DataSheet1_SS-RNN: A Strengthened Skip Algorithm for Data Classification Based on Recurrent Neural Networks.pdf by Wenjie Cao (780652)

Subtype classification of AMI patients based on hub genes. by Nan Huang (464390)

DataSheet1_Identification of a ferroptosis-related gene signature predicting recurrence in stage II/III colorectal cancer based on machine learning algorithms.DOCX by Ze Wang (132986)

“…We developed a machine learning framework with 83 combinations of 10 algorithms based on 10-fold cross-validation (CV) or bootstrap resampling algorithm to identify the most robust and stable model. …”

Most informative genes for predicting cell-line responses for Gefitinib. by Sofia P. Miranda (11418930)

Summary of datasets used. by Stephen R. Piccolo (8367780)

Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures by Dibyajyoti Das (14845321)

Subjects:

Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures by Dibyajyoti Das (14845321)

Subjects:

Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures by Dibyajyoti Das (14845321)

Subjects:

Simulated Design–Build–Test–Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering by Paul van Lent (16876977)

“…In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. …”

Table1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.xlsx by Yu Yin (329063)

“…In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes.…”

Image1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.pdf by Yu Yin (329063)

“…In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes.…”

Table2_Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules.XLSX by Emna Harigua-Souiai (4705753)

Table4_Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules.XLSX by Emna Harigua-Souiai (4705753)

Table5_Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules.XLSX by Emna Harigua-Souiai (4705753)

Table1_Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules.XLSX by Emna Harigua-Souiai (4705753)

Table3_Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules.XLSX by Emna Harigua-Souiai (4705753)

Image1_Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules.PNG by Emna Harigua-Souiai (4705753)

Image_2_A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.TIF by Qingqing Li (1505614)

“…First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. …”

Image_1_A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.TIF by Qingqing Li (1505614)

“…First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. …”

Image_3_A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.TIF by Qingqing Li (1505614)

“…First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. …”