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Machine learning can be broadly divided into supervised and unsupervised learning (Hastie et al. in The elements of statistical learning, Springer, New York, 2009). In supervised learning which is also known as classification, a classifier learns from some objects with known class labels and later assigns class labels to unknown objects based on acquired knowledge (Kotsiantis et al. in Proceedings...
In this paper, we introduce a connectivity-based protein complex finding method to find dense and sparse complexes along with core and periphery proteins. We named this method CNCM (Connectivity-based Network Clustering Method) as it uses clustering coefficient and the connectivity among nodes to find complexes. This method also ensures detection of protein complexes which are overlapping in nature...
Complex biological systems are often represented as networks and studied computationally. In protein–protein interaction networks, interactions give rise to certain compounds known as protein complexes. Identifying functional protein complexes is an emerging field of study in system biology. Several machine learning methods have been proposed so far to detect functionally enriched protein complexes...
Shifting and scaling correlations are correspondent of biological significance in gene expression data analysis. Recent works have mentioned about the significance of negative correlation as well. In this paper, we distinguish and define the negative form of shifting and scaling correlations as negatively shifted correlation and negatively scaled correlation, respectively. Another issue in gene expression...
Various feature selection techniques have been proposed in the field of machine learning. The filter approaches are typically faster while wrapper approaches are more reliable though computationally expensive. Feature selection techniques often strive to achieve performance similar to wrapper approaches employing various computational approaches. Feature selection techniques typically depend on ways...
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