machine learning model (Deepmind Technologies Ltd)
86
Structured Review
Deepmind Technologies Ltd
machine learning model
Machine Learning Model, supplied by Deepmind Technologies Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/machine+learning+models/learning+reinforcement/us12561351-267-10-24
Average 86 stars, based on 1 article reviews
Machine Learning Model, supplied by Deepmind Technologies Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/machine+learning+models/learning+reinforcement/us12561351-267-10-24
Average 86 stars, based on 1 article reviews
machine learning model - by Bioz Stars,
2026-09
86/100 stars
Images
Related Articles
Expressing:Article Title: Expression of the readthrough transcript CiDRE in alveolar macrophages boosts SARS-CoV-2 susceptibility and promotes COVID-19 severity. Article Snippet: We used the flair pipeline90 to identify the full-length of the IFNAR2-IL10RB readthrough transcripts and filtered them using the following criteria: 1) isoforms expressing more than 50 reads in total, 2) isoforms whose 50 end was located within 100 bp from the FANTOM CAGE peak (TSS peak based on a relaxed 0.14 threshold by TSS classifier), 3) isoforms whose 30 end is located within 100 bp from the TES of PolyASite2.0, and 4) isoforms evaluated as protein coding isoforms by CPAT v3.0.4 (coding probability R 0.364).44 To visualize the structures of IFNAR2IL10RB readthrough transcripts in IFNAR2-IL10RB region, sashimi plot was generated by mapping RNA-seq reads of monocytes obtained from the EvoImmunoPop project42 to the annotated these transcripts (in GTF format) using ggsashimi (https://github. com/guigolab/ggsashimi).72 Three-dimensional structure computational analysis Three-dimensional (3D) structures of the hybrid receptor and receptor-ligand complexes were predicted by AlphaFold v2.2.2 (https:// github.com/deepmind/alphafold).73 For each receptor-ligand complex, 25 structural models were generated (5 predictions for each of 5 AlphaFold machine learning models). .. We used the flair pipeline90 to identify the full-length of the IFNAR2-IL10RB readthrough transcripts and filtered them using the following criteria: 1) isoforms expressing more than 50 reads in total, 2) isoforms whose 50 end was located within 100 bp from the FANTOM CAGE peak (TSS peak based on a relaxed 0.14 threshold by TSS classifier), 3) isoforms whose 30 end is located within 100 bp from the TES of PolyASite2.0, and 4) isoforms evaluated as protein coding isoforms by CPAT v3.0.4 (coding probability R 0.364).44 To visualize the structures of IFNAR2IL10RB readthrough transcripts in IFNAR2-IL10RB region, sashimi plot was generated by mapping RNA-seq reads of monocytes obtained from the EvoImmunoPop project42 to the annotated these transcripts (in GTF format) using ggsashimi (https://github. com/guigolab/ggsashimi).72 Three-dimensional structure computational analysis Three-dimensional (3D) structures of the hybrid receptor and receptor-ligand complexes were predicted by Generated:Article Title: Expression of the readthrough transcript CiDRE in alveolar macrophages boosts SARS-CoV-2 susceptibility and promotes COVID-19 severity. Article Snippet: We used the flair pipeline90 to identify the full-length of the IFNAR2-IL10RB readthrough transcripts and filtered them using the following criteria: 1) isoforms expressing more than 50 reads in total, 2) isoforms whose 50 end was located within 100 bp from the FANTOM CAGE peak (TSS peak based on a relaxed 0.14 threshold by TSS classifier), 3) isoforms whose 30 end is located within 100 bp from the TES of PolyASite2.0, and 4) isoforms evaluated as protein coding isoforms by CPAT v3.0.4 (coding probability R 0.364).44 To visualize the structures of IFNAR2IL10RB readthrough transcripts in IFNAR2-IL10RB region, sashimi plot was generated by mapping RNA-seq reads of