Anaconda3
Anaconda3 is a free and open-source distribution of the Python programming language for scientific computing, data science, and machine learning. It includes a curated collection of over 1,500 data science packages and the Conda package and environment management system.
Lab products found in correlation
10 protocols using Anaconda3
Hierarchical Clustering of PD Patients
Automated Liquid Chromatography Protocol
Python 3.9 using PyCharm 2021.1.2 (JetBrains, Prague, Czech Republic).
The Python environment was set up using Anaconda 3 (Anaconda Inc.,
Austin, TX, USA). To interface with the LC instrument, an algorithm
was written in C++ using Microsoft Visual Studio 2022 (Microsoft,
Redmond, WA, USA) to interface with the OpenLAB CDS Chemstation Edition
(rev. C.01.10 [287]). For retention modeling, the AutoLC algorithm
and signal processing was done in Python 3.9 using PyCharm 2021.1.2
and MATLAB 2021b (Mathworks, Natick, MA, USA), which was used for
the peak detection, tracking, and optimization algorithms, whereas
peak detection was supported by the findpeaks MATLAB function.31 To monitor progress, the AutoLC algorithm was
programmed to report its status and data continuously in Slack 4.22
(San Francisco, CA, USA) using the Python Slack SDK.
Bioinformatic Analysis of Enzyme Kinetics
Automated Image Processing and Analysis
LOD values were defined as follows: Output values obtained with different concentrations of tgRNA were fitted to a linear curve (the output values correspond to the number of positive chambers and the fluorescence intensities in SATORI and the plate reader-based method, respectively). The means + 3 S.D. for output values obtained without tgRNA were measured, and the crossing point of the linear curve and the mean + 3 S.D. value was determined. The concentration corresponding to the crossing point was defined as the LOD value.
Detailed Data Analysis Protocol
Soil Organic Carbon Variation Factors
Automated Data Processing for Regression Analysis
Artificial Neural Network Pharmacokinetic Model
n represents the number of training data items.
Coagulopathy and Inflammation in COVID-19
Identifying Key Genes in Rheumatoid Arthritis
The key RA gene sets based on PPI network analysis and feature extraction were intersected again to obtain the core gene set of RA. To verify the disease classification effect of these core genes, the three different machine learning classifiers mentioned above were used to predict RA classification, which was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) value. All classification prediction analyses were performed on the Anaconda3 (https://www.anaconda.com/).
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