Illustration of the unrestricted model and its training algorithm. by Yuru Song (21300760)

Algorithmic image feature identification. by Alex T. Keeley (16612727)

Rule-in and rule-out criteria provided by the machine learning algorithm. by Stephan Ellmann (314565)

Subjects: “…Cell Biology…”

The pseudocode for the NAFPSO algorithm. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

PSO algorithm flowchart. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

Identification of recurrent synapses by the smooth-index algorithm. by Alexander Borst (32619)

Subjects:

Table_1_Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas.xlsx by Yang Li (7082)

“…Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. …”

Table_5_Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas.xlsx by Yang Li (7082)

“…Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. …”

Table_6_Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas.xlsx by Yang Li (7082)

“…Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. …”

Image_5_Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas.pdf by Yang Li (7082)

“…Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. …”

Table_4_Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas.xlsx by Yang Li (7082)

“…Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. …”

Table_7_Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas.xlsx by Yang Li (7082)

“…Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. …”

Image_4_Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas.jpeg by Yang Li (7082)

“…Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. …”

Table_2_Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas.xlsx by Yang Li (7082)

“…Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. …”

Image_3_Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas.jpeg by Yang Li (7082)

“…Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. …”

Image_1_Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas.jpeg by Yang Li (7082)

“…Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. …”

Table_3_Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas.xlsx by Yang Li (7082)

“…Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. …”

Image_2_Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas.jpeg by Yang Li (7082)

“…Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. …”

UNCOVER: Functional complementarity of alterations discovery. by Rebecca Sarto Basso (6728921)

Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”