ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation
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چکیده
منابع مشابه
In silico prediction of anticancer peptides by TRAINER tool
Cancer is one of the causes of death in the world. Several treatment methods exist against cancer cells such as radiotherapy and chemotherapy. Since traditional methods have side effects on normal cells and are expensive, identification and developing a new method to cancer therapy is very important. Antimicrobial peptides, present in a wide variety of organisms, such as plants, amphibians and ...
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cancer is one of the causes of death in the world. several treatment methods exist against cancer cells such as radiotherapy and chemotherapy. since traditional methods have side effects on normal cells and are expensive, identification and developing a new method to cancer therapy is very important. antimicrobial peptides, present in a wide variety of organisms, such as plants, amphibians and ...
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A theoretical comparison of the data augmentation, marginal augmentation and PX-DA algorithms
The data augmentation (DA) algorithm is a widely used Markov chain Monte Carlo (MCMC) algorithm that is based on a Markov transition density of the form p(x|x′) = ∫ Y fX|Y (x|y)fY |X(y|x) dy, where fX|Y and fY |X are conditional densities. The PX-DA and marginal augmentation algorithms (Liu and Wu, 1999; Meng and van Dyk, 1999) are alternatives to DA that often converge much faster and are only...
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ژورنال
عنوان ژورنال: Frontiers in Genetics
سال: 2021
ISSN: 1664-8021
DOI: 10.3389/fgene.2021.698477