High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

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High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

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High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration by Xia Xu (83500)

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Functional enrichment analysis of hub genes. by Tingting Liu (267387)

“…<p>(A) Hub genes were selected using the MCC algorithm. (B) Enrichment analysis of biological processes (BP) hub genes. …”

Matlab code of a function used in S2 File. by Hiroko Kato Solvang (5718250)

Flowchart of DAPF-RRT algorithm. by Zhenggang Wang (1753657)

Subjects: “…target gravitational function…”

Performance comparison of different algorithms. by Zhenggang Wang (1753657)

Subjects: “…target gravitational function…”

Algorithm for constructing Boot-t CIs. by Qin Gong (118801)

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The convergence curve of each optimization algorithm under the test function. by Hao Yang (328526)

Table 6_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

Table 7_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

Table 3_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

Table 2_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

Table 1_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

Table 4_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

Table 5_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

Boxplot analysis for ITAE objective function using en-CSA, CSA, RUN, PDO and RIME algorithms. by Sarah A. Alzakari (19704611)

“…<p>Boxplot analysis for ITAE objective function using en-CSA, CSA, RUN, PDO and RIME algorithms.…”

Supplemental files to the study "Limitations of Current Machine-Learning Models in Predicting Enzymatic Functions for Uncharacterized Proteins" by Valerie de Crecy-Lagard (12823436)

“…An open question is the ability of machine-learning approaches to predict enzymatic functions unseen in the training sets. Using a set of <i>E. coli</i> unknowns, we evaluated the current state-of-the-art machine-learning approaches and found that these methods currently lack the ability to integrate scientific reasoning into their prediction algorithms. …”

Box plots of different algorithms on CEC2017 test function. by Tengfei Ma (597633)

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Expression levels of spinal cord proteins in each group of rats. by Yufeng He (5673119)

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