MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM.

By Kuo-Chen Chou, Hongbin Shen. Biochemical and Biophysical Research Communications - BBRC, 2007

428 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ad9ed439d8caa56314caccdab64f049097e35ce0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Kuo-Chen Chou
2. Hongbin Shen
3. Bin Liu
4. Longyun Fang
5. Ren Long
6. Xun Lan
7. Xuan Xiao
8. Hao Lin

Citations in the corpus

• Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology (2009, background): “The concept of PseAAC was also be extended to cover the sequential evolution information for predicting protein membrane protein type [54], enzyme functional classes [53], protease types [178,179], and protein quaternary structural attributes [70].”
• REVIEW : Recent advances in developing web-servers for predicting protein attributes (2009, methodology): “Recently, a user-friendly web-server predictor called “ MemType-2L ” was developed [54].”
• iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition (2016, methodology): “To make the prediction method not only able to identify whether a DNA sample is an enhancer but also make it able to identify the strength type, here we are to develop a two-layer predictor similar to the treatment in identifying membrane proteins and their types (Chou and Shen, 2007a,b).”
• Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization (2010, background): “did not contain the functional domain and sequential evolution informations, which have been proved to play an important role in enhancing the prediction quality of other protein attributes (see, e.g.,”
• Recent progress in protein subcellular location prediction. (2007, neutral): “[22,23,26,31,33,36,37, 54,61–67,87–91]).”
• The in silico human surfaceome (2018, background): “(iii) Subcellular localization of a protein can to some extent be predicted by computational methods (11).”
• Some remarks on protein attribute prediction and pseudo amino acid composition (2010, methodology): “Accordingly, protein samples defined in a GO database space would be clustered in a way better reflecting some of their important attributes, such as subcellular localization and biological function (Chou and Shen, 2007c, 2008b).”
• Multitask Learning for Protein Subcellular Location Prediction (2011, neutral): “Index Terms Protein subcellular localization; multitask learning.”