Matches in SemOpenAlex for { <https://semopenalex.org/work/W4376609015> ?p ?o ?g. }
- W4376609015 endingPage "880" @default.
- W4376609015 startingPage "871" @default.
- W4376609015 abstract "Abstract Cryogenic-electron tomography enables the visualization of cellular environments in extreme detail, however, tools to analyze the full amount of information contained within these densely packed volumes are still needed. Detailed analysis of macromolecules through subtomogram averaging requires particles to first be localized within the tomogram volume, a task complicated by several factors including a low signal to noise ratio and crowding of the cellular space. Available methods for this task suffer either from being error prone or requiring manual annotation of training data. To assist in this crucial particle picking step, we present TomoTwin: an open source general picking model for cryogenic-electron tomograms based on deep metric learning. By embedding tomograms in an information-rich, high-dimensional space that separates macromolecules according to their three-dimensional structure, TomoTwin allows users to identify proteins in tomograms de novo without manually creating training data or retraining the network to locate new proteins." @default.
- W4376609015 created "2023-05-17" @default.
- W4376609015 creator A5012560227 @default.
- W4376609015 creator A5020492463 @default.
- W4376609015 creator A5030445850 @default.
- W4376609015 creator A5039034314 @default.
- W4376609015 creator A5040763698 @default.
- W4376609015 creator A5056934118 @default.
- W4376609015 date "2023-05-15" @default.
- W4376609015 modified "2023-10-14" @default.
- W4376609015 title "TomoTwin: generalized 3D localization of macromolecules in cryo-electron tomograms with structural data mining" @default.
- W4376609015 cites W151377110 @default.
- W4376609015 cites W1826842517 @default.
- W4376609015 cites W2003294049 @default.
- W4376609015 cites W2029101220 @default.
- W4376609015 cites W2042455841 @default.
- W4376609015 cites W2051555473 @default.
- W4376609015 cites W2100455255 @default.
- W4376609015 cites W2115251427 @default.
- W4376609015 cites W2123176293 @default.
- W4376609015 cites W2142161929 @default.
- W4376609015 cites W2175730676 @default.
- W4376609015 cites W2262536800 @default.
- W4376609015 cites W2427061751 @default.
- W4376609015 cites W2431898786 @default.
- W4376609015 cites W2513935955 @default.
- W4376609015 cites W2525287802 @default.
- W4376609015 cites W2614431660 @default.
- W4376609015 cites W2760207683 @default.
- W4376609015 cites W2791376837 @default.
- W4376609015 cites W2797639802 @default.
- W4376609015 cites W2890922689 @default.
- W4376609015 cites W2899714098 @default.
- W4376609015 cites W2949676527 @default.
- W4376609015 cites W2952729952 @default.
- W4376609015 cites W2958578978 @default.
- W4376609015 cites W2963466847 @default.
- W4376609015 cites W2965314492 @default.
- W4376609015 cites W2969656782 @default.
- W4376609015 cites W2969985801 @default.
- W4376609015 cites W2977933365 @default.
- W4376609015 cites W2978113727 @default.
- W4376609015 cites W2982649325 @default.
- W4376609015 cites W3006390590 @default.
- W4376609015 cites W3014501010 @default.
- W4376609015 cites W3024272706 @default.
- W4376609015 cites W3024513041 @default.
- W4376609015 cites W3083428681 @default.
- W4376609015 cites W3099206234 @default.
- W4376609015 cites W3099398293 @default.
- W4376609015 cites W3103292928 @default.
- W4376609015 cites W3127722007 @default.
- W4376609015 cites W3138595690 @default.
- W4376609015 cites W3154498115 @default.
- W4376609015 cites W3163368994 @default.
- W4376609015 cites W3173393387 @default.
- W4376609015 cites W3201590422 @default.
- W4376609015 cites W3207554858 @default.
- W4376609015 cites W3213779066 @default.
- W4376609015 cites W4212820271 @default.
- W4376609015 cites W4225013468 @default.
- W4376609015 cites W4226195134 @default.
- W4376609015 cites W4280502557 @default.
- W4376609015 cites W4281653124 @default.
- W4376609015 cites W4281726159 @default.
- W4376609015 cites W4282936228 @default.
- W4376609015 cites W4307540205 @default.
- W4376609015 cites W4310596742 @default.
- W4376609015 cites W4315465107 @default.
- W4376609015 cites W4320068442 @default.
- W4376609015 doi "https://doi.org/10.1038/s41592-023-01878-z" @default.
- W4376609015 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37188953" @default.
- W4376609015 hasPublicationYear "2023" @default.
- W4376609015 type Work @default.
- W4376609015 citedByCount "9" @default.
- W4376609015 countsByYear W43766090152023 @default.
- W4376609015 crossrefType "journal-article" @default.
- W4376609015 hasAuthorship W4376609015A5012560227 @default.
- W4376609015 hasAuthorship W4376609015A5020492463 @default.
- W4376609015 hasAuthorship W4376609015A5030445850 @default.
- W4376609015 hasAuthorship W4376609015A5039034314 @default.
- W4376609015 hasAuthorship W4376609015A5040763698 @default.
- W4376609015 hasAuthorship W4376609015A5056934118 @default.
- W4376609015 hasBestOaLocation W43766090151 @default.
- W4376609015 hasConcept C120665830 @default.
- W4376609015 hasConcept C121332964 @default.
- W4376609015 hasConcept C124101348 @default.
- W4376609015 hasConcept C127413603 @default.
- W4376609015 hasConcept C153180895 @default.
- W4376609015 hasConcept C154945302 @default.
- W4376609015 hasConcept C163716698 @default.
- W4376609015 hasConcept C176217482 @default.
- W4376609015 hasConcept C181990884 @default.
- W4376609015 hasConcept C185592680 @default.
- W4376609015 hasConcept C193016168 @default.
- W4376609015 hasConcept C201995342 @default.
- W4376609015 hasConcept C204389451 @default.
- W4376609015 hasConcept C21547014 @default.