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Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition.

, , , , , , , , , and . BMC Bioinform., 13 (S-17): S3 (2012)

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Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition., , , , , , , , , and . BMC Bioinform., 13 (S-17): S3 (2012)PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning., , , , , and . Comput. Biol. Medicine, (May 2023)NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides., , , , , and . Comput. Biol. Medicine, (2022)DNA Microarray Data Clustering by Hidden Markov Models and Bayesian Information Criterion., , , , and . ADMA, volume 4093 of Lecture Notes in Computer Science, page 827-834. Springer, (2006)Designing predictors of halophilic and non-halophilic proteins using support vector machines., , , , and . CIBCB, page 230-237. IEEE, (2013)Predicting protein crystallization using a simple scoring card method., , , , , and . CIBCB, page 23-30. IEEE, (2013)SCMPSP: Prediction and characterization of photosynthetic proteins based on a scoring card method., , , , , and . BMC Bioinform., 16 (S-1): S8 (2015)StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens., , and . BMC Bioinform., 24 (1): 301 (December 2023)SETAR: Stacking Ensemble Learning for Thai Sentiment Analysis Using RoBERTa and Hybrid Feature Representation., and . IEEE Access, (2023)Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides., , , , , and . J. Comput. Aided Mol. Des., 36 (11): 781-796 (2022)