The user can interact with the results page to view more detailed information: Steps 3 and 4 are repeated five times, according to the 5-fold CV setting. Thus, a negative correlation for a particular feature suggests that the feature is important for TF1, while a negative correlation suggests that the feature is important for TF2. The latter measure represents the average decrease in classification accuracy when the values of the respective variable are randomly permuted Then, the test data is used for prediction and evaluation. Third, some paralogous TFs are expressed in different cells or at different stages during cellular differentiation or during the cell cycle; in such cases, the precise chromatin environment in the cell where each paralogous TF is expressed will dictate where the TF binds in the genome. If the user provides FASTA files, COUGER will consider that the classes are predetermined and will start with Step 2 Figure 1 , so only the nucleotide sequences will be read we note that in the absence of genomic coordinates, it is much harder and time-consuming to identify the overlapping sequences between the two sets. COUGER takes as input two sets of genomic regions bound by paralogous TFs, and it identifies a small set of putative co-factors that best distinguish the two sets of sequences. This procedure also allows us to avoid the MI's bias toward features with larger sets of possible values.
Output After submission and validation, the user is redirected to a status page, where job details and the running log are provided. Feature selection One of the most important steps of our classification approach is feature selection, because we expect only a small number of TFs to be potential co-factors and interact with the considered paralogous TFs. Second, paralogous TFs may interact with different protein co-factors that modulate their DNA binding specificity 5 , or they may respond differently to certain chromatin environments. In order to avoid a potential classification bias toward one of the two classes, an equal number of DNA sequences from each set is selected. Performance evaluation We evaluate the performance of our classification models using a 5-fold cross-validation CV approach. Third, some paralogous TFs are expressed in different cells or at different stages during cellular differentiation or during the cell cycle; in such cases, the precise chromatin environment in the cell where each paralogous TF is expressed will dictate where the TF binds in the genome. We note that we tested several discretization approaches, as well as several feature selection algorithms 15 , 16 , 18—20 , and we found that NMIFS with discretized features achieved the highest classification accuracy data not shown. However, individual TF family members i. We use different classifiers in order to assess the reliability of the results and their independence of particular techniques. Despite the large amount of in vivo ChIP-seq data currently available, especially through the ENCODE project 3 , computational tools for analyzing differences between the genomic binding profiles of paralogous TFs are still lacking. When the job is completed, the results replace the status page, and an e-mail is sent to the user if an email address was provided. Step 3 illustrates the custom feature selection procedure. For each set of features and each type of classifier, COUGER computes median values for accuracy, sensitivity, specificity and precision, which are reported in separate files. After the feature selection step, classification is performed on five feature sets: The identified co-factors are presented in a user-friendly output page, together with information that allows the user to understand and to explore the contributions of individual co-factor features. Each set is specified in a separate, uncompressed file. Here, we present an interactive web implementation of COUGER, a classification-based framework for identifying protein co-factors that might provide specificity to paralogous TFs. The current version of the web server offers six choices of such features for human and mouse data: Only sequences in the training set are used during feature selection, to ensure the complete independence of the test set, which is used only to evaluate the performance of our classification models. The median classification accuracies before and after feature selection are displayed in a heatmap-like color coded table with values for each type of algorithm and each set of features Figure 2 A. This procedure also allows us to avoid the MI's bias toward features with larger sets of possible values. In the case of a BED-like format, the user may choose the reference genome among five different versions of the human genome, four versions of the mouse genome and three versions of the fly genome. Features reflecting the binding specificity of putative co-factors are computed from: Both algorithms are highly accurate, can successfully handle high-dimensional data and are robust on data with highly correlated features. We present an extensive web implementation of our recently published algorithm COUGER co-factors associated with uniquely-bound genomic regions 6 , a classification-based framework for identifying protein co-factors that might provide specificity to paralogous TFs. SVM is a non-probabilistic binary linear classifier with great performance on both linear and nonlinear classification problems.
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Cougar Culture - MGTOW
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