Skip to content
Fig. 1 | Interpretable biologically informed deep learning.
P-NET is a neural network architecture that encodes different biological entities into a neural network language with customized connections between consecutive layers (that is, features from patient profile, genes, pathways, biological processes and outcome). In this study, we focus primarily on processing mutations and copy-number alterations. The trained P-NET provides a relative ranking of nodes in each layer to inform generation of biological hypotheses. Solid lines show the flow of information from the inputs to generate the outcome and dashed lines show the direction of calculating the importance score of different nodes. Candidate genes are validated to understand their function and mechanism of action. Elmarakeby, H.A., Hwang, J., Arafeh, R. et al. Biologically informed deep neural network for prostate cancer discovery. Nature (2021). https://doi.org/10.1038/s41586-021-03922-4
I think it is safe to assume that the Lena image became a standard in our "industry" for two reasons. First, the image contains a nice mixture of detail, flat regions, shading, and texture that do a good job of testing various image processing algorithms. It is a good test image! Second, the Lena image is a picture of an attractive woman. It is not surprising that the (mostly male) image processing research community gravitated toward an image that they found attractive. A Note on Lena. IEEE TRANSACTIONS ON IMAGE PROCESSING. VOL. 5. NO. 1. JANUARY 1996. image source: http://lenna.org/
Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
Figure S5. Identifying Primitive AML Subpopulations by Surface and Signaling Phenotypes, Related to Figure 6 (A) Detailed features of subpopulations displaying primitive and mature signaling phenotypes for each sample. (B) Canonical variates analysis demonstrating the linear separability of IFPCs from non-IFPCs across the entire dataset on the basis of 224 signaling features (left) or on the basis of only the 5 most important features (right) as determined by this analysis Levine, J. H., Simonds, E. F., Bendall, S. C., Davis, K. L., Amir, E. D., Tadmor, M. D., … Nolan, G. P. (2015). Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis. Cell, 162(1), 184–197. doi:10.1016/j.cell.2015.05.047
pxocgx01_blastx against multiple related xanthomonas species
Niu, X.-N., et al. (2015). "Complete sequence and detailed analysis of the first indigenous plasmid from Xanthomonas oryzae pv. oryzicola." BMC Microbiol 15(1): 1-15. DOI: 10.1186/s12866-015-0562-x Bacterial plasmids have a major impact on metabolic function and adaptation of their hosts. An indigenous plasmid was identified in a Chinese isolate (GX01) of the invasive phytopathogen Xanthomonas oryzae pv. oryzicola (Xoc), the causal agent of rice bacterial leaf streak (BLS). To elucidate the biological functions of the plasmid, we have sequenced and comprehensively annotated the plasmid.
HR2MSI mouse urinary bladder S096 - spatial regions
The spatial regions that detected via the UMAP manifold data embedding. the pixels region is cluster via DBSCAN algorithm. and re-shape the spatial regions via contour tracing algorithm. Römpp A, Guenther S, Schober Y, Schulz O, Takats Z, Kummer W, Spengler B; Histology by mass spectrometry: label-free tissue characterization obtained from high-accuracy bioanalytical imaging., Angew Chem Int Ed Engl, 2010 May 17, 49, 22, 3834-8, , PubMed: 20397170
What is flux balance analysis?