monocytes obtained from the EvoImmunoPop project42 to the annotated these transcripts (in GTF format) using ggsashimi (https://github. com/guigolab/ggsashimi).72 Three-dimensional structure computational analysis Three-dimensional (3D) structures of the hybrid receptor and receptor-ligand complexes were predicted by AlphaFold v2.2.2 (https:// github.com/deepmind/alphafold).73 For each receptor-ligand complex, 25 structural models were generated (5 predictions for each of 5 AlphaFold machine learning models). .. We used the flair pipeline90 to identify the full-length of the IFNAR2-IL10RB readthrough transcripts and filtered them using the following criteria: 1) isoforms expressing more than 50 reads in total, 2) isoforms whose 50 end was located within 100 bp from the FANTOM CAGE peak (TSS peak based on a relaxed 0.14 threshold by TSS classifier), 3) isoforms whose 30 end is located within 100 bp from the TES of PolyASite2.0, and 4) isoforms evaluated as protein coding isoforms by CPAT v3.0.4 (coding probability R 0.364).44 To visualize the structures of IFNAR2IL10RB readthrough transcripts in IFNAR2-IL10RB region, sashimi plot was generated by mapping RNA-seq reads of monocytes obtained from the EvoImmunoPop project42 to the annotated these transcripts (in GTF format) using ggsashimi (https://github. com/guigolab/ggsashimi).72 Three-dimensional structure computational analysis Three-dimensional (3D) structures of the hybrid receptor and receptor-ligand complexes were predicted by RNA Sequencing:Article Title: Expression of the readthrough transcript CiDRE in alveolar macrophages boosts SARS-CoV-2 susceptibility and promotes COVID-19 severity. Article Snippet: We used the flair pipeline90 to identify the full-length of the IFNAR2-IL10RB readthrough transcripts and filtered them using the following criteria: 1) isoforms expressing more than 50 reads in total, 2) isoforms whose 50 end was located within 100 bp from the FANTOM CAGE peak (TSS peak based on a relaxed 0.14 threshold by TSS classifier), 3) isoforms whose 30 end is located within 100 bp from the TES of PolyASite2.0, and 4) isoforms evaluated as protein coding isoforms by CPAT v3.0.4 (coding probability R 0.364).44 To visualize the structures of IFNAR2IL10RB readthrough transcripts in IFNAR2-IL10RB region, sashimi plot was generated by mapping RNA-seq reads of monocytes obtained from the EvoImmunoPop project42 to the annotated these transcripts (in GTF format) using ggsashimi (https://github. com/guigolab/ggsashimi).72 Three-dimensional structure computational analysis Three-dimensional (3D) structures of the hybrid receptor and receptor-ligand complexes were predicted by AlphaFold v2.2.2 (https:// github.com/deepmind/alphafold).73 For each receptor-ligand complex, 25 structural models were generated (5 predictions for each of 5 AlphaFold machine learning models). .. We used the flair pipeline90 to identify the full-length of the IFNAR2-IL10RB readthrough transcripts and filtered them using the following criteria: 1) isoforms expressing more than 50 reads in total, 2) isoforms whose 50 end was located within 100 bp from the FANTOM CAGE peak (TSS peak based on a relaxed 0.14 threshold by TSS classifier), 3) isoforms whose 30 end is located within 100 bp from the TES of PolyASite2.0, and 4) isoforms evaluated as protein coding isoforms by CPAT v3.0.4 (coding probability R 0.364).44 To visualize the structures of IFNAR2IL10RB readthrough transcripts in IFNAR2-IL10RB region, sashimi plot was generated by mapping RNA-seq reads of monocytes obtained from the EvoImmunoPop project42 to the annotated these transcripts (in GTF format) using ggsashimi (https://github. com/guigolab/ggsashimi).72 Three-dimensional structure computational analysis Three-dimensional (3D) structures of the hybrid receptor and receptor-ligand complexes were predicted by |