Here, we describe a systematic computational framework based on a classification model for identifying genetic interactions using high-dimensional single-cell morphological data from genetic screens, apply it to RhoGAP/GTPase regulation in Drosophila, and evaluate its efficacy.
Because these results are based on a classification model, it is important to make sure that what we see in Fig. 2d is not due to bias in the way Botometer was trained that the model did not simply learn to assign higher scores to more active accounts.
According to the following three dimensions, a decision is classified as informed or uninformed based on the classification model of Marteau et al 8: knowledge (sufficient/insufficient), attitude (positive/negative) and intention/implementation (yes/no).
The building and testing of comparison model based on the classification model framework can be accomplished using the following steps: 1) Sample positive and negative target genes data as a training model of the data set, from positive target genes (788 groups) and negative target genes (4000 groups) randomly, with total of 4788 target genes.
Since predictions are carried out based on a classification model that is built upon a training data set extracted from two consecutive snapshots of the concept network, the performance of link discovery can be evaluated by measures such as classification accuracy, recall, and precision as results of n-fold cross validation on the training data.
And, p-value denotes the probability at which one candidate was classified as a positive hit by mistake based on the SVM classification model.
One would therefore choose a specificity close to one and could read from the ROC curve the achievable sensitivity based on the underlying classification model.
Our classification model is based on Support Vector Machine (SVM).
Three levels of risk debates, modified from Renn (1992), based on the knowledge classification model by Funtowicz and Ravetz (1985).
In this paper, we propose a fault diagnosis methodology based on a new classification model called Quantum Clustering based Multi-valued Quantum Fuzzification Decision Tree (QC-MQFDT).
