Several studies have established the critical role of microbiome in shaping human health. Steady state balance, a microbial homeostasis, of disparate microbial colonies is the outcome of coevolution and affects the continued health, chronic disease or susceptibility to ill-health. Environmental stressors, including infection and pharmaceuticals, can trigger imbalance and maladaptation of these microbial colonies. Microbial populations of related species are often associated with a specific biological outcome due to a shared biological function (clustered signal). Similarly, diverse interdependent species are also associated with a specific biological outcome (dense signal). When either deliberate or inadvertent influences disrupt the stable relative population of microbes, understanding the dynamics of coevolution in the altered state is important if we are to ultimately understand the longer-term effects of such a disruption. This study attempts to create a generalized approach to model the coevolutionary dynamics of the microbiome due externally triggered disruptions. Preliminary results suggest that the model is successful in simulating stable relative compositions and evaluating pair-wise competition/cooperation scores for microbiome species. The results support the prospect of simulating and predicting the prevalence of Inflammatory Bowel Disease (IBD) as a result of co-evolutionary dynamics. The results further support the possibility of using such a computational approach to model antibiotic induced disruptions to the microbiome.
{"title":"Using Genetic Algorithms to Model Microbiome Coevolutionary Dynamics and Dysbiosis Due to Environmental and Pharmaceutical Stressors","authors":"Mithra V. Karamchedu","doi":"10.1145/3448340.3448343","DOIUrl":"https://doi.org/10.1145/3448340.3448343","url":null,"abstract":"Several studies have established the critical role of microbiome in shaping human health. Steady state balance, a microbial homeostasis, of disparate microbial colonies is the outcome of coevolution and affects the continued health, chronic disease or susceptibility to ill-health. Environmental stressors, including infection and pharmaceuticals, can trigger imbalance and maladaptation of these microbial colonies. Microbial populations of related species are often associated with a specific biological outcome due to a shared biological function (clustered signal). Similarly, diverse interdependent species are also associated with a specific biological outcome (dense signal). When either deliberate or inadvertent influences disrupt the stable relative population of microbes, understanding the dynamics of coevolution in the altered state is important if we are to ultimately understand the longer-term effects of such a disruption. This study attempts to create a generalized approach to model the coevolutionary dynamics of the microbiome due externally triggered disruptions. Preliminary results suggest that the model is successful in simulating stable relative compositions and evaluating pair-wise competition/cooperation scores for microbiome species. The results support the prospect of simulating and predicting the prevalence of Inflammatory Bowel Disease (IBD) as a result of co-evolutionary dynamics. The results further support the possibility of using such a computational approach to model antibiotic induced disruptions to the microbiome.","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121200508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We aim to synthesize a series of dipolar crystalline molecular rotors with substituted benzene rotators in an extended structure. By taking advantage of the porosity of metal organic frameworks (MOFs), we can fine-tune a large cavity for the dynamic rotator to rotate efficiently in the crystalline phase. This would not only allow one to further investigate the dipole-dipole interactions in these dipolar rotors but also their dynamics at various temperatures. We presume that the ideal MOF pillars with ultra-large dipole moment will allow one to investigate the ferroelectric or antiferroelectric properties even at room temperature. In addition, dynamic study of the rotors under an applied AC field will also be studied in order to further investigate any leads regarding externally controlled rotations in the crystalline phase.
{"title":"Constructing Crystalline Molecular Dipolar Rotor Arrays with Ultra-Large Dipole Moments","authors":"Tong Gao","doi":"10.1145/3448340.3448352","DOIUrl":"https://doi.org/10.1145/3448340.3448352","url":null,"abstract":"We aim to synthesize a series of dipolar crystalline molecular rotors with substituted benzene rotators in an extended structure. By taking advantage of the porosity of metal organic frameworks (MOFs), we can fine-tune a large cavity for the dynamic rotator to rotate efficiently in the crystalline phase. This would not only allow one to further investigate the dipole-dipole interactions in these dipolar rotors but also their dynamics at various temperatures. We presume that the ideal MOF pillars with ultra-large dipole moment will allow one to investigate the ferroelectric or antiferroelectric properties even at room temperature. In addition, dynamic study of the rotors under an applied AC field will also be studied in order to further investigate any leads regarding externally controlled rotations in the crystalline phase.","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115528557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The assessment of muscle function is an important part of the rehabilitation of stroke patients. The study proposed two indexes, muscle activation sequence (MAS) and muscle activation duration (MAD), to evaluate the muscle function and the damage level of patients. EMG signals were collected from 6 upper limb muscles of 10 stroke and 10 healthy subjects. Then, the EMG signals were processed by the TKE operator. The MAS and MAD were obtained from each muscle of each subject. Results of the MAS and MAD between healthy and stroke subjects showed that MAS and MAD are more sensitive in evaluating the muscle function compared to other features. The study would promote the evaluation process of muscle function and assist in motor rehabilitation.
