Detects antifreeze proteins (AFP) from sequence information. AFP-Pred utilizes sequence derived properties from protein primary sequence to predict AFPs with more than eighty per cent of accuracy. This software can be also used to identify other specific functional properties in the domain of large-scale and high-throughput analysis of genomic and proteomic data.
Assists users to detect antifreeze proteins (AFPs). The AFP-PseAAC consists of support vector machines-based predictor that uses Chou's pseudo amino acid composition for identifying AFP. This approach uses a classifier for solving biological challenges related to prediction, classification and regression. It can be used in AFP based research for biotechnological applications.
Detects fast in-silico antifreeze proteins (AFP). afpCOOL makes use of four features normalized by the length of the sequence to encode each protein: hydropathy, amino acid composition, physicochemical properties and evolutionary profile. This software works with evolutionary information of a protein in the form of position-specific scoring matrix (PSSM). It works with the trained support vector machine (SVM)-based model to evaluate the AFP or non-AFP label of the queries.
Assists users to perform localized analysis of antifreeze proteins (AFPs) sequences. RAFP-pred processes the localized segments of the AFP sequences: each protein sequence is segmented into two sub-sequences; and each subsequence is individually analyzed for amino acid and di-peptide compositions.
Predicts the Antifreeze protein. iAFP-Ense is a protector that can become a useful high-throughput software for both basic research and drug development. This random forests algorithm was adopted to conduct prediction using each descriptor features and the final result was gotten by integrating all the random forests results via voting. To obtain the predicted result with the anticipated success rate, the entire sequence of the query protein rather than its fragment should be used as an input.
Serves for classifying antifreeze proteins (AFPs) from non-antifreeze proteins (non-AFPs). CryoProtect is a web application for classifying query protein sequence and represents a computational predictor for AFPs, based on a random forest classifier. Users have to enter its protein sequence, either by inserting a sequence manually or by uploading a FASTA file.
Assists users to predict antifreeze proteins using a support vector machine (SVM) and position specific scoring matrix (PSSM) profiles. AFP PSSM represents an antifreeze protein predictor and uses the feature PSSM-400 as the input of support vector machine (SVM). Users can perform different types of analysis online, and make queries for small and large protein sequences.
0 - 0 of 0
1 - 4 of 4