changes of the statistics of selection if some "agent" is capable of predicting (and thus recognizing) [7], and in the functional analysis of gene expression data from microarray experiments [8].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Method Implemented In Analyzing of Organ System Using Intelligent Tools

Hameed¹,

Hamed²

2016

juhd

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The paper proposes an efficient approach applied in DNA base calling, which concerns efficiency and sensitivity. We utilized the Neuro-Fuzzy model in the analysis issues to determine the confidence value prediction in DNA base calling, that is solved by several attempts applied in the MATLAB tool, the model is implemented for the collected data for each base in the DNA sequencing. The model is designed by using the ANFIS tool, which contains three subsystems a main system. We obtain three features (peakness, height, and spacing) for each base from the three subsystems and in the main system use these three features as the input to predict the confidence value for each base in the DNA. This achieves a high accuracy in the obtained results with high-performance.

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“…Azuaje [2] applied a simplified fuzzy ARTMAP to identify normal and diffuse large B-cell lymphoma patients and to provide interpretation for the genome expression patterns. Neagu and Palade [3] have applied a neuro-fuzzy system for the functional analysis of gene expression data from microarray experiments. Chen et al [4] have applied neuro-fuzzy methods for the identification of critical genes in microarray experiments.…”

Section: Introductionmentioning

confidence: 99%