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The identification of individual-cancer-related genes typically is an imbalanced classification issue. The number of known cancer-related genes is far less than the number of all unknown genes, which makes it very hard to detect novel predictions from such imbalanced training samples. A regular machine learning method can either only detect genes related to all cancers or add clinical knowledge to...
One task of heterogeneous face recognition is to match a near infrared (NIR) face image to a visible light (VIS) image. In practice, there are often a few pairwise NIR-VIS face images but it is easy to collect lots of VIS face images. Therefore, how to use these unpaired VIS images to improve the NIR-VIS recognition accuracy is an ongoing issue. This paper presents a deep TransfeR NIR-VIS heterogeneous...
In our recently developed denoising method, a linear combination of five features is used to adjust the peak intensities in tandem mass spectra. Although the method shows a promise, the coefficients (weights) of the linear combination were fixed and determined empirically. In this paper, we propose an adaptive approach for estimating these weights. The proposed approach: (1) calculates the score for...
We apply active learning and logistic regression to perform statistical analysis of Mascot peptide identification.Uncertainty sampling is used to select examples for labeling, and selected examples are labeled with reference data as the oracle. In each iteration of active learning, the penalized Newton-Raphson method is used to solve the logistic regression model. By testing the method on two datasets...
In tradition, grey System treats any random variations as a variation in the grey value within a certain range, and the random process is treated as a time-varying grey process within a certain range. Grey System successfully utilizes accumulated generation data instead of original data to build forecasting model, which makes raw data stochastic weak, or reduces noise influence to a certain extent...
Machine learning algorithms are widely used for quality assessment of tandem mass spectra based on a number of features. However, it is still unclear which features are most relevant to the quality of tandem mass spectra. In this paper, a sparse logistical regression method is proposed for selecting the most relevant features from those features found in the literature. To investigate the performance...
Gene profile classification is achieved by casting the classification problem as finding the sparse representation of testing samples with respect to training samples. The sparse representation is found by subspace pursuit, which is much more efficient than linear programming techniques. The new approach, with no need of model selection, however, still has the performance which can match the best...
In this paper, we present a fast and accurate lithographic hotspot detection flow with a novel MLK (Machine Learning Kernel), based on critical feature extraction and classification. In our flow, layout binary image patterns are decomposed/analyzed and critical lithographic hotspot related features are defined and employed for low noise MLK supervised training. Combining novel critical feature extraction...
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