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

Kamal, Masud; Chowdhury, Linkon; Khan, Mohammad Ibrahim; Ashour, Amira S.; Tavares, João Manuel R. S.; Dey, Nilanjan

doi:10.1016/j.compbiolchem.2017.04.003

Cited by 24 publications

(6 citation statements)

References 69 publications

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“…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%

Realizing drug repositioning by adapting a recommendation system to handle the process

et al. 2018

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BackgroundDrug repositioning is the process of identifying new targets for known drugs. It can be used to overcome problems associated with traditional drug discovery by adapting existing drugs to treat new discovered diseases. Thus, it may reduce associated risk, cost and time required to identify and verify new drugs. Nowadays, drug repositioning has received more attention from industry and academia. To tackle this problem, researchers have applied many different computational methods and have used various features of drugs and diseases.ResultsIn this study, we contribute to the ongoing research efforts by combining multiple features, namely chemical structures, protein interactions and side-effects to predict new indications of target drugs. To achieve our target, we realize drug repositioning as a recommendation process and this leads to a new perspective in tackling the problem. The utilized recommendation method is based on Pareto dominance and collaborative filtering. It can also integrate multiple data-sources and multiple features. For the computation part, we applied several settings and we compared their performance. Evaluation results show that the proposed method can achieve more concentrated predictions with high precision, where nearly half of the predictions are true.ConclusionsCompared to other state of the art methods described in the literature, the proposed method is better at making right predictions by having higher precision. The reported results demonstrate the applicability and effectiveness of recommendation methods for drug repositioning.

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Section: Introductionmentioning

confidence: 99%

Realizing drug repositioning by adapting a recommendation system to handle the process

et al. 2018

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“…[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%

Short Poem Generation (SPG): A Performance Evaluation of Hidden Markov Model based on Readability Index and Turing Test

Tarnate¹,

Garcia²,

Sotelo-Bator³

2020

IJACSA

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We developed a Hidden Markov Model (HMM) that automatically generates short poem. The HMM was trained using the forward-backward algorithm also known as Baum Welch algorithm. The training process was exhausted by a hundreds of iterations through recursion method. Then we used the Viterbi algorithm to decode all the best possible hidden states to predict the next word, and from the previous predicted word, it will generate another word, then another word until it reaches the desire word length that was set in the program. Afterwards, the model was evaluated using several kinds of readability metrics index which measure the reading difficulty and comprehensiveness of the generated poem. Then, we performed a Turing Test, which participated by 75 college students, who are well versed in poetry. They determined if the generated poems was created by a human or a machine. Based from the evaluation results, the highest readability score index of the generated short poem is in the grade 16 th level. While 69.2% of the participants in the Turing Test, agreed that most of the machine generated poems were likely created by some well-known poets and writers.

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“…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%

An application of a semi-hidden Markov model in wireless communication systems

2019

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Stochastic processes are approved presentation of real systems which its development in space or time can be supposed as random. A semi-hidden Markov model as a type of stochastic processes is a modification of hidden Markov models with states that are no longer totally unobservable and are less hidden. This mathematical model is employed for modeling data sequences with long runs, memory and statistical inertia. In this article, we investigate the theory of the semi-hidden Markov model along with its parameter estimation and order estimation methods. Moreover, the proposed model is applied to model the error traces generated by the wireless channels. A new Markov-based trace analysis algorithm is suggested to divide a non-stationary network error trace into stationary parts. By means of the best semi-hidden Markov model and fitting probability distribution, we would be able to model these parts accurately. Calculating the information measure criteria and the autocorrelation function by running the modified Baum-Welch algorithm several times help us to find the optimal order of the semi-hidden Markov model.

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

Cited by 24 publications

References 69 publications

Realizing drug repositioning by adapting a recommendation system to handle the process

Realizing drug repositioning by adapting a recommendation system to handle the process

Short Poem Generation (SPG): A Performance Evaluation of Hidden Markov Model based on Readability Index and Turing Test

An application of a semi-hidden Markov model in wireless communication systems

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