Synopsis of Social media discussions
The groups emphasize the innovative nature of the autoencoder, with phrases like 'new AI model' and 'robust prediction,' reflecting excitement and perceived high impact. Words like 'accurately predict' and mentions of 'personalized clinical drug response' demonstrate interest, while references to 'Nature Machine Intelligence' and remarks on 'methodological advancements' show active engagement and deeper understanding.
Agreement
Moderate agreementMost discussions acknowledge and support the significance and potential of the new autoencoder model for predicting personalized drug responses.
Interest
High level of interestPosts demonstrate high curiosity and enthusiasm, often mentioning innovative aspects of the model and its applications.
Engagement
Moderate level of engagementThe discussions include references to methodological details and implications, indicating active engagement, though some posts are more superficial.
Impact
High level of impactSeveral posts highlight the transformative impact of the research, such as improving personalized medicine and drug development, showing high perceived significance.
Social Mentions
YouTube
3 Videos
14 Posts
Blogs
3 Articles
News
26 Articles
Metrics
Video Views
24
Total Likes
20
Extended Reach
200,116
Social Features
46
Timeline: Posts about article
Top Social Media Posts
Reiner
A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening
https://t.co/nzqcuOwAjc
A new AI model can accurately predict human response to novel drug compounds
https://t.co/KyVUN9ojoO
@_atanas_
view full post
Lifeboat Foundation
A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening https://t.co/gTCJQkFufy
view full post
Machine Learning in Chemistry
A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening #machinelearning https://t.co/yxCFqQaxyT
view full post
Ordo Fraterna Fibonacci
A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening | Nature Machine Intelligence https://t.co/iM4zbPjXJT
view full post
Catarina Cunha
A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening
with Lei Xie and Kyle Cool https://t.co/BPIriPXdEG
view full post
Posts referencing the article
Advancing Personalized Medicine with the CODE-AE Autoencoder Model
In this episode, Dr Xie discusses developing the CODE-AE autoencoder, which enhances prediction accuracy of individual drug responses by extracting relevant biological signals amid data scarcity and variability, aiding personalized cancer treatment.
January 5, 2023
12 views
Deconfounding Autoencoder for Accurate Personalized Drug Response Prediction
This video explains how a new autoencoder model predicts patient-specific drug responses more accurately by addressing data variability and heterogeneity, enhancing personalized cancer treatment.
November 2, 2023
9 views
Deconfounding Autoencoder for Personalized Drug Response Prediction in Cancer Treatments
This video explains a new machine learning model called CODE-AE, which enhances the prediction of individual patient drug responses by addressing data variability and extracting relevant biological signals, supporting personalized cancer treatment strategies.
September 14, 2023
3 views
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A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening with Lei Xie and Kyle Cool https://t.co/w66l2lzirE
view full postDecember 8, 2022
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Catarina Cunha
@clemoscatarina (Twitter)A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening with Lei Xie and Kyle Cool https://t.co/BPIriPXdEG
view full postDecember 8, 2022
1
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Lifeboat Foundation
@LifeboatHQ (Twitter)A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening https://t.co/gTCJQkFufy
view full postOctober 30, 2022
2
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Mehmet Arif Mirasoğlu
@Mehmet_1326 (Twitter)RT @erlesen: A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound…
view full postOctober 23, 2022
7
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International Natural Product Sciences Taskforce
@_INPST (Twitter)RT @erlesen: A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound…
view full postOctober 20, 2022
7
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Science Communication
@ScienceCommuni2 (Twitter)RT @erlesen: A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound…
view full postOctober 20, 2022
7
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Dibungi T. Kalenda
@dibungikalend (Twitter)RT @erlesen: A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound…
view full postOctober 20, 2022
7
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Atanas G. Atanasov
@_atanas_ (Twitter)RT @erlesen: A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound…
view full postOctober 20, 2022
7
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Machine Learning in Chemistry
@ML_Chem (Twitter)A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening #machinelearning https://t.co/yxCFqQaxyT
view full postOctober 19, 2022
1
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Reiner
@erlesen (Twitter)RT @erlesen: A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound…
view full postOctober 18, 2022
7
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sic
@kimvie (Twitter)RT @erlesen: A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound…
view full postOctober 18, 2022
7
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Reiner
@erlesen (Twitter)A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening https://t.co/nzqcuOwAjc A new AI model can accurately predict human response to novel drug compounds https://t.co/KyVUN9ojoO @_atanas_
view full postOctober 18, 2022
14
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Ordo Fraterna Fibonacci
@OrdoFibonacci (Twitter)A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening | Nature Machine Intelligence https://t.co/iM4zbPjXJT
view full postOctober 17, 2022
1
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Reluctant Quant
@DrMattCrowson (Twitter)RT A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening https://t.co/qyEz52Yw89 https://t.co/amcQMC1YWr
view full postOctober 17, 2022
1
Abstract Synopsis
- The accuracy of predicting how individual patients respond to new drugs is vital for personalized medicine, but limited patient data makes it challenging to create effective machine learning models.
- Despite existing methods to leverage cell-line data for these predictions, issues like data variability lead to unreliable outcomes.
- The newly developed CODE-AE autoencoder addresses these challenges by extracting relevant biological signals and improving prediction accuracy for patient-specific drug responses, demonstrating promise in screening multiple drugs for a large cancer patient population.
Science Society
@ScienceSociety8 (Twitter)