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

"…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …"

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

"…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …"

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

"…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …"

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

"…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …"

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

"…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …"

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

"…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …"

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

"…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …"

Benchmark test function results. حسب Ruyi Dong (9038174)

"…From the statistical results of mean, best and variance of different algorithms, the LLSKSO algorithm outperforms the other algorithms. …"

Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. حسب Gursimran Singh (575288)

الموضوعات:

Merged dataset with analysis of variance and visualization after removal of batch effects for GSE183272 and GSE236374. حسب Wenlong Kuang (20485167)

MitoDEGs in MI; PPI network analysis and hub MitoDEGs identification. حسب Wenlong Kuang (20485167)

Riemann Hypothesis Solution: A Structured Hilbert-Polya Operator Realization via Entropy Geometry حسب Jennifer Bulyaki (22283308)

Machine learning from 21 gly - DEGs filter core gene. حسب Ziyang Liu (242055)

ddPCR mtDNA amplicon concentration as a function of distance. حسب Patrick Kurtz (21772385)

Figure 1 from Functional Genomic Identification of Predictors of Sensitivity and Mechanisms of Resistance to Multivalent Second-Generation TRAIL-R2 Agonists حسب Vera Grinkevitch (15089742)

Greedy optimization algorithm. حسب Alexis Bénichou (19832361)

Skin transfer function ankle render diagram. حسب Yuliang Yuan (4478698)

Bone transfer function pelvis render diagram. حسب Yuliang Yuan (4478698)

The instance of MaxCasesAverted and MaxPeaksReduced is a graph <i>G</i> constructed from the given instance of Max k-SI. حسب Jeffrey Keithley (14626551)

<b>Fig. 3 | Performance analysis of microrobot navigation in various environments using reinforcement learning algorithms.</b> حسب Mahmoud Medany (20766911)

"…<b>e.</b> Impact of different reward functions on the rate of target achievement. …"