(a) Radar chart of these algorithms (23 Benchmark functions), (b) The sorting diagram of these algorithms (23 Benchmark functions). by Yu Liu (6938)

“…<p>(a) Radar chart of these algorithms (23 Benchmark functions), (b) The sorting diagram of these algorithms (23 Benchmark functions).…”

Confirmation of hub MitoDEGs expression and association with cardiac function in MI mice. by Wenlong Kuang (20485167)

Subjects:

The rules obtained with the original method versus the ones obtained with the improved parameter classification algorithm. by Ljubisa Miskovic (4175095)

A CoCoNet model with a small <i>K</i> is often preferred than a CoCoNet model with a large <i>K</i> in real data sets. by Lulu Shang (8735736)

SARS-CoV-2 secondary structure motifs free energy per nt. by Luke Trinity (6792770)

Establishment of a senescence-related graft survival prognostic model. by Xihao Shen (20347942)

Subjects:

The optimal solution set of NYN by using different algorithms. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The optimal solution set of HN by using different algorithms. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

SARS-CoV-2 pseudoknot predictions overlap, 222 nt window via Shapify. by Luke Trinity (6792770)

CEC2022 and CEC2017 benchmark functions. by Chengcheng Ding (9013862)

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)

“…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. …”

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)

“…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. …”

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)

“…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. …”

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)

“…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. …”

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)

“…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. …”

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)

“…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. …”

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)

“…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. …”

Construction of weighted co-expression network related datasets and identification of related key modules. by Wenlong Kuang (20485167)

Subjects:

Table1_Accelerate the Electrolyte Perturbed-Chain Statistical Associating Fluid Theory–Density Functional Theory Calculation With the Chebyshev Pseudo-Spectral Collocation Method.... by Yunhao Sun (4165822)

Subjects:

Schematics for gmxapi operations. by M. Eric Irrgang (4487278)