2017
DOI: 10.1016/j.compbiolchem.2017.04.003
|View full text |Cite
|
Sign up to set email alerts
|

Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images

Abstract: Protein structure prediction and analysis are more significant for living organs to perfect asses the living organ functionalities. Several protein structure prediction methods use neural network (NN). However, the Hidden Markov model is more interpretable and effective for more biological data analysis compared to the NN. It employs statistical data analysis to enhance the prediction accuracy. The current work proposed a protein prediction approach from protein images based on Hidden Markov Model and Chapman … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
6
0

Year Published

2017
2017
2020
2020

Publication Types

Select...
5
4

Relationship

1
8

Authors

Journals

citations
Cited by 24 publications
(6 citation statements)
references
References 69 publications
0
6
0
Order By: Relevance
“…The work described in [ 18 ] focused on cancer disease and applied several different machine learning methods for data reduction and coding area selection, which is considered as key area for discovering the desired medicine. The research described in [ 14 , 17 ] can be used for extracting drug-disease relations, which aim to predict the primary, secondary and tertiary protein structure and to handle large volume biological datasets.…”
Section: Introductionmentioning
confidence: 99%
“…The work described in [ 18 ] focused on cancer disease and applied several different machine learning methods for data reduction and coding area selection, which is considered as key area for discovering the desired medicine. The research described in [ 14 , 17 ] can be used for extracting drug-disease relations, which aim to predict the primary, secondary and tertiary protein structure and to handle large volume biological datasets.…”
Section: Introductionmentioning
confidence: 99%
“…[11], [14] For decades, the literature in poetry generation and the application of Hidden Markov Model in the computational creativity and natural language processing is still uprising and for many years it was already proven that Hidden Markov Model was suitable and efficient to use for text generation. One examples of this are; Polish language text generator [11] Steganographic text based [12] and Chinese couplets generation [13] However, researchers strongly recommended to explore more on the type of testing and what type of other evaluation metrics can be more suitable to evaluate the performance of the HMM model [6], [7]. For this purpose, we explored and used the other evaluation metrics used in computational creativity to evaluate our HMM model.…”
Section: Related Workmentioning
confidence: 99%
“…In this paper, we tested the learning ability of the hidden markov based on the number of iterations performed before it produces a high quality machine generated poem. [6], [7] Then we evaluated the content of the generated poem by getting the readability score index. And lastly, we performed a standard Turing Test, to confirm the validity and authenticity of the generated poems.…”
Section: Introductionmentioning
confidence: 99%
“…Given {H k } , the O k is conditionally independent and the conditional distribution of it relies on {H k } only through H n . The (HMMs) have many applications in different fields such as speech recognition [1], hand gesture recognition [2], source coding [3], seismic hazard assessment [4], traffic prediction [5],wireless network [6][7][8], protein structure prediction [9] and finance [10]. The semi-hidden Markov models (SHMMs) are stochastic models related to HMMs.…”
Section: Introductionmentioning
confidence: 99%