Table2_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX by Zhen Wang (72451)

“…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …”

Table1_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX by Zhen Wang (72451)

“…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …”

Flowchart of the FPA algorithm. by Yiqiong Gao (16698968)

“…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …”

Algorithm convergence curve. by Yiqiong Gao (16698968)

“…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …”

Flowchart of the IFPA algorithm. by Yiqiong Gao (16698968)

“…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …”

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

Algorithm results based on experimental likelihood functions. by Nicolas Herzig (9230425)

Loss function and accuracy curve comparison. by Dayu Zhang (710629)

Algorithm parameter settings. by Tao Dong (15551)

“…The results revealed that the improved multi-objective differential evolution algorithm in Zitzler-Deb-Thiele 1 function test had generational distance of 0.0513, inverted generational distance of 0.3265, and hyper volume metric of 0.1301. …”

Algorithm of the main experiment targeted to measure the perceptual point spread function (pPSF) treating patients visual system including its optics, physiology and psychology as an integrated imaging system, and patient’s perceptions as its output signal. by Krzysztof Petelczyc (3954203)

“…<p>In the algorithm, the following variables were used: “Ic” denotes the intensity of the central diode (Ic = 40 cd); “DIST(i)” is a randomly sorted list of “D” angular stimuli positions distributed equally as a function of distance from 0.24° to 7.67° from the central point (D = 10), while “i” is an index corresponding to the current distance of a probe diode (“d”); “N” denotes the number of trials for each stimuli position (N = 20); “s” denotes the perceptual brightness value transformed to diode luminous intensity by an array “I(s)” corresponds to the table “scale (level)” determined by the algorithm presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0306331#pone.0306331.g003" target="_blank">Fig 3</a>; “cnt” is a counter of trials for the current probe diode’s distance, array threshold (d), and slope (d), i.e., it denotes the intensity of the single point of the pPSF and its uncertainty. …”

PSO parameters and objective function. by André G. Coimbra (21354135)

GA parameters and objective function. by André G. Coimbra (21354135)

Objective function value statistics. by Chao Li (145513)

F4 function and convergence curve graph. by Jiangli Yu (4601986)

F5 function and convergence curve graph. by Jiangli Yu (4601986)

F1 function and convergence curve graph. by Jiangli Yu (4601986)

F6 function and convergence curve graph. by Jiangli Yu (4601986)

F7 function and convergence curve graph. by Jiangli Yu (4601986)

F10 function and convergence curve graph. by Jiangli Yu (4601986)