Essential proteins as a vital part of maintaining the cells' life play an important role in the study of biology and drug design. With the generation of large amounts of biological data related to essential proteins, an increasing number of computational methods have been proposed. Different from the methods which adopt a single machine learning method or an ensemble machine learning method, this paper proposes a predicting framework named by XGBFEMF for identifying essential proteins, which includes a SUB-EXPAND-SHRINK method for constructing the composite features with original features and obtaining the better subset of features for essential protein prediction, and also includes a model fusion method for getting a more effective prediction model. We carry out experiments on Yeast data to assess the performance of the XGBFEMF with ROC analysis, accuracy analysis, and top analysis. Meanwhile, we set up experiments on E. coli data for the validation of performance. The test results show that the XGBFEMF framework can effectively improve many essential indicators. In addition, we analyze each step in the XGBFEMF framework; our results show that both each step of the SUB-EXPAND-SHRINK method as well as the step of multi-model fusion can improve prediction performance.
Literature on consumer choice has demonstrated that the inclusion of an inferior alternative choice (decoy) can increase interest in a target product or action. In two online studies, we tested the impact of decoys on the probability of previous non-intenders to have a screening test which could significantly lower their chances of dying of colorectal cancer. We find that the presence of a decoy increased the probability to choose screening at the target hospital (over no screening) from 39% to 54% and 37% to 59% depending on how many hospital attributes were communicated and how strongly the decoy was dominated by the target. We also show that the presence of the decoy was associated with lower levels of reported decisional complexity while not undermining information seeking and knowledge acquisition. These findings offer a ‘proof of principle’ that decoys have the potential to increase screening uptake without negatively influencing informed choice.
Background Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins. Results In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression. Conclusions We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.
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