Figure 2 Formulation of an FBA problem. (a) A metabolic network reconstruction consists of a list of stoichiometrically balanced biochemical reactions. (b) This reconstruction is converted into a mathematical model by forming a matrix (labeled S), in which each row represents a metabolite and each column represents a reaction. Growth is incorporated into the reconstruction with a biomass reaction (yellow column), which simulates metabolites consumed during biomass production. Exchange reactions (green columns) are used to represent the flow of metabolites, such as glucose and oxygen, in and out of the cell. (c) At steady state, the flux through each reaction is given by Sv = 0, which defines a system of linear equations. As large models contain more reactions than metabolites, there is more than one possible solution to these equations. (d) Solving the equations to predict the maximum growth rate requires defining an objective function Z = cTv (c is a vector of weights indicating how much each reaction (v) contributes to the objective). In practice, when only one reaction, such as biomass production, is desired for maximization or minimization, c is a vector of zeros with a value of 1 at the position of the reaction of interest. In the growth example, the objective function is Z = vbiomass (that is, c has a value of 1 at the position of the biomass reaction). (e) Linear programming is used to identify a flux distribution that maximizes or minimizes the objective function within the space of allowable fluxes (blue region) defined by the constraints imposed by the mass balance equations and reaction bounds. The thick red arrow indicates the direction of increasing Z. As the optimal solution point lies as far in this direction as possible, the thin red arrows depict the process of linear programming, which identifies an optimal point at an edge or corner of the solution space. Orth JD, Thiele I, Palsson BØ. What is flux balance analysis? Nat Biotechnol. 2010 Mar;28(3):245-8. doi: 10.1038/nbt.1614. PMID: 20212490; PMCID: PMC3108565.
Electron micrographs of Synechococcus
Ultrastructural comparison of Synechococcus strains. Electron micrographs of (A) Synechococcus UTEX 2973 and (B) Synechococcus PCC 7942 grown in 3% CO2. Labeled are carboxysomes (C) and thylakoid membranes (T). White arrowheads point to the numerous electron-dense bodies. Bar = 500 nm. Yu, J., Liberton, M., Cliften, P. et al. Synechococcus elongatus UTEX 2973, a fast growing cyanobacterial chassis for biosynthesis using light and CO2. Sci Rep 5, 8132 (2015). https://doi.org/10.1038/srep08132
This is a photograph of Egypt, Red Sea, Sinai Peninsula and the Nile from Earth orbit annotated with chemical composition of Earth's atmosphere.
The Golden Record Cover
the cover is designed to show how pictures are to be constructed from the recorded signals. The top drawing shows the typical signal that occurs at the start of a picture. The picture is made from this signal, which traces the picture as a series of vertical lines, similar to ordinary television (in which the picture is a series of horizontal lines). Picture lines 1, 2 and 3 are noted in binary numbers, and the duration of one of the "picture lines," about 8 milliseconds, is noted. The drawing immediately below shows how these lines are to be drawn vertically, with staggered "interlace" to give the correct picture rendition. Immediately below this is a drawing of an entire picture raster, showing that there are 512 vertical lines in a complete picture. Immediately below this is a replica of the first picture on the record to permit the recipients to verify that they are decoding the signals correctly. A circle was used in this picture to ensure that the recipients use the correct ratio of horizontal to vertical height in picture reconstruction. https://voyager.jpl.nasa.gov/golden-record/golden-record-cover/
Integration of bulk RNAseq with single cell RNAseq of human postnatal thymus a Uniform
Integration of bulk RNAseq with single cell RNAseq of human postnatal thymus a, Uniform manifold approximation and projection (UMAP) of the single cell RNAseq dataset available from³⁰ with integration of our 11 bulk RNAseq subsets (red diamonds). DN: CD4 CD8 double negative; DP: CD4⁺ CD8⁺; P: proliferative; Q: quiescent; T(agonist): agonist selected T cells; DP (late): Positively selected DP T cells. b, Classification of single cells to each of the 11 bulk populations by logistic regression. c, Probability that single cells are classified according to any of the bulk RNAseq samples by logistic regression. Distinct and temporary-restricted epigenetic mechanisms regulate human αβ and γδ T cell development. Nature Immunology volume 21, pages1280–1292 (2020). https://doi.org/10.1038/s41590-020-0747-9
LC-MS scatter 2D
This scatter plot visualize of the LC-MS ms1 scan raw data. the X axis is the retention time in time unit seconds and the Y axis is the m/z value. Scatter color is mapping from the intensity value scale.
