High dimensional biomedical datasets contain thousands of features which can be used in molecular diagnosis of disease, however, such datasets contain many irrelevant or weak correlation features which influence the predictive accuracy of diagnosis. Without a feature selection algorithm, it is difficult for the existing classification techniques to accurately identify patterns in the features. The purpose of feature selection is to not only identify a feature subset from an original set of features [without reducing the predictive accuracy of classification algorithm] but also reduce the computation overhead in data mining. In this paper, we present our improved shuffled frog leaping algorithm which introduces a chaos memory weight factor, an absolute balance group strategy and an adaptive transfer factor. Our proposed approach explores the space of possible subsets to obtain the set of features that maximizes the predictive accuracy and minimizes irrelevant features in high-dimensional biomedical data. To evaluate the effectiveness of our proposed method we have employed the K-nearest neighbor method with a comparative analysis in which we compare our proposed approach with genetic algorithms, particle swarm optimization, and the shuffled frog leaping algorithm. Experimental results show that our improved algorithm achieves improvements in the identification of relevant subsets and in classification accuracy.
Emotion is an important indicator of depressive conditions. Emotion recognition based on physiological signals such as electroencephalogram (EEG) and functional nearinfrared spectroscopy (fNIRS) has gained significant attraction in healthcare domain research. Sharing of physiological signal data related to emotional response between different healthcare systems has the potential to benefit both laboratory-based healthcare research and 'real-world' clinical practice. However, management and distribution of the data presents significant challenges; addressing these challenges requires advanced tools for data representation, mining and integration. In this paper we propose such a tool which contains an ontology model called EmotionO+ and rules set based on EEG, which is obtained by random forest algorithm to predict emotional state. It presents not only an effective method to enable semantic representation of the EEG and fNIRS data, but also an emotion knowledge mining tool. Results using EEG data in the eNTERFACE'06 dataset show an accuracy for our proposed model of 99.11% as compared to 97.8% for competing methods using the C4.5 algorithm. The experimental results demonstrate that the posited approach is potentially usable for early stage prediction and intervention for depressive disorders.
Early diagnosis of cancer is of paramount significance for the therapeutic intervention of cancers. Although the detection of circulating cell-free DNA (cfDNA) has emerged as a promising, minimally invasive approach for early cancer diagnosis, there is an urgent need to develop a highly sensitive and rapid method to precisely identify plasma cfDNA from clinical samples. Herein, we report a robust fluorescent “turn-on” clutch probe based on non-emissive QDs-Ru complexes to rapidly recognize EGFR gene mutation in plasma cfDNA from lung cancer patients. In this system, the initially quenched emission of QDs is recovered while the red emission of Ru(II) complexes is switched on. This is because the Ru(II) complexes can specifically intercalate into the double-stranded DNA (dsDNA) to form Ru-dsDNA complexes and simultaneously liberate free QDs from the QDs-Ru complexes, which leads to the occurrence of an overlaid red fluorescence. In short, the fluorescent “turn-on” clutch probe offers a specific, rapid, and sensitive paradigm for the recognition of plasma cfDNA biomarkers from clinical samples, providing a convenient and low-cost approach for the early diagnosis of cancer and other gene-mutated diseases.
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