scholarly journals Unique protein features of SARS-CoV-2 relative to other Sarbecoviruses

2021 ◽  
Author(s):  
Matthew Cotten ◽  
David L. Robertson ◽  
My V.T. Phan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Virus Replication ◽  
Hidden Markov ◽  
Small Subset ◽  
Viral Agent ◽  
Unique Protein ◽  
Total Proteome ◽  
Small Set ◽  
Spike Analysis

Defining the unique protein features of SARS-CoV-2, the viral agent causing Coronavirus Disease 2019, may guide efforts to control this pathogen. We examined proteins encoded by the Sarbecoviruses closest to SARS-CoV-2 using profile Hidden Markov Model similarities to identify features unique to SARS-CoV-2. Consistent with previous reports, a small set of bat and pangolin-derived Sarbecoviruses show the greatest similarity to SARS-CoV-2. The analysis provided a measure of total proteome similarity and showed that a small subset of bat Sarbecoviruses are closely related but unlikely to be the direct source of SARS-CoV-2. Spike analysis reveals that the current SARS-CoV-2 variants of concern have sampled only 36% of the possible spikes changes which have occurred historically in Sarbecovirus evolution. It is likely that new SARS-CoV-2 variants with changes in these regions are compatible with virus replication and are to be expected in the coming months, unless global viral replication is severely reduced.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions

Implementation of Android Based Speech Recognition for Indonesian Geography Dictionary

2016 ◽  
Vol 7 (2) ◽  
pp. 76-82
Author(s):  
Hugeng Hugeng ◽  
Edbert Hansel
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Spoken Word ◽  
Silent Condition ◽  
Word Detection ◽  
Index Terms ◽  
To Receive ◽  
Noisy Condition

We have built an application of speech recognition for Indonesian geography dictionary based on Android operating system, named GAIA. This application uses a smartphone as a device to receive input in the form of a spoken word from a user. The approach used in recognition is Hidden Markov Model which is contained in the Pocketsphinx library. The phonemes used are Indonesian phonemes’ rule. The advantage of this application is that it can be used without internet access. In the application testing, word detection is done with four conditions to determine the level of accuracy. The four conditions are near silent, near noisy, far silent, and far noisy. From the testing and analysis conducted, it can be concluded that GAIA application can be built as a speech recognition application on Android for Indonesian geography dictionary; with the results in the near silent condition accuracy of word recognition reaches an average of 52.87%, in the near noisy reaches an average of 14.5%, in the far silent condition reaches an average of 23.2%, and in the far noisy condition reaches an average of 2.8%. Index Terms—speech recognition, Indonesian geography dictionary, Hidden Markov Model, Pocketsphinx, Android.

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