Computational Domain: The computational domain was discretized using conformal tetrahedral elements, resulting in a total of 926K elements. حسب Martina Bukač (20161460)

الموضوعات:

Phenotype frequencies for each mutant and for complementation testing. حسب McKenna Feltes (11242085)

Phenotype frequency and complementation for additional novel DY alleles. حسب McKenna Feltes (11242085)

Flowchart of DQN algorithm. حسب Jiandong Qiu (20389944)

Comparison of different algorithms. حسب Jiandong Qiu (20389944)

A classification scheme of bipartite randomization algorithms, based on whether the matrix row and columns sums are preserved exactly (Fixed, <i>F</i>), preserved on average (Propo... حسب Zachary P. Neal (9946331)

ML algorithms in cyber security. حسب Abdullah Asım Yılmaz (20715311)

Generator network and discriminator network. حسب Xiaojun Wang (50332)

"…The model comprehensively utilizes the image restoration and style transfer capabilities of the attention mechanism and the cycle consistency adversarial network, and introduces an improved attention module, which can adaptively highlight the key visual elements in the illustration, thereby maintaining artistic integrity during the style transfer process. …"

Co-authorship network (graph generated using the force-directed Fruchterman-Reingold algorithm). حسب Philippine Dupé (21485482)

The proportions of immune cells based on the xCell algorithm. حسب Wan-Jing Zhen (20427813)

الموضوعات:

Improved modelling of breast cancer cells using machine learning heuristic algorithms حسب Maria Sajjad (19737807)

"…This approach also considers the different properties and responses of immune cells. Our validated results, obtained using metaheuristic algorithms and sensitivity analysis, provide significant insights into the correlation between the advancement of breast cancer and the innate and adaptive immune systems. …"

ML algorithms used in this study. حسب Toheeb Salahudeen (21368040)

Flowchart of faults identification and location algorithms. حسب Mohammed Youssef (8691402)

Algorithm training accuracy experiments. حسب Yingying Liu (360782)

Kappa coefficients for different algorithms. حسب Yingying Liu (360782)

Notations and definitions used in the algorithm. حسب Nazym Alimbekova (22766158)

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)

"…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. 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 حسب 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. 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 حسب 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. 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 حسب 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. 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. …"