On High Dimensional Searching Spaces and Learning Methods

Elsea

et al. 2019

BMC Genomics

Background Many genome-wide association studies have detected genomic regions associated with traits, yet understanding the functional causes of association often remains elusive. Utilizing systems approaches and focusing on intermediate molecular phenotypes might facilitate biologic understanding. Results The availability of exome sequencing of two populations of African-Americans and European-Americans from the Atherosclerosis Risk in Communities study allowed us to investigate the effects of annotated loss-of-function (LoF) mutations on 122 serum metabolites. To assess the findings, we built metabolomic causal networks for each population separately and utilized structural equation modeling. We then validated our findings with a set of independent samples. By use of methods based on concepts of Mendelian randomization of genetic variants, we showed that some of the affected metabolites are risk predictors in the causal pathway of disease. For example, LoF mutations in the gene KIAA1755 were identified to elevate the levels of eicosapentaenoate ( p -value = 5E-14), an essential fatty acid clinically identified to increase essential hypertension. We showed that this gene is in the pathway to triglycerides, where both triglycerides and essential hypertension are risk factors of metabolomic disorder and heart attack. We also identified that the gene CLDN17, harboring loss-of-function mutations, had pleiotropic actions on metabolites from amino acid and lipid pathways. Conclusion Using systems biology approaches for the analysis of metabolomics and genetic data, we integrated several biological processes, which lead to findings that may functionally connect genetic variants with complex diseases. Electronic supplementary material The online version of this article (10.1186/s12864-019-5772-4) contains supplementary material, which is available to authorized users.

Section: Discussionmentioning

confidence: 99%

Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics

Elsea

et al. 2019

BMC Genomics

“…BFPM avoids the problem of reducing the objects' memberships when the number of clusters increases. According to the geometry example, objects (points) are allowed to obtain full memberships from more than one even all clusters (lines) with no restrictions [31]. Based on the membership assignments provided by BFPM we can obtain the following results for the points on the lines.…”

Section: New Learning Methodology (Bfpm)mentioning

confidence: 99%

Bounded Fuzzy Possibilistic Method

2020

Fuzzy Sets and Systems

Self Cite

This paper introduces Bounded Fuzzy Possibilistic Method (BFPM) by addressing several issues that previous clustering/classification methods have not considered. In fuzzy clustering, object's membership values should sum to 1. Hence, any object may obtain full membership in at most one cluster. Possibilistic clustering methods remove this restriction. However, BFPM differs from previous fuzzy and possibilistic clustering approaches by allowing the membership function to take larger values with respect to all clusters. Furthermore, in BFPM, a data object can have full membership in multiple clusters or even in all clusters. BFPM relaxes the boundary conditions (restrictions) in membership assignment. The proposed methodology satisfies the necessity of obtaining full memberships and overcomes the issues with conventional methods on dealing with overlapping. Analysing the objects' movements from their own cluster to another (mutation) is also proposed in this paper. BFPM has been applied in different domains in geometry, set theory, anomaly detection, risk management, diagnosis diseases, and other disciplines. Validity and comparison indexes have been also used to evaluate the accuracy of BFPM. BFPM has been evaluated in terms of accuracy, fuzzification constant (different norms), object's movement analysis, and covering diversity. The promising results prove the importance of considering the proposed methodology in learning methods to track the behaviour of data objects, in addition to obtain accurate results.

“…BFPM avoids the problem of reducing the objects' memberships as the number of clusters increases. BFPM allows the origin to attend all clusters (lines) with full memberships without decreasing its ability, and on the other side, it shows which points have the potential ability to move from one cluster to another by mutation [17]. In crucial systems, addressing critical objects is very important, as investigators need to encourage or prevent objects to/from contributing to other clusters [18].…”

Section: Example In Society and Economymentioning

confidence: 99%

“…The chapter demonstrates the achievements of the proposed method and the introduced similarity and membership functions in comparison with other methods. The proposed method has been already published in the Fuzzy Sets and Systems Journal [11] as well as other ideas in different papers cited at the end of the Thesis [12], [13][14][15][16][17][18][19][20], [21].…”

Section: Thesis Organizationmentioning

confidence: 99%

Section: Example In Society and Economymentioning

confidence: 99%

See 1 more Smart Citation

Applying bounded fuzzy possibilistic method on critical objects

2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI)

Ortiz-Arroyo

Choroś

et al. 2016

Self Cite

Providing a flexible environment to process data objects is a desirable goal of machine learning algorithms. In fuzzy and possibilistic methods, the relevance of data objects is evaluated and a membership degree is assigned. However, some critical objects objects have the potential ability to affect the performance of the clustering algorithms if they remain in a specific cluster or they are moved into another. In this paper we analyze and compare how critical objects affect the behaviour of fuzzy possibilistic methods in several data sets. The comparison is based on the accuracy and ability of learning methods to provide a proper searching space for data objects. The membership functions used by each method when dealing with critical objects is also evaluated. Our results show that relaxing the conditions of participation for data objects in as many partitions as they can, is beneficial.