In this study, the breast cancer-related GWAS dataset comprising 528,173 SNPs was used to detect genetic epistasis. All the SNPs with a missing genotype rate < 0.1, MAF > 0.05, a Hardy-Weinberg equilibrium (HWE) p > 0.001, and a pair-wise R2 < 0.8 were retained. In total, 498,847 SNPs were used for detecting genetic epistasis (available upon request). The eight most widely cited genetic epistasis detection software packages, including pMDR [20 (link)], GBOOST [21 (link)], PLINK [22 (link)], FastEpistasis [23 (link)], SNPRuler [24 (link)], AntEpiSeeker [25 (link)], Ranger [26 (link)], and BEAM3 [27 (link)], were used to detect SNP-SNP interactions. All eight software packages were run using the default configuration on a machine with an Intel Xeon E5-2630 CPU and an Nvidia Quadro K2000 GPU.
Quadro k2000
Manufactured by NVIDIA
Sourced in United States
The Quadro K2000 is a professional graphics card designed for use in workstations. It features 2GB of GDDR5 video memory and supports a maximum resolution of 2560 x 1600 pixels.
Automatically generated - may contain errors
Lab products found in correlation
3 protocols using quadro k2000
1
Genetic Epistasis Detection in Breast Cancer
2
Deep Reinforcement Learning for Robotics
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Our DQN architecture
is similar to the dueling network architecture reported by Wang and
co-workers.29 Raw camera frames were used
as inputs to the neural network–based Q-function. An initial
random phase of 10 000 frames and an annealing phase of 135 000
frames (number of frames to change from 100% random actions to 0.05%
random actions) were used. Furthermore, the target network parameters
were updated every 5000 frames, storing and learning from only the
most recent 50 000 frames. A custom DQN version was used, implemented
in Python 2.7 using Keras47 and the Theano48 backend running on Windows 7 (Microsoft Corporation,
Redmond, USA). For training and inference of the ANN, a GPU (Quadro
K2000; Nvidia, Santa Clara, USA) was used. Finally, custom Python
scripts were used to post-process and visualize results.
is similar to the dueling network architecture reported by Wang and
co-workers.29 Raw camera frames were used
as inputs to the neural network–based Q-function. An initial
random phase of 10 000 frames and an annealing phase of 135 000
frames (number of frames to change from 100% random actions to 0.05%
random actions) were used. Furthermore, the target network parameters
were updated every 5000 frames, storing and learning from only the
most recent 50 000 frames. A custom DQN version was used, implemented
in Python 2.7 using Keras47 and the Theano48 backend running on Windows 7 (Microsoft Corporation,
Redmond, USA). For training and inference of the ANN, a GPU (Quadro
K2000; Nvidia, Santa Clara, USA) was used. Finally, custom Python
scripts were used to post-process and visualize results.
3
Crowding Task with Flanking Letters
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For the crowding task, the stimuli consisted of a target letter flanked by four tumbling Es on four cardinal sides of the target. The target letter was randomly drawn from a set of 10 Sloan letters: CDHKNORSVZ. All the letters were black on a uniform gray background (159 cd/m2) with a contrast of 99%, and a letter size of 0.8° (x-height). The fixation dot used in this experiment was a black circle in the center of the screen spanning 0.25° of the visual field. All stimuli were generated and controlled using MATLAB (version 8.3) and Psychophysics Toolbox extensions (version 3)64 (link),65 (link) for Windows 7, running on a PC desktop computer (model: Dell Precision Tower 5810). Stimuli were presented on a liquid crystal display monitor (model: Asus VG278HE; refresh rate: 144 Hz; resolution: 1920 × 1080, graphic card: 2 GB Nvidia Quadro K2000, subtending 60° × 34° visual angle at a viewing distance of 57 cm) with the mean luminance of the monitor at 159 cd/m2. The luminance of the display monitor was made linear using an 8-bit look-up table in conjunction with photometric readings from a MINOLTA LS-110 Luminance Meter (Konica Minolta Inc., Tokyo, Japan).
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