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Cross-React
Enables prediction of cross-reactivity between distantly related allergenic proteins using their X-ray or homology modeled structures in combination with epitope analysis. Cross-React uses a patch analysis, solvent accessible surface area of amino acids, and structural similarity between amino acids in the epitope region of a query allergen and allergens in the SDAP database. The search results are ranked based on the calculated Pearson correlation coefficient (PCC) between the amino acid composition in the query epitope and the accessible surface patches on the target allergens. Cross-React can be used as a predictive tool to assess protein allergenicity and cross-reactivity.
AllergenFP / Allergen FingerPrint
An alignment-free method for allergenicity prediction, based on amino acid principal properties as hydrophobicity, size, relative abundance, helix and β-strand forming propensities. AllergenFP transforms proteins into descriptor-based fingerprints and compares them by Tanimoto coefficient. The algorithm was optimized in terms of lag length and resolution step and cross-validated by a set of 2427 known allergens and 2427 non-allergens. It recognized 87% of the allergens and 89% of the non-allergens. AllergenFP was compared with five freely available web servers for allergenicity prediction and showed the highest predictive ability.
AllerTOP
A bioinformatics tool for allergenicity prediction. AllerTOP is based on amino acid descriptors, accounting for residue hydrophobicity, size, abundance, helix- and β-strand forming propensities. The protein strings were transformed into uniform vectors by auto- and cross-covariance and a machine learning method using k nearest neighbours was used to classify allergens and non-allergens. The comparison between several servers for allergen prediction indicates that AllerTOP v.2 has the highest accuracy. AllerTOP v.2 offers a useful, robust, and strongly complimentary approach to allergen prediction that should provide researchers with important and persuasive new approach to identifying allergens in both existing and newly developed materials.
Allerdictor
Obsolete
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A fast and accurate sequence-based allergen prediction tool that models protein sequences as text documents and uses support vector machine in text classification for allergen prediction. Test results on multiple highly skewed datasets demonstrated that Allerdictor predicted allergens with high precision over high recall at fast speed. For example, Allerdictor only took approximately 6 min on a single core PC to scan a whole Swiss-Prot database of approximately 540 000 sequences and identified <1% of them as allergens.
AllerHunter
Obsolete
A cross-reactive allergen prediction program built on a combination of support vector machine (SVM) and pairwise sequence similarity. Cross-reactivity is based on similarity of proteins to allergens. However, not all proteins with similar sequence or structure to known allergens are cross-reactive allergens. AllerHunter aims to predict allergens and non-allergens with high sensitivity and specificity, without compromising efficiency at classification of proteins with similar sequence to known allergens.
AllerTool
Obsolete
A web server with essential tools for the assessment of predicted as well as published cross-reactivity patterns of allergens. AllerTool includes graphical representation of allergen cross-reactivity information; a local sequence comparison tool that displays information of known cross-reactive allergens; a sequence similarity search tool for assessment of cross-reactivity in accordance to FAO/WHO Codex alimentarius guidelines; and a method based on support vector machine (SVM).
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