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Titlebook: Bioinformatics Research and Applications; 15th International S Zhipeng Cai,Pavel Skums,Min Li Conference proceedings 2019 Springer Nature S

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發(fā)表于 2025-3-27 00:39:59 | 只看該作者
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發(fā)表于 2025-3-27 04:18:26 | 只看該作者
https://doi.org/10.1007/978-90-313-6360-5 to reveal the dependency relationship between genotypes and phenotypes. Furthermore, a genomic data sanitization method is proposed to protect against optimal inference attacks launched by powerful attackers.
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發(fā)表于 2025-3-27 13:57:34 | 只看該作者
Sorting by Reversals, Transpositions, and Indels on Both Gene Order and Intergenic Sizesom intergenic regions of the genome, respectively. We study problems considering both gene order and intergenic regions size. We investigate the reversal distance between two genomes in two scenarios: with and without non-conservative events. For both problems, we show that they belong to NP-hard pr
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發(fā)表于 2025-3-27 18:54:09 | 只看該作者
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發(fā)表于 2025-3-28 00:30:15 | 只看該作者
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發(fā)表于 2025-3-28 03:16:40 | 只看該作者
Gene- and Pathway-Based Deep Neural Network for Multi-omics Data Integration to Predict Cancer Surviet captures nonlinear effects of multi-omics data to survival outcomes via a neural network framework, while allowing one to biologically interpret the model. In the extensive experiments with multi-omics data of Gliblastoma multiforme (GBM) patients, MiNet outperformed the current cutting-edge meth
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發(fā)表于 2025-3-28 07:06:24 | 只看該作者
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發(fā)表于 2025-3-28 13:55:41 | 只看該作者
Deep Learning and Random Forest-Based Augmentation of sRNA Expression Profiles accuracy for tissue groups is 98% (DL), for tissues - 96.5% (DL), and for sex - 77% (DL). The “one dataset out” average accuracy for tissue group prediction is 83% (DL) and 59% (RF). On average, DL provides better results as compared to RF, and considerably improves classification performance for ‘
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