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Takallou MA, Fallahtafti F, Hassan M, Al-Ramini A, Qolomany B, Pipinos I, Myers S, Alsaleem F. Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation. Sci Rep 2024; 14:1075. [PMID: 38212467 PMCID: PMC10784467 DOI: 10.1038/s41598-023-50727-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 12/23/2023] [Indexed: 01/13/2024] Open
Abstract
This paper demonstrates the value of a framework for processing data on body acceleration as a uniquely valuable tool for diagnosing diseases that affect gait early. As a case study, we used this model to identify individuals with peripheral artery disease (PAD) and distinguish them from those without PAD. The framework uses acceleration data extracted from anatomical reflective markers placed in different body locations to train the diagnostic models and a wearable accelerometer carried at the waist for validation. Reflective marker data have been used for decades in studies evaluating and monitoring human gait. They are widely available for many body parts but are obtained in specialized laboratories. On the other hand, wearable accelerometers enable diagnostics outside lab conditions. Models trained by raw marker data at the sacrum achieve an accuracy of 92% in distinguishing PAD patients from non-PAD controls. This accuracy drops to 28% when data from a wearable accelerometer at the waist validate the model. This model was enhanced by using features extracted from the acceleration rather than the raw acceleration, with the marker model accuracy only dropping from 86 to 60% when validated by the wearable accelerometer data.
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Affiliation(s)
- Mohammad Ali Takallou
- Architectural Engineering Department, University of Nebraska-Lincoln, Omaha, NE, 68182, USA
| | - Farahnaz Fallahtafti
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, 6160, USA
- Department of Surgery and VA Research Service, VA Nebraska-Western Iowa Health Care System, Omaha, NE, 68105, USA
| | - Mahdi Hassan
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, 6160, USA
- Department of Surgery and VA Research Service, VA Nebraska-Western Iowa Health Care System, Omaha, NE, 68105, USA
| | - Ali Al-Ramini
- Mechanical Engineering Department, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Basheer Qolomany
- Cyber Systems Department, University of Nebraska at Kearney, Kearney, NE, 68849, USA
| | - Iraklis Pipinos
- Department of Surgery and VA Research Service, VA Nebraska-Western Iowa Health Care System, Omaha, NE, 68105, USA
- Department of Surgery, University of Nebraska Medical Center, Omaha, NE, 68105, USA
| | - Sara Myers
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, 6160, USA
- Department of Surgery and VA Research Service, VA Nebraska-Western Iowa Health Care System, Omaha, NE, 68105, USA
| | - Fadi Alsaleem
- Architectural Engineering Department, University of Nebraska-Lincoln, Omaha, NE, 68182, USA.
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Wang W, Zhang Y, Liu D, Zhang H, Wang X, Zhou Y. PseAraUbi: predicting arabidopsis ubiquitination sites by incorporating the physico-chemical and structural features. PLANT MOLECULAR BIOLOGY 2022; 110:81-92. [PMID: 35773617 DOI: 10.1007/s11103-022-01288-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
We makes three kinds of important features from Arabidopsis thaliana: protein secondary structure based on the Chou-Fasman parameter, amino acids hydrophobicity and polarity information, and analyze their properties. Ubiquitination modification is an important post-translational modification of proteins, which participates in the regulation of many important life activities in cells. At present, ubiquitination proteomics research is mostly concentrated in animals and yeasts, while relatively few studies have been carried out in plants. It can be said that the calculation and prediction of Arabidopsis thaliana ubiquitination sites is still in its infancy. Based on this, we describe a calculation method, PseAraUbi (Prediction of Arabidopsis thaliana ubiquitination sites using pseudo amino acid composition), that can effectively detect ubiquitination sites on Arabidopsis thaliana using support vector machine learning classifiers. Based on protein sequence information, extract features from the Chou-Fasman parameter, amino acids hydrophobicity features, polarity information and selected for classification with the Boruta algorithm. PseAraUbi achieves promising performances with an AUC score of 0.953 with fivefold cross-validation on the training dataset, which are significantly better than that of the pioneer Arabidopsis thaliana ubiquitination sites method. We also proved the ability of our proposed method on independent test sets, thus gaining a competitive advantage. In addition, we also in-depth analyzed the physicochemical properties of amino acids in the region adjacent to the ubiquitination site. To facilitate the community, the source code, optimal feature subset, ubiquitination sites dataset in the Arbidopsis proteome are available at GitHub ( https://github.com/HNUBioinformatics/PseAraUbi.git ) for interest users.
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Affiliation(s)
- Wei Wang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453000, China.
- Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, Xinxiang, China.
| | - Yu Zhang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453000, China
| | - Dong Liu
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453000, China
| | - HongJun Zhang
- School of Computer Science and Technology, Anyang University, Anyang, 455000, China
| | - XianFang Wang
- College of Computer Science and Technology Engineering, Henan Institute of Technology, Xinxiang, 453000, China
| | - Yun Zhou
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453000, China.
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Long short term memory based functional characterization model for unknown protein sequences using ensemble of shallow and deep features. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06674-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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