All networks were implemented in Tensorflow1.10 (NVIDIA, Santa Clara, CA, USA) and evaluated on a computer with an NVIDIA GeForce TITAN XP GPU. All statistical analyses were performed using the statistical software SPSS Statistics 26.0 (IBM, Armonk, NY, USA).
Geforce titan xp gpu
Manufactured by NVIDIA
Sourced in United States
The NVIDIA Geforce Titan Xp is a high-performance graphics processing unit (GPU) designed for demanding professional and enthusiast applications. The Titan Xp features 3,840 CUDA cores, 12GB of GDDR5X memory, and a 384-bit memory interface, delivering exceptional graphics processing power and memory bandwidth.
Automatically generated - may contain errors
Lab products found in correlation
4 protocols using geforce titan xp gpu
1
GPU-Accelerated Neural Network Evaluation
2
Benchmarking Machine Learning Models for Proteoform RT Prediction
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A total of
eight machine learning models were assessed for predicting proteoform
RTs in top-down RPLC–MS: LR, RFR, SVR, the model in GPTime,26 (link) an FNN model, the CNN + capsule model in DeepRT+,30 (link) the GRU + FNN model in Prosit,32 (link) and the CNN + LSTM + FNN model in DeepDIA.34 (link) The last four models and the semi-empirical model in the
study by Chen et al.41 (link) were also benchmarked
for predicting proteoform MTs in top-down CZE-MS. All the models were
implemented in Python (version 3.6.8). The FNN and CNN + capsule models
were implemented using the PyTorch package (version 1.18.1)45 and the GRU + FNN and CNN + LSTM + FNN models
using the Keras package (version 2.1.1)46 with the TensorFlow backend (version 1.14.0). The machine learning
models were trained on a computer with an Intel Xeon 2.20 GHz 10 core
CPU, 192 GB memory, and an Nvidia Geforce Titan Xp GPU running the
Ubuntu 18.04 operating system.
eight machine learning models were assessed for predicting proteoform
RTs in top-down RPLC–MS: LR, RFR, SVR, the model in GPTime,26 (link) an FNN model, the CNN + capsule model in DeepRT+,30 (link) the GRU + FNN model in Prosit,32 (link) and the CNN + LSTM + FNN model in DeepDIA.34 (link) The last four models and the semi-empirical model in the
study by Chen et al.41 (link) were also benchmarked
for predicting proteoform MTs in top-down CZE-MS. All the models were
implemented in Python (version 3.6.8). The FNN and CNN + capsule models
were implemented using the PyTorch package (version 1.18.1)45 and the GRU + FNN and CNN + LSTM + FNN models
using the Keras package (version 2.1.1)46 with the TensorFlow backend (version 1.14.0). The machine learning
models were trained on a computer with an Intel Xeon 2.20 GHz 10 core
CPU, 192 GB memory, and an Nvidia Geforce Titan Xp GPU running the
Ubuntu 18.04 operating system.
3
Benchmarking ML Models for Proteoform RT and MT Prediction
4
Deep Learning Model Development for Image Analysis
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The software used to develop the DCNN model was based on the Ubuntu 16.04 operating system and included TensorFlow 1.9, Keras 2.1.4 and the open-source programme Python 3.6.5 (The Python Software Foundation). The training was conducted on an Intel Core I7-7740X CPU 4.30 GHz with an NVIDIA GeForce TITAN Xp GPU. All statistical analyses were performed using the Python packages statsmodels, pymc, pylab, sklearn and seaborn.
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