Their main findins: they measured the folding time of 8 proteins, is took 1 to 4 milliseconds.

Ideally t-SNE embedding grows, during its learning process, from a tine spot to a short bar that then elongates, splits and expands to a cluster of various shapes with gradient boarder. The following is an embedding of 6K proteins based on their 3D coordinates:

t-SNE & Initialization. Because the use of exaggerated learning phase its embeddings normally converge to a single final map (upto rotaion). This makes dedicated initialization largely irrelevant. For this reason, VisuMap's implementation only supports random initialization.

t-SNE & learning-rate. A key feature of t-SNE is its use of adaptive-gains for each optimized variables. This make the learning-rate parameter largely irrelevant. For this reason, VisuMap's implementation has a fixed learning-rate (500), users don't have to care about it.

Protein Atlas with t-SNE: We can vectorize amino-acid chains with Fourier-Transform and embed them with t-SNE into 2D space. Here is a map of ca. 100K AA-chains. The map reveals the similarity between the 3D structures.

This year’s chemistry laureates Demis Hassabis and John Jumper have developed an AI model, AlphaFold2, to solve a 50-year-old problem: predicting proteins’ complex structures. Check out two examples of protein structures determined using AlphaFold2. First up, a bacterial enzyme that causes antibiotic resistance. The structure is important for discovering ways of preventing antibiotic resistance. Animation: ©Terezia Kovalova/The Royal Swedish Academy of Sciences #NobelPrize

UnFolding protein with t-SNE: The following video clip shows the 3D structure of a protein complex (6EMK, rcsb.org/3d-view/6EMK). The t-SNE embedding shows clearly the C2 symmetry between upper and lower halves.

t-SNE for un-folding protein 3D structure: When we add the sequential index as the forth dimension to the 3D coordinates of polypeptides, t-SNE can produce 2D maps which unfold their 3D structures; and reveal more sub-clusters with their distinguished shapes.

新华网在B站关于姜萍的视频“探索数学世界只是姜萍的PlanB”已经被删除了。

T-sne was the true breakthrough from 3 dozens of nonlinear dim. reduction methods in 2008. bhSne, fastSne and umap are sad "optimizations" which ignore large distances, and made it more or less useless for complex data. nature.com/articles/s41592-0…

t-SNE & scRNA-seq analysis: From expression matrix to network of dominant & pilot genes. Labeling cell clusters with dominant genes; and labeling links between them with pilot genes we get a network that indicates the major variation of gene expressions.

t-SNE & scRNA-seq Analysis: t-SNE embedding annotated with dominant genes offers an effective way for comparative study of cell groups. The following video shows the basic steps:

t-SNE for ribosome gene (rRNA) expression: t-SNE embedding forms cell clusters with 1 to 3 dominant genes; and neighboring clusters often differ only on 1 or 2 dominant genes. Annotating links between clusters by those differences leads to a kind of directed correlation-graph: