Table 5_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.xlsx by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Table 1_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.xlsx by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Table 7_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.xlsx by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Data Sheet 7_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.csv by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Table 4_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.xlsx by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Data Sheet 2_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.csv by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Table 6_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.xlsx by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Image 2_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.png by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Presentation 1_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.pptx by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Data Sheet 8_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.csv by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Data Sheet 4_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.csv by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Table 8_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.xlsx by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Presentation 2_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.pptx by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Table 2_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.xlsx by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Data Sheet 9_Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box.csv by Douglas R. Walker (3664039)

“…This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.</p>Discussion<p>Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. …”

Data Sheet 1_Identification of novel lipid metabolism-related biomarkers of aortic dissection by integrating single-cell RNA sequencing analysis and machine learning algorithms.zip by Zhechen Li (15456929)

“…Single-cell RNA sequencing data from aortic dissection and control samples were processed to analyze lipid metabolism activity and identify differentially expressed genes. Machine learning algorithms and protein-protein interaction networks were then used to prioritize biomarkers, which were further validated through bulk RNA-seq analysis and immune infiltration studies and experiments using an Ang II-induced aortic dissection mouse model.. …”

Table 3_Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.xlsx by Zuhui Pu (10931751)

“…</p>Results<p>Our analysis identified 1953 DEGs between T2D and ND patients, with the Stepglm[backward] plus GBM model demonstrating high predictive accuracy and identifying 13 hub RMITRGs. Twelve protein structures were predicted using AlphaFold 3, revealing potential functional conformations. …”

Table 2_Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.xlsx by Zuhui Pu (10931751)

“…</p>Results<p>Our analysis identified 1953 DEGs between T2D and ND patients, with the Stepglm[backward] plus GBM model demonstrating high predictive accuracy and identifying 13 hub RMITRGs. Twelve protein structures were predicted using AlphaFold 3, revealing potential functional conformations. …”

Table 1_Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.xlsx by Zuhui Pu (10931751)

“…</p>Results<p>Our analysis identified 1953 DEGs between T2D and ND patients, with the Stepglm[backward] plus GBM model demonstrating high predictive accuracy and identifying 13 hub RMITRGs. Twelve protein structures were predicted using AlphaFold 3, revealing potential functional conformations. …”

Table 4_Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.xlsx by Zuhui Pu (10931751)

“…</p>Results<p>Our analysis identified 1953 DEGs between T2D and ND patients, with the Stepglm[backward] plus GBM model demonstrating high predictive accuracy and identifying 13 hub RMITRGs. Twelve protein structures were predicted using AlphaFold 3, revealing potential functional conformations. …”