scholarly journals Classification and prediction of frontotemporal dementia based on plasma microRNAs

2020 ◽  
Author(s):  
Iddo Magen ◽  
Nancy Sara Yacovzada ◽  
Jason D. Warren ◽  
Carolin Heller ◽  
Imogen Swift ◽  
...  
Keyword(s):  
Frontotemporal Dementia ◽  
Language Impairment ◽  
Neurodegenerative Disorder ◽  
Machine Learning Techniques ◽  
Small Rna Sequencing ◽  
Accurate Identification ◽  
Diagnostic Laboratory ◽  
Delay In Diagnosis ◽  
Learning Techniques ◽  
Diagnostic Laboratory Tests

AbstractFrontotemporal dementia (FTD) is a heterogeneous neurodegenerative disorder characterized by frontal and temporal lobe atrophy, typically manifesting with behavioural or language impairment. Because of its heterogeneity and lack of available diagnostic laboratory tests there can be a substantial delay in diagnosis. Cell-free, circulating, microRNAs are increasingly investigated as biomarkers for neurodegeneration, but their value in FTD is not yet established. In this study, we investigate microRNAs as biomarkers for FTD diagnosis. We performed next generation small RNA sequencing on cell-free plasma from 52 FTD cases and 21 controls. The analysis revealed the diagnostic importance of 20 circulating endogenous miRNAs in distinguishing FTD cases from controls. The study was repeated in an independent second cohort of 117 FTD cases and 35 controls. The combinatorial microRNA signature from the first cohort, precisely diagnosed FTD samples in a second cohort. To further increase the generalizability of the prediction, we implemented machine learning techniques in a merged dataset of the two cohorts, which resulted in a comparable or improved classification precision with a smaller panel of miRNA classifiers. In addition, there are intriguing molecular commonalities with cell free miRNA signature in ALS, a motor neuron disease that resides on a pathological continuum with FTD. However, the signature that describes the ALS-FTD spectrum is not shared with blood miRNA profiles of patients with multiple sclerosis. Thus, microRNAs are promising FTD biomarkers that might enable earlier detection of FTD and improve accurate identification of patients for clinical trials

Autoimmunity and Frontotemporal Dementia

2018 ◽  
Vol 15 (7) ◽  
pp. 602-609 ◽  
Author(s):  
Antonella Alberici ◽  
Viviana Cristillo ◽  
Stefano Gazzina ◽  
Alberto Benussi ◽  
Alessandro Padovani ◽  
...  
Keyword(s):  
Frontotemporal Dementia ◽  
Language Impairment ◽  
Neurodegenerative Disorder ◽  
Chromosome 9 ◽  
Bibliographic Databases ◽  
Extensive Literature ◽  
Genetic Studies ◽  
Reading Frame ◽  
Current State ◽  
Extensive Evaluation

Background: Frontotemporal Dementia (FTD) is a neurodegenerative disorder which asymmetrically affects the frontotemporal lobe, characterized by behavioural abnormalities, language impairment, and deficits of executive functions. Genetic studies identified mutations causing the disease, namely Microtubule Associated Protein Tau (MAPT), Granulin (GRN) and chromosome 9 open reading frame 72 (C9orf72) mutations, which contributed to elucidate the molecular pathways involved in brain depositions of either Tau or TAR DNA-binding protein 43 (TDP43) inclusions. However, in the majority of sporadic FTD patients, the mechanisms triggering Tau or TDP43 protein deposition are still to be uncovered. Objective: We aimed to present an extensive evaluation of literature data on immune homeostasis in FTD, in order to provide potentially evidence-based approaches for a disease still orphan of any treatment. Methods: A structured search of bibliographic databases from peer-reviewed literature was pursued focusing on autoimmunity in the brain and FTD. Results: One-hundred-fourteen papers were included in this review. The majority of studies (32) were represented by extensive literature revision on immunity, central nervous system (CNS) and autoimmunity; neuroimaging papers (11) in autoimmune diseases were evaluated, and immunomodulatory approaches (25) were revised. Six papers were found specifically related to FTD and autoimmune hypothesis, the other papers referring to current state of art on FTD. Conclusion: Overall this review contribute to expand the knowledge of a possible immune hypothesis in FTD, suggesting therapeutic perspectives in autoimmune related neurodegeneration, to reduce or revert the disease.

