Patterns

杂志介绍

Patterns杂志介绍

《Patterns》是一本以English为主的开放获取国际优秀期刊，中文名称模式，本刊主要出版、报道领域的研究动态以及在该领域取得的各方面的经验和科研成果，介绍该领域有关本专业的最新进展，探讨行业发展的思路和方法，以促进学术信息交流，提高行业发展。该刊已被国际权威数据库SCIE收录，为该领域相关学科的发展起到了良好的推动作用，也得到了本专业人员的广泛认可。该刊最新影响因子为6.7，最新CiteScore 指数为10.6。

本刊近期中国学者发表的论文主要有：

• Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships

  Author: Wang, Yi; Sun, Zijun; He, Qiushun; Li, Jiwei; Ni, Ming; Yang, Meng

• Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations

  Author: Shen, Wan Xiang; Liang, Shu Ran; Jiang, Yu Yang; Chen, Yu Zong

• Continuous diagnosis and prognosis by controlling the update process of deep neural networks

  Author: Sun, Chenxi; Li, Hongyan; Song, Moxian; Cai, Derun; Zhang, Baofeng; Hong, Shenda

• TriNet: A tri-fusion neural network for the prediction of anticancer and antimicrobial peptides

  Author: Zhou, Wanyun; Liu, Yufei; Li, Yingxin; Kong, Siqi; Wang, Weilin; Ding, Boyun; Han, Jiyun; Mou, Chaozhou; Gao, Xin; Liu, Juntao

英文介绍

Patterns杂志英文介绍

Patterns is a high-quality open access journal published by Cell Press, which publishes groundbreaking original research in various fields of data science. We are committed to sharing data science solutions for cross disciplinary problems. Patterns cover a wide global audience, including computer scientists, researchers in data intensive fields, data administrators, and policy makers. We adhere to the principle of fairness, ensuring that the data, software, workflows, algorithms, and other research results we publish are searchable, accessible, interoperable, and reusable. Patterns are the home of data scientists and researchers in both academic and industrial data intensive fields. This journal shares data science solutions from various disciplines, including computational science, physical science, life sciences, social sciences, and humanities.