Matches in Nanopublications for { ?s <https://w3id.org/np/o/ntemplate/hasLabelFromApi> ?o ?g. }
- ENVO_00000524 hasLabelFromApi "abandoned watercourse - A former stream or distributary no longer carrying flowing water, but still evident due to lakes, wetland, topographic or vegetation patterns." pubinfo.
- e304eb12-78a6-406e-a764-92367212b0f5 hasLabelFromApi "Carabus (genus), <Unspecified Agent>" pubinfo.
- e304eb12-78a6-406e-a764-92367212b0f5 hasLabelFromApi "Carabus (genus), <Unspecified Agent>" pubinfo.
- RO_0001018 hasLabelFromApi "contained in" pubinfo.
- RO_0002471 hasLabelFromApi "is eaten by" pubinfo.
- UBERON_0007222 hasLabelFromApi "late adult stage" pubinfo.
- ENVO_01000829 hasLabelFromApi "water vapour saturated air - Air which has a partial pressure of water vapour equal or near equal to its equilibrium vapor pressure at a given temperature." pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- M4M.24.T1 hasLabelFromApi "M4M.24.T1 | PARC M4M Training 1 - by Barbara Magagna" pubinfo.
- 9685 hasLabelFromApi "Felis catus (species)" pubinfo.
- 7VW9H hasLabelFromApi "Mus (Mus) musculus (species), Linnaeus, 1758" pubinfo.
- RO_0002439 hasLabelFromApi "preys on" pubinfo.
- Orphanet_120204 hasLabelFromApi "tumor protein p53" pubinfo.
- Orphanet_120204 hasLabelFromApi "tumor protein p53" pubinfo.
- Orphanet_120204 hasLabelFromApi "tumor protein p53" pubinfo.
- 7965 hasLabelFromApi "AIMP2 - The protein-coding gene AIMP2 (aminoacyl tRNA synthetase complex-interacting multifunctional protein 2) located on the chromosome 7 mapped at 7p22." pubinfo.
- 7965 hasLabelFromApi "AIMP2 - The protein-coding gene AIMP2 (aminoacyl tRNA synthetase complex-interacting multifunctional protein 2) located on the chromosome 7 mapped at 7p22." pubinfo.
- 7965 hasLabelFromApi "AIMP2 - The protein-coding gene AIMP2 (aminoacyl tRNA synthetase complex-interacting multifunctional protein 2) located on the chromosome 7 mapped at 7p22." pubinfo.
- ENVO_00000111 hasLabelFromApi "forested area - An area with a high density of trees. A small forest may be called a wood." pubinfo.
- ENVO_00000111 hasLabelFromApi "forested area - An area with a high density of trees. A small forest may be called a wood." pubinfo.
- ENVO_00000111 hasLabelFromApi "forested area - An area with a high density of trees. A small forest may be called a wood." pubinfo.
- ENVO_00000111 hasLabelFromApi "forested area - An area with a high density of trees. A small forest may be called a wood." pubinfo.
- ENVO_00000111 hasLabelFromApi "forested area - An area with a high density of trees. A small forest may be called a wood." pubinfo.
- ENVO_00000111 hasLabelFromApi "forested area - An area with a high density of trees. A small forest may be called a wood." pubinfo.
- ENVO_00000111 hasLabelFromApi "forested area - An area with a high density of trees. A small forest may be called a wood." pubinfo.
- 9612 hasLabelFromApi "Canis lupus (species)" pubinfo.
- 9612 hasLabelFromApi "Canis lupus (species)" pubinfo.
- 9612 hasLabelFromApi "Canis lupus (species)" pubinfo.
- 9612 hasLabelFromApi "Canis lupus (species)" pubinfo.
- 9612 hasLabelFromApi "Canis lupus (species)" pubinfo.
- 9612 hasLabelFromApi "Canis lupus (species)" pubinfo.
- 9612 hasLabelFromApi "Canis lupus (species)" pubinfo.
- 9612 hasLabelFromApi "Canis lupus (species)" pubinfo.
- 9612 hasLabelFromApi "Canis lupus (species)" pubinfo.
- 9612 hasLabelFromApi "Canis lupus (species)" pubinfo.
- 9612 hasLabelFromApi "Canis lupus (species)" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT1 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 1 - by Barbara Magagna" pubinfo.
- M4M.26.INT2 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 2 - by Barbara Magagna" pubinfo.
- M4M.26.INT2 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 2 - by Barbara Magagna" pubinfo.
- M4M.26.INT2 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 2 - by Barbara Magagna" pubinfo.
- M4M.26.INT2 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 2 - by Barbara Magagna" pubinfo.
- M4M.26.INT2 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 2 - by Barbara Magagna" pubinfo.
- M4M.26.INT2 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 2 - by Barbara Magagna" pubinfo.
- M4M.26.INT2 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 2 - by Barbara Magagna" pubinfo.
- M4M.26.INT2 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 2 - by Barbara Magagna" pubinfo.
- M4M.26.INT2 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 2 - by Barbara Magagna" pubinfo.
- M4M.26.INT2 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 2 - by Barbara Magagna" pubinfo.
- M4M.26.INT2 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 2 - by Barbara Magagna" pubinfo.
- M4M.26.INT2 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 2 - by Barbara Magagna" pubinfo.
- M4M.26.INT2 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 2 - by Barbara Magagna" pubinfo.
- M4M.26.INT2 hasLabelFromApi "M4M.26.INT1| PARC M4M Introduction event 2 - by Barbara Magagna" pubinfo.
- 51338 hasLabelFromApi "Castor canadensis (species)" pubinfo.
- 9874 hasLabelFromApi "Odocoileus virginianus (species)" pubinfo.
- 9874 hasLabelFromApi "Odocoileus virginianus (species)" pubinfo.
- Q107750691 hasLabelFromApi "graph neural network - technique for statistics" pubinfo.
- Q107750691 hasLabelFromApi "graph neural network - technique for statistics" pubinfo.
- Q107750691 hasLabelFromApi "graph neural network - technique for statistics" pubinfo.
- Q107750691 hasLabelFromApi "graph neural network - technique for statistics" pubinfo.
- Q107750691 hasLabelFromApi "graph neural network - technique for statistics" pubinfo.
- Q107750691 hasLabelFromApi "graph neural network - technique for statistics" pubinfo.
- STATO_0000415 hasLabelFromApi "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." pubinfo.
- STATO_0000415 hasLabelFromApi "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." pubinfo.
- STATO_0000415 hasLabelFromApi "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." pubinfo.
- STATO_0000415 hasLabelFromApi "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." pubinfo.
- STATO_0000415 hasLabelFromApi "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." pubinfo.
- STATO_0000415 hasLabelFromApi "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." pubinfo.
- STATO_0000415 hasLabelFromApi "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." pubinfo.
- STATO_0000415 hasLabelFromApi "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." pubinfo.
- STATO_0000415 hasLabelFromApi "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." pubinfo.
- STATO_0000415 hasLabelFromApi "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." pubinfo.