Fig 1 - uploaded by Ibtissam Brahmi
Content may be subject to copyright.
Similar publications
![FIGURE 5: (a) Output neuron map for the SE-DBGSOM [16]. (b) Output...](publication/339545730/figure/fig4/AS:863353786343430@1582850991942/a-Output-neuron-map-for-the-SE-DBGSOM-16-b-Output-neuron-map-for-the-second-layer_Q320.jpg)
![Comparison between SE-DBGHSOM (without CDF) and Naive Bayes [23]](publication/339545730/figure/tbl1/AS:863353786359810@1582850991958/Comparison-between-SE-DBGHSOM-without-CDF-and-Naive-Bayes-23_Q320.jpg)
- [...]
A new evaluation mechanism was proposed to enhance the representation of data topology in the directed batch growth hierarchical self-organizing mapping. In the proposed mechanism, the growth threshold and the correlation worked in a case-sensitive manner through the statistic calculation of the input data. Since the proposed model enabled a more t...
Citations
... This approach extends the traditional SOM framework to operate across multiple hierarchical levels, creating a structured and layered learning model similar to Boltzmann machine in deep belief networks [24]. The authors emphasize that each level of the multi-level SOM contributes to a layered learning process, with topology preservation and efficient learning algorithms, as detailed by Brahmi et al. in [25]. This hierarchical organization enhances the capacity of the model to extract intricate relationships within complex convoluted biological signals, contributing to a more refined patterns in the data. ...
The development of disease detection with progression modelling due to long-term exposure to toxicity is complex. This leads to unwrapping the underlying intricate molecular network of toxicity and is thus a crucial challenge for the researchers. Therefore, identifying a set of biomarkers to predict the risk of exposure is vital. Thus, this article aims to provide a holistic machine learning-based solution over various time-varying ‘omic data to understand and explore the factors involved in the development of diseases. To address this issue, a flexible non-negative matrix factorization based multi-level self organizing map (FNMF-MLSOM) is developed. The proposed algorithm utilizes two open-source time series datasets: Type-2 diabetes mellitus and Huntington disease, namely. The flexible non-negative matrix factorization based self organization model introduced in this article provides a negative value acceptance constraint as well as the clustering on the basis matrix to keep the biological meaning of the data intact. Since microarray data have rich information, we applied the proposed method to obtain the progression-specific convoluted biomarker for precise feature extraction. Further, to validate the differentially expressed biomarkers, the proposed method is applied to the test samples to verify the mathematical validity as well as the biological significance of the biomarkers.