Machine Learning for Neurodegenerative Disorder Diagnosis — Survey of Practices and Launch of Benchmark Dataset

2018 ◽  
Vol 27 (03) ◽  
pp. 1850011 ◽  
Author(s):  
Athanasios Tagaris ◽  
Dimitrios Kollias ◽  
Andreas Stafylopatis ◽  
Georgios Tagaris ◽  
Stefanos Kollias
Keyword(s):  
Machine Learning ◽  
Neurodegenerative Disorder ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Network Architectures ◽  
Imaging Data ◽  
Learning Techniques ◽  
Medical Examinations

Neurodegenerative disorders, such as Alzheimer’s and Parkinson’s, constitute a major factor in long-term disability and are becoming more and more a serious concern in developed countries. As there are, at present, no effective therapies, early diagnosis along with avoidance of misdiagnosis seem to be critical in ensuring a good quality of life for patients. In this sense, the adoption of computer-aided-diagnosis tools can offer significant assistance to clinicians. In the present paper, we provide in the first place a comprehensive recording of medical examinations relevant to those disorders. Then, a review is conducted concerning the use of Machine Learning techniques in supporting diagnosis of neurodegenerative diseases, with reference to at times used medical datasets. Special attention has been given to the field of Deep Learning. In addition to that, we communicate the launch of a newly created dataset for Parkinson’s disease, containing epidemiological, clinical and imaging data, which will be publicly available to researchers for benchmarking purposes. To assess the potential of the new dataset, an experimental study in Parkinson’s diagnosis is carried out, based on state-of-the-art Deep Neural Network architectures and yielding very promising accuracy results.

scholarly journals Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

2020 ◽  
Author(s):  
Hao Li ◽  
Liqian Cui ◽  
Liping Cao ◽  
Yizhi Zhang ◽  
Yueheng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Functional Mri ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Accurate Identification ◽  
Learning Techniques ◽  
Svm Model

Abstract Background: Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.Methods: In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.Results: After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5-95.3%), sensitivity of 86.4% (95%CI: 64.0-96.4%), and specificity of 88.9% (95%CI: 63.9-98.0%) in the test data (p=0.0022). Conclusions: A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

scholarly journals Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

2020 ◽  
Author(s):  
Hao Li ◽  
Liqian Cui ◽  
Liping Cao ◽  
Yizhi Zhang ◽  
Yueheng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Functional Mri ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Accurate Identification ◽  
Learning Techniques ◽  
Svm Model

Abstract Background: Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.Methods: In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.Results: After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5-95.3%), sensitivity of 86.4% (95%CI: 64.0-96.4%), and specificity of 88.9% (95%CI: 63.9-98.0%) in the test data (p=0.0022). Limitations: The sample size was small, and we were unable to eliminate the potential effects of medications. Conclusions: A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

scholarly journals Identifying Polarity in Tweets from an Imbalanced Dataset about Diseases and Vaccines Using a Meta-Model Based on Machine Learning Techniques

2020 ◽  
Vol 10 (24) ◽  
pp. 9019
Author(s):  
Alejandro Rodríguez-González ◽  
Juan Manuel Tuñas ◽  
Lucia Prieto Santamaría ◽  
Diego Fernández Peces-Barba ◽  
Ernestina Menasalvas Ruiz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Unbalanced Data ◽  
Correct Identification ◽  
Imbalanced Dataset ◽  
Accurate Identification ◽  
Textual Information ◽  
Meta Model ◽  
Learning Techniques ◽  
Data Problem

Sentiment analysis is one of the hottest topics in the area of natural language. It has attracted a huge interest from both the scientific and industrial perspective. Identifying the sentiment expressed in a piece of textual information is a challenging task that several commercial tools have tried to address. In our aim of capturing the sentiment expressed in a set of tweets retrieved for a study about vaccines and diseases during the period 2015–2018, we found that some of the main commercial tools did not allow an accurate identification of the sentiment expressed in a tweet. For this reason, we aimed to create a meta-model which used the results of the commercial tools to improve the results of the tools individually. As part of this research, we had to deal with the problem of unbalanced data. This paper presents the main results in creating a metal-model from three commercial tools to the correct identification of sentiment in tweets by using different machine-learning techniques and methods and dealing with the unbalanced data problem.

scholarly journals Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Hao Li ◽  
Liqian Cui ◽  
Liping Cao ◽  
Yizhi Zhang ◽  
Yueheng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Functional Mri ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Accurate Identification ◽  
Learning Techniques ◽  
Svm Model

Abstract Background Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD. Methods In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed. Results After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5–95.3%), sensitivity of 86.4% (95%CI: 64.0–96.4%), and specificity of 88.9% (95%CI: 63.9–98.0%) in the test data (p = 0.0022). Conclusions A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

scholarly journals Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Jieru Zhang ◽  
Ying Ju ◽  
Huijuan Lu ◽  
Ping Xuan ◽  
Quan Zou
Keyword(s):  
Machine Learning ◽  
Extraction Methods ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Technology ◽  
Accurate Identification ◽  
Learning Techniques ◽  
Hybrid Machine ◽  
Computational Identification ◽  
Identification Techniques

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

scholarly journals Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

2020 ◽  
Author(s):  
Hao Li ◽  
Liqian Cui ◽  
Liping Cao ◽  
Yizhi Zhang ◽  
Yueheng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Functional Mri ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Accurate Identification ◽  
Learning Techniques ◽  
Svm Model

Abstract Background: Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed for years. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.Methods: In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, voxel-based morphometry (VBM), and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.Results: After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5%, sensitivity of 86.4%, and specificity of 88.9% in the test data (p=0.0022).Conclusions: A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

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