Results 91 to 100 of about 392,357 (290)
Dimensionality reduction of quality of life indicators
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2012 Selecting indicators for assessing the quality of life at the regional level is not unambigous. Currently, there are no precisely defined indicators that would give comprehensive information about the quality of life on a local level.
Andrea Jindrová, Julie Poláčková
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Molecular Oncology, EarlyView.The cancer problem is increasing globally with projections up to the year 2050 showing unfavourable outcomes in terms of incidence and cancer‐related deaths. The main challenges are prevention, improved therapeutics resulting in increased cure rates and enhanced health‐related quality of life.
Ulrik Ringborg +43 more
wiley +1 more source
Reduction algorithm based on supervised discriminant projection for network security data
Tongxin xuebao, 2021 In response to the problem that for dimensionality reduction, traditional manifold learning algorithm did not consider the raw data category information, and the degree of clustering was generally at a low level, a manifold learning dimensionality ...
Fangfang GUO +3 more
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Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning [PDF]
, 2019 Takanori Fujiwara +2 more
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Molecular Oncology, EarlyView.Methods to improve antibody–drug conjugate (ADC) treatment durability in cancer therapy are needed. We utilized ADCs and immune‐stimulating antibody conjugates (ISACs), which are made from two non‐competitive antibodies, to enhance the entry of toxic payloads into cancer cells and deliver immunostimulatory agents into immune cells.
Tiexin Wang +3 more
wiley +1 more source
Tumor mutational burden as a determinant of metastatic dissemination patterns
Molecular Oncology, EarlyView.This study performed a comprehensive analysis of genomic data to elucidate whether metastasis in certain organs share genetic characteristics regardless of cancer type. No robust mutational patterns were identified across different metastatic locations and cancer types.
Eduardo Candeal +4 more
wiley +1 more source
IEEE AccessThis paper presents an extensive empirical study on the integration of dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models.
Mahsun Altin, Altan Cakir
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Cascade Support Vector Machines with Dimensionality Reduction
Applied Computational Intelligence and Soft Computing, 2015 Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing.
Oliver Kramer
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Molecular Oncology, EarlyView.To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley +1 more source
Multiple Kernel Spectral Regression for Dimensionality Reduction
Journal of Applied Mathematics, 2013 Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples.
Bing Liu, Shixiong Xia, Yong Zhou
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