Journal: Briefings in Bioinformatics

Article Title: Deep generative model for protein subcellular localization prediction

doi: 10.1093/bib/bbaf152

Figure Lengend Snippet: An overview design of deepGPS. A schematic diagram illustrating the architecture of deepGPS with a nucleus image and a protein sequence as inputs. DeepGPS enables the prediction of protein subcellular localization with generating a text label and an artificial fluorescence image as outputs.

Article Snippet: Since the performance of a model largely depends on the quality of the training dataset, we thus set to build a comprehensive image dataset of protein subcellular localization for deepGPS construction from OpenCell [ ].

Techniques: Sequencing, Fluorescence

Journal: Briefings in Bioinformatics

Article Title: Deep generative model for protein subcellular localization prediction

doi: 10.1093/bib/bbaf152

Figure Lengend Snippet: Construction and evaluation for deepGPS-single-2. (a) A specific example of image processing, illustrating the workflow from image segmentation to image cropping. (b) Distribution of proteins with only one major localization in the OpenCell database. (c) Strategy for training deepGPS-single-2. (d) Six examples generated by deepGPS-single-2. GT, ground truth; PSNR, peak signal-to-noise ratio. (e) A cross-assay using the ground-truth nucleus image as a nuclear fiducial marker and inputting different protein sequences to deepGPS-single-2. AGO1 and FAM120A are cytoplasmic proteins shown in blue, while HNRNPD and SMARCD2 are nuclear proteins shown in red. Of note, protein images generated by deepGPS in panels (d and e) were all from the test set, which were not used for model training.

Article Snippet: Since the performance of a model largely depends on the quality of the training dataset, we thus set to build a comprehensive image dataset of protein subcellular localization for deepGPS construction from OpenCell [ ].

Techniques: Generated, Marker

Journal: Briefings in Bioinformatics

Article Title: Deep generative model for protein subcellular localization prediction

doi: 10.1093/bib/bbaf152

Figure Lengend Snippet: Performance comparison of deepGPS-single-2 variants using different input formats. (a) Schematic diagram illustrating the conversion of a protein structure predicted by AlphaFold2 into a point cloud tensor with carbon, nitrogen, and oxygen channels using PyUUL. (b) Strategies for training three variants of deepGPS-single-2 with different inputs of “nucleus image + protein sequence”, “nucleus image + protein structure”, and “nucleus image + protein sequence + protein structure”. (c) General performance of deepGPS-single-2 variants on the classification task including accuracy, specificity, sensitivity, and F1 score in left and ROC curve in right. (d) General performance of deepGPS-single-2 variants on the generation task.

Article Snippet: Since the performance of a model largely depends on the quality of the training dataset, we thus set to build a comprehensive image dataset of protein subcellular localization for deepGPS construction from OpenCell [ ].

Techniques: Comparison, Sequencing

Journal: Briefings in Bioinformatics

Article Title: Deep generative model for protein subcellular localization prediction

doi: 10.1093/bib/bbaf152

Figure Lengend Snippet: Performance comparison of the HEK293T-specific deepGPS with other published models on classification task using the test set from OpenCell.

Article Snippet: Since the performance of a model largely depends on the quality of the training dataset, we thus set to build a comprehensive image dataset of protein subcellular localization for deepGPS construction from OpenCell [ ].

Techniques: Comparison

Journal: Briefings in Bioinformatics

Article Title: Deep generative model for protein subcellular localization prediction

doi: 10.1093/bib/bbaf152

Figure Lengend Snippet: Performance comparison of the U2OS-specific deepGPS with other published models on classification task using the test set from HPA.

Article Snippet: Since the performance of a model largely depends on the quality of the training dataset, we thus set to build a comprehensive image dataset of protein subcellular localization for deepGPS construction from OpenCell [ ].

Techniques: Comparison

Journal: Briefings in Bioinformatics

Article Title: Deep generative model for protein subcellular localization prediction

doi: 10.1093/bib/bbaf152

Figure Lengend Snippet: Extended deepGPS models for predicting other subcellular localization types. (a) Strategies for training deepGPS-single-4 and deepGPS-all. (b and c) General performance of deepGPS-single-4 on the classification task including accuracy, specificity, sensitivity, and F1 score in panel b and ROC curve in panel c. (d and e) Confusion matrix of proteins (d) and cropped images (e) for the classification task achieved by deepGPS-single-4. (f) Twelve examples generated by deepGPS-single-4. GT, ground truth; PSNR, peak signal-to-noise ratio. (g) General performance of deepGPS-single-2, deepGPS-single-4, and deepGPS-all on the generation task.

Article Snippet: Since the performance of a model largely depends on the quality of the training dataset, we thus set to build a comprehensive image dataset of protein subcellular localization for deepGPS construction from OpenCell [ ].

Techniques: Generated