Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Ronghui You"'
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Homologous protein search is one of the most commonly used methods for protein annotation and analysis. Compared to structure search, detecting distant evolutionary relationships from sequences alone remains challenging. Here we propose PLMS
Externí odkaz:
https://doaj.org/article/153d17fae1214cbb9670f987b43ab0d1
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-14 (2024)
Abstract Contig binning plays a crucial role in metagenomic data analysis by grouping contigs from the same or closely related genomes. However, existing binning methods face challenges in practical applications due to the diversity of data types and
Externí odkaz:
https://doaj.org/article/fcc9b203a74f44adbdec007db0e67bca
Publikováno v:
Genome Biology, Vol 24, Iss 1, Pp 1-18 (2023)
Abstract Binning aims to recover microbial genomes from metagenomic data. For complex metagenomic communities, the available binning methods are far from satisfactory, which usually do not fully use different types of features and important biologica
Externí odkaz:
https://doaj.org/article/4a8b7be5e78f49cda3bf5a167e5c9924
Publikováno v:
International Journal of Molecular Sciences, Vol 24, Iss 24, p 17599 (2023)
Maintenance of proteome integrity is essential for cell function and survival in changing cellular and environmental conditions. The endoplasmic reticulum (ER) is the major site for the synthesis of secretory and membrane proteins. However, the accum
Externí odkaz:
https://doaj.org/article/683348387567469cac4287a66e33717a
Autor:
Naihui Zhou, Yuxiang Jiang, Timothy R. Bergquist, Alexandra J. Lee, Balint Z. Kacsoh, Alex W. Crocker, Kimberley A. Lewis, George Georghiou, Huy N. Nguyen, Md Nafiz Hamid, Larry Davis, Tunca Dogan, Volkan Atalay, Ahmet S. Rifaioglu, Alperen Dalkıran, Rengul Cetin Atalay, Chengxin Zhang, Rebecca L. Hurto, Peter L. Freddolino, Yang Zhang, Prajwal Bhat, Fran Supek, José M. Fernández, Branislava Gemovic, Vladimir R. Perovic, Radoslav S. Davidović, Neven Sumonja, Nevena Veljkovic, Ehsaneddin Asgari, Mohammad R.K. Mofrad, Giuseppe Profiti, Castrense Savojardo, Pier Luigi Martelli, Rita Casadio, Florian Boecker, Heiko Schoof, Indika Kahanda, Natalie Thurlby, Alice C. McHardy, Alexandre Renaux, Rabie Saidi, Julian Gough, Alex A. Freitas, Magdalena Antczak, Fabio Fabris, Mark N. Wass, Jie Hou, Jianlin Cheng, Zheng Wang, Alfonso E. Romero, Alberto Paccanaro, Haixuan Yang, Tatyana Goldberg, Chenguang Zhao, Liisa Holm, Petri Törönen, Alan J. Medlar, Elaine Zosa, Itamar Borukhov, Ilya Novikov, Angela Wilkins, Olivier Lichtarge, Po-Han Chi, Wei-Cheng Tseng, Michal Linial, Peter W. Rose, Christophe Dessimoz, Vedrana Vidulin, Saso Dzeroski, Ian Sillitoe, Sayoni Das, Jonathan Gill Lees, David T. Jones, Cen Wan, Domenico Cozzetto, Rui Fa, Mateo Torres, Alex Warwick Vesztrocy, Jose Manuel Rodriguez, Michael L. Tress, Marco Frasca, Marco Notaro, Giuliano Grossi, Alessandro Petrini, Matteo Re, Giorgio Valentini, Marco Mesiti, Daniel B. Roche, Jonas Reeb, David W. Ritchie, Sabeur Aridhi, Seyed Ziaeddin Alborzi, Marie-Dominique Devignes, Da Chen Emily Koo, Richard Bonneau, Vladimir Gligorijević, Meet Barot, Hai Fang, Stefano Toppo, Enrico Lavezzo, Marco Falda, Michele Berselli, Silvio C.E. Tosatto, Marco Carraro, Damiano Piovesan, Hafeez Ur Rehman, Qizhong Mao, Shanshan Zhang, Slobodan Vucetic, Gage S. Black, Dane Jo, Erica Suh, Jonathan B. Dayton, Dallas J. Larsen, Ashton R. Omdahl, Liam J. McGuffin, Danielle A. Brackenridge, Patricia C. Babbitt, Jeffrey M. Yunes, Paolo Fontana, Feng Zhang, Shanfeng Zhu, Ronghui You, Zihan Zhang, Suyang Dai, Shuwei Yao, Weidong Tian, Renzhi Cao, Caleb Chandler, Miguel Amezola, Devon Johnson, Jia-Ming Chang, Wen-Hung Liao, Yi-Wei Liu, Stefano Pascarelli, Yotam Frank, Robert Hoehndorf, Maxat Kulmanov, Imane Boudellioua, Gianfranco Politano, Stefano Di Carlo, Alfredo Benso, Kai Hakala, Filip Ginter, Farrokh Mehryary, Suwisa Kaewphan, Jari Björne, Hans Moen, Martti E.E. Tolvanen, Tapio Salakoski, Daisuke Kihara, Aashish Jain, Tomislav Šmuc, Adrian Altenhoff, Asa Ben-Hur, Burkhard Rost, Steven E. Brenner, Christine A. Orengo, Constance J. Jeffery, Giovanni Bosco, Deborah A. Hogan, Maria J. Martin, Claire O’Donovan, Sean D. Mooney, Casey S. Greene, Predrag Radivojac, Iddo Friedberg
Publikováno v:
Genome Biology, Vol 20, Iss 1, Pp 1-23 (2019)
Abstract Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third C
Externí odkaz:
https://doaj.org/article/4c2b017b155b41ceab8d6d078186cafa
Homologous protein search is one of the most commonly used methods for protein annotation and analysis. Compared to structure search, detecting distant evolutionary relationships from sequences alone remains challenging. Here we propose PLMSearch (Pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a5fb5d1b4bb6babfb9b6cba28714e7f4
https://doi.org/10.1101/2023.04.03.535375
https://doi.org/10.1101/2023.04.03.535375
As one of the state-of-the-art automated function prediction (AFP) methods, NetGO 2.0 integrates multi-source information to improve the performance. However, it mainly utilizes the proteins with experimentally supported functional annotations withou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ae9ce60ca507b17663a7bf5aa7d4670
https://doi.org/10.1101/2022.12.05.519073
https://doi.org/10.1101/2022.12.05.519073
Publikováno v:
Bioinformatics
Motivation Automated function prediction (AFP) of proteins is a large-scale multi-label classification problem. Two limitations of most network-based methods for AFP are (i) a single model must be trained for each species and (ii) protein sequence in
Motivation Computationally predicting major histocompatibility complex (MHC)-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to achieve sati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b817ede38b8c32cc1e9727b3f0545839
https://aaltodoc.aalto.fi/handle/123456789/115791
https://aaltodoc.aalto.fi/handle/123456789/115791
Publikováno v:
Nucleic Acids Research
With the explosive growth of protein sequences, large-scale automated protein function prediction (AFP) is becoming challenging. A protein is usually associated with dozens of gene ontology (GO) terms. Therefore, AFP is regarded as a problem of large