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Titlebook: Algorithms for Computational Biology; 7th International Co Carlos Martín-Vide,Miguel A. Vega-Rodríguez,Travis Conference proceedings 2020 S

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發(fā)表于 2025-3-23 09:45:50 | 只看該作者
12#
發(fā)表于 2025-3-23 14:15:29 | 只看該作者
BESTox: A Convolutional Neural Network Regression Model Based on Binary-Encoded SMILES for Acute Oraiments are required to confirm the acute oral toxicity of chemical compounds. However, these methods are often restricted by availability of experimental facilities, long experimentation time, and high cost. In this paper, we propose a novel convolutional neural network regression model, named BESTo
13#
發(fā)表于 2025-3-23 20:54:50 | 只看該作者
Stratified Test Alleviates Batch Effects in Single-Cell Data, elegantly mitigates the problem. We also modified the common language effect size to supplement this test, further improving its utility. On both simulated and real patient data we show the ability of Van Elteren test to control for false positives and false negatives. The effect size also estimat
14#
發(fā)表于 2025-3-23 22:45:19 | 只看該作者
A Topological Data Analysis Approach on?Predicting Phenotypes from Gene Expression Dataividuals in the context of phenotype prediction. We observed that standard machine learning methods alone performed somewhat poorly on the disease phenotype prediction task; therefore we devised an approach augmenting machine learning with topological data analysis..We describe a framework for predi
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發(fā)表于 2025-3-24 04:00:48 | 只看該作者
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發(fā)表于 2025-3-24 09:47:52 | 只看該作者
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發(fā)表于 2025-3-24 11:38:23 | 只看該作者
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發(fā)表于 2025-3-24 18:04:15 | 只看該作者
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/153208.jpg
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發(fā)表于 2025-3-24 22:54:31 | 只看該作者
https://doi.org/10.1007/978-3-030-42266-0artificial intelligence; bioinformatics; communication; computational genomics; computer networks; correl
20#
發(fā)表于 2025-3-25 01:54:18 | 只看該作者
978-3-030-42265-3Springer Nature Switzerland AG 2020
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