{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:13:10Z","timestamp":1753884790982,"version":"3.41.2"},"reference-count":34,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42007289"],"award-info":[{"award-number":["42007289"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundmental Research Funds for the Central Universities","award":["53200759777"],"award-info":[{"award-number":["53200759777"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:p> Nucleosome localization is a dynamic process and consists of nucleosome dynamic intervals (NDIs). We preprocessed nucleosome sequence data as time series data (TSD) and developed a long short-term memory network (LSTM) model for training time series data (TSD; LSTM-TSD model) using iterative training and feature learning that predicts NDIs with high accuracy. Sn, Sp, Acc, and MCC of the obtained LSTM model is 91.88%, 92.72%, 92.30%, and 84.61%, respectively. LSTM model could precisely predict the NDIs of yeast 16 chromosome. The NDIs contain 90.29% of nucleosome core DNA and 91.20% of nucleosome central sites, indicating that NDIs have high confidence. We found that the binding sites of transcriptional proteins and other proteins are outside NDIs, not in NDIs. These results are important for analysis of nucleosome localization and gene transcriptional regulation. <\/jats:p>","DOI":"10.1142\/s0219720022500093","type":"journal-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T16:28:59Z","timestamp":1651163339000},"source":"Crossref","is-referenced-by-count":2,"title":["Prediction of nucleosome dynamic interval based on long\u2013short-term memory network (LSTM)"],"prefix":"10.1142","volume":"20","author":[{"given":"Jianli","family":"Liu","sequence":"first","affiliation":[{"name":"School of Water Resource and Environment Engineering, China University of Geosciences (Beijing), Beijing 100083, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deliang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Beijing Zhongdianyida Technology Co., Ltd, Beijing 100190, P. R. 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