[PDF] Top 20 One-class support vector machines for protein-protein interactions prediction
Has 10000 "One-class support vector machines for protein-protein interactions prediction" found on our website. Below are the top 20 most common "One-class support vector machines for protein-protein interactions prediction".
One-class support vector machines for protein-protein interactions prediction
... The best results were found by the RBF kernel (Fig. 7 (c)). Even though, RBF kernel could give as low accuracy as 29% with unsuitable choice of parameters, it achieves around 80% with proper choice of parameters. These ... See full document
8
Predicting protein-protein interactions as a one-class classification problem
... identifying protein-protein interactions represents a crucial step toward understanding proteins ...predicting protein-protein interactions has gain a lot of ...using one ... See full document
8
False Positives Reduction in Top-down Protein Informatics using Support Vector Machines
... Our preliminary results show that SVM has a small yet certain effect on classification. Till now, we could not ascertain however, which size of feature table, out of a maximum of 19, would be the best suited for the best ... See full document
5
Software Fault Proneness Prediction Using Support Vector Machines
... both class-level and method-level static ...the class level, values of 10 metrics are computed including seven metrics given by Chidamber and Kemerer ... See full document
6
Automatic Prediction of Cognate Orthography Using Support Vector Machines
... of Support Vector Machines and their behaviour, which turned out to be quite useful when deciding which output should be classified as “Very ...extra class was added to the “No” class ... See full document
6
A bayesian kernel for the prediction of protein-protein interactions
... a protein in order to understand its ...predict protein-protein ...the support vector ...the support vector machine to predict protein-protein ... See full document
6
Protein binding affinity prediction using support vector regression and interfecial features
... the prediction of binding affinity as classification and regression problem with least-squared and support vector regression models using structure and sequence features of ...at protein ... See full document
6
Learning from positive examples when the negative class is undetermined- microRNA gene identification
... containing one or more single-stranded ...gene prediction using methods based on sequence conservation and/or structural similarity ...non-miRNA class based on the absence of features used to define ... See full document
9
Prediction of crystal packing and biological protein-protein interactions with Linear Dimensionality Reduction-SVD
... In pattern recognition feature extraction is a special form of dimensionality reduction meth- ods. When the input features for a classification is too large to be processed and consists of redundant data then the input ... See full document
107
Prediction of obligate and non-obligate protein-protein interactions
... the class types, every prediction method needs observed properties of the known class samples called ...feature vector is very large, we may need to apply feature extraction methods to find ... See full document
90
The role of electrostatic energy in prediction of obligate protein-protein interactions
... SVMs are well known machine learning techniques used for classification, regression and other tasks. The main goal of the SVM is to find a hyperplane that classifies all the feature vectors into two regions. In most ... See full document
12
FEATURE EXTRACTION TECHNIQUES USING SUPPORT VECTOR MACHINES IN DISEASE PREDICTION
... basis. Support Vector Machine is the most commonly used classification algorithm for disease prediction in healthcare ...to Support Vector Machine in order to get efficient ...disease ... See full document
8
Prediction of Drosophila melanogaster gene function using Support Vector Machines
... We attempted to compare our results with those from the study of Yan and co-workers [10], who also assigned GO terms to groups of similarly behaving genes to a dataset which in part included the data we used in our ... See full document
18
PREDICTION OF PLASMA PROTEIN BINDING AFFINITY BY SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORK
... Plasma Protein Binding plays a major role in ...distribution prediction by support vector statistical ...Plasma protein means low drug ...plasma protein means high drug ...plasma ... See full document
10
Prediction of DNA-binding protein based on statistical and geometric features and support vector machines
... predict protein-DNA ...original protein structures with the target ...the protein-DNA complex, and we extract statistical and geometric features including residue index, TM-score, curvature index, ... See full document
6
Support vector machines for predicting protein-protein interactions using domains and hydrophobicity features
... Protein interaction data can be obtained from the Database of Interacting Proteins (DIP; http://www.dip.doe- mbi.ucla.edu/). At the time of our experiments, the database comprises 15117 entries representing pairs ... See full document
5
Protein-dependent prediction of messenger RNA binding using Support Vector Machines
... the protein and oligonucleotides (oligos) of length 4 to encode the RNA ...A protein is encoded with 343 features by counting the (normalized) frequency of all occurring ...mainly interactions with ... See full document
89
Comparison of domain and hydrophobicity features for the prediction of protein-protein interactions using support vector machines
... The Support Vector Machines have been used as the learning ...non-interacting protein pairs using domain and hydrophobicity training ... See full document
7
Chunking with Support Vector Machines
... This data set consists of 20 sections (02-21) of the WSJ part of the Penn Treebank for the training data, and one section (00) for the test data. POS tags in this data sets are also anno- tated by the Brill ... See full document
8
Sparseness of Support Vector Machines
... Note, that the probability of the set on which the labels are not noise free is always greater than or equal to 2 R P . In the extreme case where the noise does not vanish on the entire set (e.g. when the conditional ... See full document
35
Related subjects