Zobrazeno 1 - 10
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pro vyhledávání: '"Dna Sequence"'
Autor:
Liang, Wang
There are already many DNA large language models, but most of them still follow traditional uses, such as extracting sequence features for classification tasks. More innovative applications of large language models, such as prompt engineering, RAG, a
Externí odkaz:
http://arxiv.org/abs/2410.16917
Autor:
Young, Stephanie, Gilles, Jerome
Publikováno v:
Journal of Theoretical Biology, Vol.596, 111972, 2025
A 3D chaos game is shown to be a useful way for encoding DNA sequences. Since matching subsequences in DNA converge in space in 3D chaos game encoding, a DNA sequence's 3D chaos game representation can be used to compare DNA sequences without prior a
Externí odkaz:
http://arxiv.org/abs/2411.05266
We present an assignment for a full Parallel Computing course. Since 2017/2018, we have proposed a different problem each academic year to illustrate various methodologies for approaching the same computational problem using different parallel progra
Externí odkaz:
http://arxiv.org/abs/2409.06075
DNA sequences encode vital genetic and biological information, yet these unfixed-length sequences cannot serve as the input of common data mining algorithms. Hence, various representation schemes have been developed to transform DNA sequences into fi
Externí odkaz:
http://arxiv.org/abs/2407.12051
Autor:
Shams, Mahmoud Y.1 mahmoud.yasin@ai.kfs.edu.eg, Farag, Romany M.2 blackfox2100@gmail.com, Aldawody, Dalia A.2 drsalama44@gmail.com, Khalid, Huda E.3 dr.hudaismael@uotelafer.edu.iq, Essa, Ahmed K.4 ahmed.k.essa@uotelafer.edu.iq, El-Bakry, Hazem M.5 elbakry@mans.edu.eg, Salama, A. A.2 ahmed_salama_2000@sci.psu.edu.eg
Publikováno v:
Neutrosophic Systems & Applications. 2024, Vol. 22, p13-30. 18p.
Autor:
Dip, Sajib Acharjee, Shuvo, Uddip Acharjee, Chau, Tran, Song, Haoqiu, Choi, Petra, Wang, Xuan, Zhang, Liqing
Pathogen identification is pivotal in diagnosing, treating, and preventing diseases, crucial for controlling infections and safeguarding public health. Traditional alignment-based methods, though widely used, are computationally intense and reliant o
Externí odkaz:
http://arxiv.org/abs/2406.13133
Self-supervised pretraining (SSP) has been recognized as a method to enhance prediction accuracy in various downstream tasks. However, its efficacy for DNA sequences remains somewhat constrained. This limitation stems primarily from the fact that mos
Externí odkaz:
http://arxiv.org/abs/2405.08538
Autor:
Li, Zehui, Ni, Yuhao, Beardall, William A V, Xia, Guoxuan, Das, Akashaditya, Stan, Guy-Bart, Zhao, Yiren
This paper introduces a novel framework for DNA sequence generation, comprising two key components: DiscDiff, a Latent Diffusion Model (LDM) tailored for generating discrete DNA sequences, and Absorb-Escape, a post-training algorithm designed to refi
Externí odkaz:
http://arxiv.org/abs/2402.06079
Autor:
Ozan, Şükrü
Recent studies in DNA sequence classification have leveraged sophisticated machine learning techniques, achieving notable accuracy in categorizing complex genomic data. Among these, methods such as k-mer counting have proven effective in distinguishi
Externí odkaz:
http://arxiv.org/abs/2401.14025