An Alignment-Free Algorithm in Comparing the Similarity of Protein Sequences Based on Pseudo-Markov Transition Probabilities among Amino Acids.

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An Alignment-Free Algorithm in Comparing the Similarity of Protein Sequences Based on Pseudo-Markov Transition Probabilities among Amino Acids.

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2016 nî lūn-bûn

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2016年の論文

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2016年論文

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2016年論文

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2016年論文

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2016年論文

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2016年論文

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An Alignment-Free Algorithm in ...... obabilities among Amino Acids.

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An Alignment-Free Algorithm in ...... obabilities among Amino Acids.

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An Alignment-Free Algorithm in ...... obabilities among Amino Acids.

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An Alignment-Free Algorithm in ...... obabilities among Amino Acids.

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An Alignment-Free Algorithm in ...... obabilities among Amino Acids.

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An Alignment-Free Algorithm in ...... obabilities among Amino Acids.

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P1476

An Alignment-Free Algorithm in ...... robabilities among Amino Acids

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P2093

Jiasheng Yang

http://www.w3.org/2001/XMLSchema#string

Yi Zhang

http://www.w3.org/2001/XMLSchema#string

Yushuang Li

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Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM.

An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms.

A new sequence distance measure for phylogenetic tree construction.

Prediction of G-protein-coupled receptor classes in low homology using Chou's pseudo amino acid composition with approximate entropy and hydrophobicity patterns.

P304

e0167430

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scholarly article

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10.1371/JOURNAL.PONE.0167430

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P407

English

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P478

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Jialiang Yang

Tian Song

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2016-12-05T00:00:00Z

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27918587

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5137889

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