Markov mean properties for cell death-related protein classification

GENERATING FUZZY RULES FOR PROTEIN CLASSIFICATION

This paper considers the generation of some interpretable fuzzy rules for assigning an amino acid sequence into the appropriate protein superfamily. Since the main objective of this classifier is the interpretability of rules, we have used the distribution of amino acids in the sequences of proteins as features. These features are the occurrence probabilities of six exchange groups in the seque...

Markov Models for Classification of Protein Helices

Producing Soluble Human Programmed Cell Death Protein-1: A Natural supporter for CD4+T cell Cytotoxicity and Tumor Cells Apoptosis

Background: Programmed cell death protein-1 (PD-1)/PD-L1 pathway is one of the immune checkpoint pathways involved in regulation of the immune responses and suppression of anti-tumor defense. PD-1/B7-H1-blocking antibodies improve immune responses such as cytotoxic activity of CD8+/CD4+T cells and also increase mortality of tumor cells; however their use is accompanied by adverse effects in pat...

Classification of Protein Sequences Using Markov Models Classification of Protein Sequences Using Markov Models

Preface This study was completed as a master thesis at the institute of Informatics and Mathematical Modeling (IMM)-Technical University of Denmark (DTU). The project has been completed in the period between September 1 st 2003 and Marts 1 st 2004 (and is rated as 35 points). Associate Professor Paul Fischer also IMM, DTU has been supervisor for the project. The report deals with a general clas...

Protein Family Classification Using Sparse Markov Transducers

We present a method for classifying proteins into families based on short subsequences of amino acids using a new probabilistic model called sparse Markov transducers (SMT). We classify a protein by estimating probability distributions over subsequences of amino acids from the protein. Sparse Markov transducers, similar to probabilistic suffix trees, estimate a probability distribution conditio...