A detailed spatial map of the nucleolar human proteome
Schematic overview of the nucleolus and its substructures: fibrillar center (FC), dense fibrillar component (DFC), and granular component (GC). UTP6 in U-2 OS cells exemplify proteins localized to whole nucleoli (HPA025936). Fibrillar center localization shown by a UBTF IF staining in U-2 OS cells (HPA006385). UMAP visualization of the IF images generated in the HPA Cell Atlas (also shown in Fig 3A). The images from singularly localizing nuclear proteins are highlighted in purple (nucleoli), blue (fibrillar center), brown (nuclear speckles), and orange (nuclear bodies). Multilocalizing nucleolar proteins are highlighted in yellow. Dual localization of LEO1 to both the fibrillar center and nucleoplasm in GFP-tagged HeLa cells (magenta), also supported by IF antibody staining using HPA040741 (green). The multilocalizing ribosomal protein RPL13 detected in the nucleoli, cytosol, and ER in MCF-7 cells (HPA051702). Data information: Protein of interest is shown in green, nuclear marker DAPI in blue, and the reference marker of microtubules in red. Scale bar 10 μm. Mapping the nucleolar proteome reveals a spatiotemporal organization related to intrinsic protein disorder. Mol Syst Biol (2020)16:e9469. https://doi.org/10.15252/msb.20209469
Automated Optimal Parameters for T-Distributed Stochastic Neighbor Embedding Improve Visualization and Allow Analysis of Large Datasets
opt-SNE allows high-quality visualization of large cytometry and transcriptomics datasets. A-D: 20 million datapoints from fluorescent cytometry dataset concatenated from 27 subjects vizualized in 2D space. A and C, cell type classes and density overlaid on 2D opt-SNE embedding. B, subject identifier overlaid on 2D opt-SNE embedding. Dashed arrows indicate clusters represented by datapoints from a single subject; D, standard t-SNE visualization (4000 iterations). E-F, 10X Genomics mouse brain scRNA-seq dataset (1.3 million datapoints) visualized in 2D space with opt-SNE (E) or standard t-SNE (F). From left to right: density features, single gene classes, and Louvain clusters (0-38) overlays. G, 5.22 million datapoints from mass cytometry dataset used in van Unen et al (2017) visualized in 2D space with opt-SNE. From left to right: CD4 expression overlaid on opt-SNE embedding; CCR7 and CD28 expression overlaid on CD4+ opt-SNE cluster; CD45RA and CD56 expression intensity overlaid on CD4+CD28-CCR7-cluster. H, CD4+CD28-CCR7-cells from control, celiac disease (CeD), refractory celiac disease (RCeD) and Crohn’s disease (CrohnD) subjects presented on density plots. Dashed encirclements indicate CD45RA+ and CD56+ areas of the cluster as defined in G. I, hierarchical t-SNE (HSNE) embedding of the CD4 (left) and CD4+CD28- cluster (right) from van Unen et al (2017). Color indicates marker expression intensity. Belkina, A.C., Ciccolella, C.O., Anno, R. et al. Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nat Commun 10, 5415 (2019). https://doi.org/10.1038/s41467-019-13055-y
GO enrichment barplot
The barplot of the go enrichment analysis. The gene ontology is consist of 3 namespace: biological process, cellular component, and molecular function. The bar length is corresponding to the -log10 transformation of the fisher pvalue.
Computational analysis of biochemical systems : a practical guide for biochemists and molecular biologists / Eberhard O. Voit.
Voit, Eberhard O. Computational analysis of biochemical systems : a practical guide for biochemists and molecular biologists / Eberhard O. Voit. New York : Cambridge University Press, 2000. [B859329]
Computational Analysis of Biochemical Systems
The book cover of 《Computational Analysis of Biochemical Systems》 in chinese edition and the original edition in English.
Data visualization of the Umap manifold data embedding in R# language scripting. The demo dataset is comes from the MNIST dataset
binary tree clustering of phenotypic
The regulation motif of gene expression is associated with the pathwayand then the motif is clustering into a binary tree via btree cluster method. the same pathway is annotated with the same color. and as you can see, a set of motif that regulates a set og gene with upsteam/downstream relationship in a reaction network could be clustering into one cluster, assign the same pathway cluster result.
Fisher Exact Test
A diagram for illustrate how to calculate the fisher test pvalue for the gene set enrichment of a specific pathway.
© 2021 この中二病に爆焔を！. Created for free using WordPress and