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Several discussions praise the publication as a 'favourite paper of 2017,' emphasizing its innovative approach to protein structure determination using metagenome data. The tone ranges from simple endorsements to mentioning potential applications, showing a balanced mixture of appreciation and curiosity.
Most discussions express positive or supportive views towards the publication, reflecting general agreement with its significance.
The posts show moderate curiosity, with some highlighting the importance of the research and its innovative methods.
Participants are engaging by referencing specific aspects like methods and implications, but the depth is somewhat superficial.
There is recognition that the work could be influential, especially in fields like protein research and therapeutics, indicating a moderate impact.
Favourite paper of 2017 "Protein structure determination using metagenome sequence data" by Ovchinnikov et al. (1/4) https://t.co/lYThcWxeOZ https://t.co/7jVGXHkopc
Favourite paper of 2017 "Protein structure determination using metagenome sequence data" by Ovchinnikov et al. (1/4) www.science.org/doi/10.1126/...
@eugenevalkov In the past, we even called it "Protein structure determination" cus the signal you get from MSA/coevolution (even w/o any supervised learning) is as good as you would from any other experimental source. With AF/RF we just made this more efficient (2/2) https://t.co/z8ZLWj47Cm
@biochem_fan 下記とか？！ https://t.co/bNc0j7EY1v https://t.co/SSZvxWYHT8 https://t.co/Vo7ug4oJXV 識者の @Ag_smith さん、どうでしょうか。
@sjurgis @gordonwells @rivatez @medialab @UWproteindesign huh - weird. Articles are: Massively parallel de novo protein design for targeted therapeutics And: Protein Structure Determination using Metagenome sequence data
RT @m_sekijima: @biochem_fan 下記とか？！ https://t.co/bNc0j7EY1v https://t.co/SSZvxWYHT8 https://t.co/Vo7ug4oJXV 識者の @Ag_smith さん、どうでしょうか。
ColabFold provides a fast and efficient way to predict protein structures using AlphaFold2 and RoseTTAFold. This video explores how ColabFold can execute predictions up to 60 times faster than traditional methods…
RNA-sequencing (RNA-seq) is a versatile tool for studying gene expression and requires a tailored analysis pipeline. This video explores essential steps in RNA-seq data analysis, addressing challenges and discussing…
RNA-sequencing (RNA-seq) is a versatile tool used for studying gene expression and requires a tailored analysis pipeline for different applications. This review covers essential steps in RNA-seq data analysis…
RT @jgreener64: Favourite paper of 2017 "Protein structure determination using metagenome sequence data" by Ovchinnikov et al. (1/4) http…
Favourite paper of 2017 "Protein structure determination using metagenome sequence data" by Ovchinnikov et al. (1/4) www.science.org/doi/10.1126/...
Favourite paper of 2017 "Protein structure determination using metagenome sequence data" by Ovchinnikov et al. (1/4) https://t.co/lYThcWxeOZ https://t.co/7jVGXHkopc
RT @m_sekijima: @biochem_fan 下記とか？！ https://t.co/bNc0j7EY1v https://t.co/SSZvxWYHT8 https://t.co/Vo7ug4oJXV 識者の @Ag_smith さん、どうでしょうか。
RT @m_sekijima: @biochem_fan 下記とか？！ https://t.co/bNc0j7EY1v https://t.co/SSZvxWYHT8 https://t.co/Vo7ug4oJXV 識者の @Ag_smith さん、どうでしょうか。
@biochem_fan 下記とか？！ https://t.co/bNc0j7EY1v https://t.co/SSZvxWYHT8 https://t.co/Vo7ug4oJXV 識者の @Ag_smith さん、どうでしょうか。
RT @sokrypton: @eugenevalkov In the past, we even called it "Protein structure determination" cus the signal you get from MSA/coevolution (…
@eugenevalkov In the past, we even called it "Protein structure determination" cus the signal you get from MSA/coevolution (even w/o any supervised learning) is as good as you would from any other experimental source. With AF/RF we just made this more efficient (2/2) https://t.co/z8ZLWj47Cm
@sjurgis @gordonwells @rivatez @medialab @UWproteindesign huh - weird. Articles are: Massively parallel de novo protein design for targeted therapeutics And: Protein Structure Determination using Metagenome sequence data