A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia

Alam, Md. Ashad; Lin, Hongbin; Deng, Hong Wen; Calhoun, Vince D.; Wang, Yu Ping

doi:10.1016/j.jneumeth.2018.08.027

Cited by 20 publications

(10 citation statements)

References 68 publications

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“…Another possible approach, suggested by some authors, is through the use of machine learning in multimodal data, consisting of the integration of multiple types of data (eg, genetics, imaging, epigenetic) through algorithms to achieve a final biomarker. 12 For instance, this study used a kernel machine (variation of machine learning), that centers on statistical learning theory to create a functional analysis (12). A strong degree of reliability has been achieved using genederived SNPs (MAGI2), ROIs (CRBLCrus1.L), and gene-specific DNA methylation (FBXO28) at p ≤ 0.000001.…”

Section: Future Perspectivesmentioning

confidence: 99%

“…9 There are currently several biomarkers being studied for schizophrenia. [10][11][12] In this context, neuroimaging techniques such as magnetic resonance imaging (MRI), functional MRI, and also new MRI-related techniques such as voxel-based morphometry (VBM) have emerged as possible biomarkers for disease progression as well as diagnostic tools.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the superior temporal gyrus as a possible biomarker in schizophrenia using voxel-based morphometry of the brain magnetic resonance imaging: a comprehensive review

2020

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The lack of predictive biomarkers for therapeutic responses to schizophrenia leads clinical procedures to be decided without taking into account the subjects’ neuroanatomical features, a consideration, which could help in identifying specific pharmacological treatments for the remission of symptoms. Magnetic resonance imaging (MRI) is a technique widely used for radiological diagnosis and produces 3-dimensional images in excellent anatomical detail, and with a great capacity to differentiate soft tissue. Various MRI techniques of the human brain have emerged as a result of research, enabling structural tests that may help to in consolidate previous findings and lead to the discovery of new patterns of abnormality in schizophrenia. A literature review was undertaken to assess the superior temporal gyrus (STG) as a possible biomarker in schizophrenia with the use of voxel-based morphometry of the brain using MRI. Many findings in studies of schizophrenia using MRI have been inconclusive and, in some cases, conflicting, although interesting results have been obtained when attempting to correlate neuroimaging changes with aspects of clinical features and prognosis of the disease. The individuals affected by this mental illness appear to have smaller STG volumes when compared to healthy controls and also to subjects with a diagnosis of first-episode affective psychosis or groups of individuals at high risk of psychosis. However, the wide variety of definitions surrounding the STG found in a number of studies is a contributing factor to the lack of correlation between brain abnormalities and clinical symptoms. For instance, disagreements have arisen due to studies using regions of interest to analyze the STG whereas other studies prioritize the analysis of only STG subregions or specific supratemporal plane regions. It is necessary to standardize the nomenclature of the areas to be studied in the future, as this will enable more consistent results, allowing higher clinical and morphological correlations.

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Section: Future Perspectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of the superior temporal gyrus as a possible biomarker in schizophrenia using voxel-based morphometry of the brain magnetic resonance imaging: a comprehensive review

2020

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“…Here, c ≥ 0 is a free parameter trading off the influence from higher-order and lower-order terms of the polynomial [29]. Then, the proposal particles, shown as green points in Fig.…”

Section: Adaptive Kernel Kalman Filtermentioning

confidence: 99%

Adaptive Kernel Kalman Filter

Sun¹,

Davies²,

Proudler³

et al. 2022

Preprint

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Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of the predictive and posterior distributions. This paper introduces a Bayesian filter called the adaptive kernel Kalman filter (AKKF). With this filter, the arbitrary predictive and posterior distributions of hidden states are approximated using the empirical kernel mean embeddings (KMEs) in reproducing kernel Hilbert spaces (RKHSs). In parallel with the KMEs, some particles, in the data space, are used to capture the properties of the dynamical system model. Specifically, particles are generated and updated in the data space, while the corresponding kernel weight mean vector and covariance matrix associated with the feature mappings of the particles are predicted and updated in the RKHSs based on the kernel Kalman rule (KKR). Simulation results are presented to confirm the improved performance of our approach with significantly reduced particle numbers, by comparing with the unscented Kalman filter (UKF), particle filter (PF) and Gaussian particle filter (GPF). For example, compared with the GPF, the proposed approach provides around 5% logarithmic mean square error (LMSE) tracking performance improvement in the bearing-only tracking (BOT) system when using 50 particles.

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“…Over the past couple decades, machine learning has become one of the strengths of information technology and with that, a rather fundamental, notwithstanding usually hidden, part of our life described in [1,9]. With the ever growing volumes of data becoming available there is good aim to trust that smart data analysis will become even more persistent as a essential component for technological advancement.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Machine Learning Algorithms in Ecological Dataset

Siraj-Ud-Doulah¹,

Alam²

2020

Int. J. Appl. Math. Mach. Learning

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Classification is a key supervised machine learning technique, which is essential and interesting topic for ecological research. It deals a way to classify a dataset into subsets that share common designs. Particularly, there are many classification processes to select from, each creating firm assumptions about the data and about how classification should be grouped. In this paper, we applied eight machine learning classification algorithms such as Decision Trees, Random Forest, Artificial Neural Network, Support Vector Machine, Linear Discriminant Analysis, k-nearest neighbours, Logistic Regression and Naive Bayes on ecological data. The objective of this study is to compare different machine learning classification algorithms in ecological dataset. In this analysis we have checked the accuracy test among the algorithms. In our study we conclude that linear discriminant analysis and k-nearest neighbours are the best methods.

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A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia

Abstract: With strong evidence (p-value ≤0.000001), the triplet (MAGI2, CRBLCrus1.L, FBXO28) is a significant biomarker for schizophrenia patients. This novel method can be applicable to the study of other disease processes, where multimodal data analysis is a common task.

Cited by 20 publications

References 68 publications

Analysis of the superior temporal gyrus as a possible biomarker in schizophrenia using voxel-based morphometry of the brain magnetic resonance imaging: a comprehensive review

Analysis of the superior temporal gyrus as a possible biomarker in schizophrenia using voxel-based morphometry of the brain magnetic resonance imaging: a comprehensive review

Adaptive Kernel Kalman Filter

Performance Evaluation of Machine Learning Algorithms in Ecological Dataset

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