Bounded Fuzzy Possibilistic Method Reveals Information about Lung Cancer through Analysis of Metabolomics

Abstract

Learning methods, such as conventional clustering and classification, have been applied in diagnosing diseases to categorize samples based on their features. Going beyond clustering samples, membership degrees represent to what degree each sample belongs to a cluster. Variation of membership degrees in each cluster provides information about the cluster as a whole and each sample individually which enables to have insights toward precision medicine. Membership degrees are measured more accurately through removing restrictions from clustering samples. Bounded Fuzzy Possibilistic Method (BFPM) introduces a membership function that keeps the search space flexible to cluster samples with higher accuracy. The method evaluates samples for their movement from one cluster to another. This technique allows us to find critical samples in advance those with potential ability to belong to other clusters in the near future. BFPM was applied on metabolomics of individuals in a lung cancer case-control study. Metabolomics as proximal molecular signals to the actual disease processes may serve as strong biomarkers of current disease process. The goal is to know whether serum metabolites of healthy human can be differentiated from those with lung cancer. Using BFPM, some differences were observed, the pathology data were evaluated, and critical samples were recognized.

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

“…Heterogeneity in expression levels across large samples: Due to heterogeneity, there may be an asymmetry in this effect on sample quality among patients with genetic brain disease and the degree of affected gene expression. Recently, an attempt was made to identify heterozygous genes in the study of gene expression data with genetic brain disease patients ( Tejeswinee, Shomona & Athilakshmi, 2017 , Escott-Price et al, 2014 ; Baker et al, 2019 ; Ruiz et al, 2014 ; Edwards, Stajich & Hansen, 2009 ; Staples et al, 2019 ; Pop & Salzberg, 2008 ; Yazdani et al, 2018 ).…”

“… Yazdani et al (2018) introduced a method which is known as Bounded Fuzzy Possibilistic Method (BFPM) that takes into account preserving the flexibility of the search space for higher accuracy cluster sampling ( Yazdani, 2020 ). The method assessed samples for their movement from one cluster to another.…”

“…They used various ML techniques to extract information from the network, determine differential regulatory patterns between cases, and control schizophrenia. Yazdani et al (2018) introduced a method which is known as Bounded Fuzzy Possibilistic Method (BFPM) that takes into account preserving the flexibility of the search space for higher accuracy cluster sampling (Yazdani, 2020). The method assessed samples for their movement from one cluster to another.…”

Bounded Fuzzy Possibilistic Method

“…In all domains and disciplines, we need to remove the limitations and restrictions in membership assignments to allow objects to participate in more clusters to track their behaviour. In medicine, we need to evaluate individuals before being affected to any disease categories to cut the further costs for treatments [30]. In such a case, we need to evaluate individuals on their participations in other clusters without any restriction to track their potential ability to participate in other clusters.…”

“…The proposed method has been applied on real applications and problems from different domains such as medicine (lung cancer diagnosis) [30], intrusion detection systems [32], and banking (financial) systems and risk managements (decision making systems) [35]. The proposed methodology has been utilized in both supervised and unsupervised learning strategies, but in this paper clustering problems have been selected for experimental verifications.…”