Reward function related parameters. by Honglei Pang (22693724)

Subjects:

Identification and functional analysis of hub genes. by Wei Ya Lan (22403712)

“…(C, D) Top 10 hub genes identified using the Maximal Clique Centrality (MCC) algorithm; darker colors indicate higher centrality within the PPI network. …”

Data from "Ecological clusters of soil taxa within bipartite networks are highly sensitive to climatic conditions in global drylands" by David sánchez (8256864)

Co-simulation architecture. by Honglei Pang (22693724)

Subjects:

Algorithm for generating virtual patients. by Adrianne L. Jenner (11133854)

“…<b>4)</b> Optimizing the objective function provides a parameter set for which the patient response to SARS-CoV-2 will be within the physiological ranges. …”

Conservation and functional variation across TcdB subtypes. by Michael J. Mansfield (9922934)

Function graph and algorithm iterative graph. by WanRu Zhao (18980374)

Subjects:

Our algorithms process large data sets within a minute. by Manu Aggarwal (15990691)

Results of proposed algorithms and algorithms in [9] for 18 instances. by Jin Zhang (53297)

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

A component overlapping attribute clustering (COAC) algorithm for single-cell RNA sequencing data analysis and potential pathobiological implications by He Peng (6377654)

“…In this study, we developed a Component Overlapping Attribute Clustering (COAC) algorithm to perform the localized (cell subpopulation) gene co-expression network analysis from large-scale scRNA-seq profiles. …”

Activation functions: Sigmoid function and ReLU function. by Wan-Hua Zhang (21601139)

Illustration of methods based on Green’s function first-passage algorithms. by Jean Marc Tregan (15195173)

Optimization flow of the co-simulation method. by Shangyang Jin (21772466)

“…Thus, a combination algorithm optimization strategy for metamaterials in terms of multiple structural parameters is proposed in this paper based on a co-simulation approach. …”

APSO–CNN and FNN algorithms’ loss function training sets. by Ahmed Bahaa (12887063)

Subjects:

APSO–CNN and FNN algorithms’ loss function validation sets. by Ahmed Bahaa (12887063)

Subjects:

Unraveling of Phosphotyrosine Signaling Complexes Associated with T Cell Exhaustion Using Multiplex Co-Fractionation/Mass Spectrometry by Wei Liu (20030)

“…T cell exhaustion, characterized by the upregulation of inhibitory receptors and loss of effector functions, plays a crucial role in tumor immune evasion. …”

Unraveling of Phosphotyrosine Signaling Complexes Associated with T Cell Exhaustion Using Multiplex Co-Fractionation/Mass Spectrometry by Wei Liu (20030)

“…T cell exhaustion, characterized by the upregulation of inhibitory receptors and loss of effector functions, plays a crucial role in tumor immune evasion. …”

Unraveling of Phosphotyrosine Signaling Complexes Associated with T Cell Exhaustion Using Multiplex Co-Fractionation/Mass Spectrometry by Wei Liu (20030)

“…T cell exhaustion, characterized by the upregulation of inhibitory receptors and loss of effector functions, plays a crucial role in tumor immune evasion. …”

Unraveling of Phosphotyrosine Signaling Complexes Associated with T Cell Exhaustion Using Multiplex Co-Fractionation/Mass Spectrometry by Wei Liu (20030)

“…T cell exhaustion, characterized by the upregulation of inhibitory receptors and loss of effector functions, plays a crucial role in tumor immune evasion. …”