Identification of molecular signatures and pathways of obese breast cancer gene expression data by a machine learning algorithm

Comertpay, Betul; Göv, Esra

doi:10.20517/jtgg.2021.44

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…In the same case, the neural system regulates one's eating patterns [99]. In differential transcriptomic analysis and GWAS/WGS analysis, the neuronal system's low level signalling pathways called "Transmission across chemical synapses -(R-HSA-112315)" highly enriched among obesity, schizophrenia and depression patients [100][101][102]., the GO term chemical synaptic transmission (GO:0007268) is substantially enriched, with a p-value of 8.66E-09 [98]. Neurotransmitter Release Cycle (R-HSA-112310) is also detected in mdd and obesity (together with scz and obesity) [103], even through significant enrichment of Dopamine Release Cycle (R-HSA-212676) with a p-value of 0.00027 [104,105].…”

Section: Discussionmentioning

confidence: 99%

Integrated bioinformatics and statistical approach to identify the common molecular mechanisms of obesity that are linked to the development of two psychiatric disorders: Schizophrenia and major depressive disorder

Islam¹,

Islam²,

Rahman³

et al. 2023

PLoS ONE

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Obesity is a chronic multifactorial disease characterized by the accumulation of body fat and serves as a gateway to a number of metabolic-related diseases. Epidemiologic data indicate that Obesity is acting as a risk factor for neuro-psychiatric disorders such as schizophrenia, major depression disorder and vice versa. However, how obesity may biologically interact with neurodevelopmental or neurological psychiatric conditions influenced by hereditary, environmental, and other factors is entirely unknown. To address this issue, we have developed a pipeline that integrates bioinformatics and statistical approaches such as transcriptomic analysis to identify differentially expressed genes (DEGs) and molecular mechanisms in patients with psychiatric disorders that are also common in obese patients. Biomarker genes expressed in schizophrenia, major depression, and obesity have been used to demonstrate such relationships depending on the previous research studies. The highly expressed genes identify commonly altered signalling pathways, gene ontology pathways, and gene-disease associations across disorders. The proposed method identified 163 significant genes and 134 significant pathways shared between obesity and schizophrenia. Similarly, there are 247 significant genes and 65 significant pathways that are shared by obesity and major depressive disorder. These genes and pathways increase the likelihood that psychiatric disorders and obesity are pathogenic. Thus, this study may help in the development of a restorative approach that will ameliorate the bidirectional relation between obesity and psychiatric disorder. Finally, we also validated our findings using genome-wide association study (GWAS) and whole-genome sequence (WGS) data from SCZ, MDD, and OBE. We confirmed the likely involvement of four significant genes both in transcriptomic and GWAS/WGS data. Moreover, we have performed co-expression cluster analysis of the transcriptomic data and compared it with the results of transcriptomic differential expression analysis and GWAS/WGS.

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Section: Discussionmentioning

confidence: 99%

Integrated bioinformatics and statistical approach to identify the common molecular mechanisms of obesity that are linked to the development of two psychiatric disorders: Schizophrenia and major depressive disorder

Islam¹,

Islam²,

Rahman³

et al. 2023

PLoS ONE

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“…The above approach allows for flexible adjustment of the power grouping network operation status weight values. Combined with the power group network operation state weight value, the abnormal degree of the power group network operation state can be characterized, and the specific computation equation is given by [12]:…”

Section: Power Communication Network Abnormal Signal Analysismentioning

confidence: 99%

Automatic fault location method for power communication network based on a machine learning algorithm

Ren

Chen

2023

J. Phys.: Conf. Ser.

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The current conventional automatic fault location method of power communication network mainly realizes the identification of faulty branches by calculating node power parameters, which leads to poor location effects due to the lack of effective analysis of fault signal data. In this regard, the automatic fault location method of the store head communication network based on a machine learning algorithm is proposed. Firstly, the fault signal data are collected and pre-processed, specifically including data cleaning and data format conversion, and then the individual fault signal is used as the basis to differentiate the weight parameters of different abnormal degrees and combine with the state threshold to realize the judgment of the fault node interval location. In the experiments, the fault location performance of the proposed method is verified. The experimental results show that when the proposed method is used for fault location, the relative positioning error value of the method is small and has a high fault location accuracy.

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“…In the future, complex and heterogeneous networks can intelligently interact with the environment. e cognitive network category includes multiple levels of the network [13]. erefore, the relay nodes that provide data sending and receiving services in the network need to intelligently perceive the complex user and network environment information.…”

Section: Machine Learning Algorithms and Cognitive Networkmentioning

confidence: 99%

Credit Risk Simulation of Enterprise Financial Management Based on Machine Learning Algorithm

Mingtao

2022

Mobile Information Systems

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In the context of the rapid development of the Internet +, the e-commerce industry has developed very rapidly and is playing an increasingly important role in the market. More and more investors are also paying attention to the financial risks of the e-commerce industry. Therefore, how effectively identifying and controlling the credit risks of e-commerce enterprises corporate financial management is particularly important. Its financial-related business will be affected by various factors such as financing, credit, and the environment. The particle swarm optimization in the machine learning algorithm optimizes the support vector machine model; using its nonoverlapping clustering properties, it can fully display the density relationship between each data and the density relationship between its subclasses in a graphical way. It is hoped that the research on the financial risk of this enterprise can provide some insights into the identification, management, and related governance of the financial risk of e-commerce enterprises. In this context, this paper studies the credit risk simulation of corporate financial management based on machine learning algorithms, aiming to provide a new research direction for the current subject of corporate financial management credit risk. This paper optimizes the support vector machine model in the machine learning algorithm, integrates it into the financial risk of the enterprise, and analyzes the convergence of the algorithm. The experimental sample selected a bad e-commerce platform owned by a company, and the data were collected from the financial statements released by the company from 2018 to 2021. The experimental results show that in 2018, the two platforms lost −151.80 and −223.45, respectively, and the B platform lost more. By 2021, both platforms have achieved profits, which are 244.76 and 241.71, respectively. However, in the past few years, platform B has achieved positive profit growth every year, and the growth rate is average, and the overall growth rate is higher than that of platform A. This shows the limitations of managers’ decision-making and also shows the importance of enterprises to adjust their strategies in a timely manner through market feedback. The study was well completed.

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Identification of molecular signatures and pathways of obese breast cancer gene expression data by a machine learning algorithm

Cited by 7 publications

References 31 publications

Integrated bioinformatics and statistical approach to identify the common molecular mechanisms of obesity that are linked to the development of two psychiatric disorders: Schizophrenia and major depressive disorder

Integrated bioinformatics and statistical approach to identify the common molecular mechanisms of obesity that are linked to the development of two psychiatric disorders: Schizophrenia and major depressive disorder

Automatic fault location method for power communication network based on a machine learning algorithm

Credit Risk Simulation of Enterprise Financial Management Based on Machine Learning Algorithm

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