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Akademický článek

Dysfunction of duplicated pair rice histone acetyltransferases causes segregation distortion and an interspecific reproductive barrier

Publikováno v: Nature Communications, Vol 15, Iss 1, Pp 1-18 (2024)

Abstract Postzygotic reproductive isolation, which results in the irreversible divergence of species, is commonly accompanied by hybrid sterility, necrosis/weakness, or lethality in the F1 or other offspring generations. Here we show that the loss of

Externí odkaz: https://doaj.org/article/d10a9a38894f448cac84756fe8dbfe1f

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Akademický článek

Optimization of rice panicle architecture by specifically suppressing ligand–receptor pairs

Autor: Tao Guo, Zi-Qi Lu, Yehui Xiong, Jun-Xiang Shan, Wang-Wei Ye, Nai-Qian Dong, Yi Kan, Yi-Bing Yang, Huai-Yu Zhao, Hong-Xiao Yu, Shuang-Qin Guo, Jie-Jie Lei, Ben Liao, Jijie Chai, Hong-Xuan Lin

Publikováno v: Nature Communications, Vol 14, Iss 1, Pp 1-15 (2023)

Rice panicle architecture dominates grain yield. In this study, the authors demonstrate a method for rationally overcoming the trade-off between yield traits and optimizing rice panicle architecture by specifically manipulating ligand–receptor pair

Externí odkaz: https://doaj.org/article/d696ba05c0bc404797d69e92005e19f3

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Akademický článek

A rice QTL GS3.1 regulates grain size through metabolic-flux distribution between flavonoid and lignin metabolons without affecting stress tolerance

Publikováno v: Communications Biology, Vol 4, Iss 1, Pp 1-14 (2021)

Zhang et al. report QTL GS3.1 as regulating rice grain size via metabolic flux allocation between two branches of phenylpropanoid metabolism. Their results identify the biosynthetic pathway for grain size regulation via a multidrug and toxic compound

Externí odkaz: https://doaj.org/article/e3a5af10a8df4285917246a8a6d0c40a

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Akademický článek

Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

Autor: Dejun Jiang, Zhenxing Wu, Chang-Yu Hsieh, Guangyong Chen, Ben Liao, Zhe Wang, Chao Shen, Dongsheng Cao, Jian Wu, Tingjun Hou

Publikováno v: Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-23 (2021)

Abstract Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In th

Externí odkaz: https://doaj.org/article/07cf64742f784799a82804abcc3c7356

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ChemistGA: A Chemical Synthesizable Accessible Molecular Generation Algorithm for Real-World Drug Discovery

Autor: Jike Wang, Xiaorui Wang, Huiyong Sun, Mingyang Wang, Yundian Zeng, Dejun Jiang, Zhenxing Wu, Zeyi Liu, Ben Liao, Xiaojun Yao, Chang-Yu Hsieh, Dongsheng Cao, Xi Chen, Tingjun Hou

Publikováno v: Journal of Medicinal Chemistry. 65:12482-12496

Many deep learning (DL)-based molecular generative models have been proposed to design novel molecules. These models may perform well on benchmarks, but they usually do not take real-world constraints into account, such as available training data set

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5e3ada260244760835a52606d07a801
https://doi.org/10.1021/acs.jmedchem.2c01179

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InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein–Ligand Interaction Predictions

Autor: Dejun Jiang, Chang-Yu Hsieh, Zhenxing Wu, Yu Kang, Jike Wang, Ercheng Wang, Ben Liao, Chao Shen, Lei Xu, Jian Wu, Dongsheng Cao, Tingjun Hou

Publikováno v: Journal of Medicinal Chemistry. 64:18209-18232

Accurate quantification of protein-ligand interactions remains a key challenge to structure-based drug design. However, traditional machine learning (ML)-based methods based on handcrafted descriptors, one-dimensional protein sequences, and/or two-di

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d2b00e698048e975e9066a046505794
https://doi.org/10.1021/acs.jmedchem.1c01830

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Mitigating Contradictions in Dialogue Based on Contrastive Learning

Autor: Weizhao Li, Junsheng Kong, Ben Liao, Yi Cai

Publikováno v: Findings of the Association for Computational Linguistics: ACL 2022.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e96d7867d9eeb04f9c98e07a26e1824b
https://doi.org/10.18653/v1/2022.findings-acl.219

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Fast Extraction of Word Embedding from Q-contexts

Autor: Junsheng Kong, Ben Liao, Jiezhong Qiu, Weizhao Li, Yi Cai, Shengyu Zhang, Chang-Yu Hsieh, Zeyi Liu

Publikováno v: CIKM

The notion of word embedding plays a fundamental role in natural language processing (NLP). However, pre-training word embedding for very large-scale vocabulary is computationally challenging for most existing methods. In this work, we show that with

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f4593e978cb97d879d9d6d3c8b120f3
https://doi.org/10.1145/3459637.3482343

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