{"title":"Evaluation of Upper Limb Muscle Function Based on Muscle Activation Sequence","authors":"Zihan Wang, Yingfei Sun, Jiankang Wu, Kunkun Zhao","doi":"10.1145/3448340.3448348","DOIUrl":"https://doi.org/10.1145/3448340.3448348","url":null,"abstract":"The assessment of muscle function is an important part of the rehabilitation of stroke patients. The study proposed two indexes, muscle activation sequence (MAS) and muscle activation duration (MAD), to evaluate the muscle function and the damage level of patients. EMG signals were collected from 6 upper limb muscles of 10 stroke and 10 healthy subjects. Then, the EMG signals were processed by the TKE operator. The MAS and MAD were obtained from each muscle of each subject. Results of the MAS and MAD between healthy and stroke subjects showed that MAS and MAD are more sensitive in evaluating the muscle function compared to other features. The study would promote the evaluation process of muscle function and assist in motor rehabilitation.","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117342351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Banghua Yang, Du Li, Baiheng Ma, Xuelin Gu, Dewen Kong
In order to solve the problem of reduced classification accuracy caused by the different spatial features of EEG motor imagery signals between the same subjects on different days, this paper proposes an EEG feature extraction and recognition method based on common spatial pattern (CSP) and decision surface adaptive linear discriminant analysis (DSALDA). The decision surface threshold of the linear discriminant analysis (LDA) classifier was updated by saving the CSP spatial feature of the different day's test data, and use parameters to control the proportion of CSP spatial features between the training data set and the different day’ test data set. The experiment collected EEG data of 24 subjects, each subject collected data on different days. The results show that the average accuracy of this method is improved by 6.35% compared with the CSP-LDA method, which effectively improves the accuracy and stability of the different day's motor imagery EEG classification. It has created conditions for the wide application of motor imagery brain-computer interface (BCI) system in the field of rehabilitation.
{"title":"Motor Imagery EEG Classification Method Based on Adaptive Decision Surface of LDA Classifier","authors":"Banghua Yang, Du Li, Baiheng Ma, Xuelin Gu, Dewen Kong","doi":"10.1145/3448340.3448346","DOIUrl":"https://doi.org/10.1145/3448340.3448346","url":null,"abstract":"In order to solve the problem of reduced classification accuracy caused by the different spatial features of EEG motor imagery signals between the same subjects on different days, this paper proposes an EEG feature extraction and recognition method based on common spatial pattern (CSP) and decision surface adaptive linear discriminant analysis (DSALDA). The decision surface threshold of the linear discriminant analysis (LDA) classifier was updated by saving the CSP spatial feature of the different day's test data, and use parameters to control the proportion of CSP spatial features between the training data set and the different day’ test data set. The experiment collected EEG data of 24 subjects, each subject collected data on different days. The results show that the average accuracy of this method is improved by 6.35% compared with the CSP-LDA method, which effectively improves the accuracy and stability of the different day's motor imagery EEG classification. It has created conditions for the wide application of motor imagery brain-computer interface (BCI) system in the field of rehabilitation.","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128495675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CD34 is a transmembrane phosphoglycoprotein that shows expression on early hematopoietic and vascular-associated tissues. Although tumor tissues usually possess upregulated CD34, little is known about this protein's exact function during the process of tumor development. Here, we carried out this study to find possible associations between tumor survival and the overexpressed CD34. We focused our data on patients with adenomas (and adenocarcinomas) occurring in liver or stomach, as these cancer types are common worldwide and contain a large amount of available patient statistics recorded in the TCGA database and we found that CD34 was among the group of proteins under significant upregulation in liver and stomach cancer. However, after constructing the survival curves comparing patient survival of high-level CD34 expression with low-level CD34 expression through a period of time, we observed that high CD34 expression level actually associates with a better tumor survival. This counter-intuitive relation between CD34 and patient survival rate implies a complicated cause-effect relationship behind, which suggests that the idea of designating CD34 as the target to develop therapies for treating cancer still needs more evaluations.
{"title":"High CD34 Expression Level Associated with Prolonged Liver and Stomach Cancer Survival","authors":"Xin Sun","doi":"10.1145/3448340.3448350","DOIUrl":"https://doi.org/10.1145/3448340.3448350","url":null,"abstract":"CD34 is a transmembrane phosphoglycoprotein that shows expression on early hematopoietic and vascular-associated tissues. Although tumor tissues usually possess upregulated CD34, little is known about this protein's exact function during the process of tumor development. Here, we carried out this study to find possible associations between tumor survival and the overexpressed CD34. We focused our data on patients with adenomas (and adenocarcinomas) occurring in liver or stomach, as these cancer types are common worldwide and contain a large amount of available patient statistics recorded in the TCGA database and we found that CD34 was among the group of proteins under significant upregulation in liver and stomach cancer. However, after constructing the survival curves comparing patient survival of high-level CD34 expression with low-level CD34 expression through a period of time, we observed that high CD34 expression level actually associates with a better tumor survival. This counter-intuitive relation between CD34 and patient survival rate implies a complicated cause-effect relationship behind, which suggests that the idea of designating CD34 as the target to develop therapies for treating cancer still needs more evaluations.","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129396359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Heart and brain (AD) diseases are easier to happen to people who are aged. Traditional therapies are not able to cure the disease and the stem cell is chosen to be applied as new therapy. However, there are many moral problems related to stem cells, so another typical therapy is developed, which is IPS therapy. IPS therapy is mainly about inducing stem cells from the somatic cells of patients then deriving them into typical cells we need to treat diseases, which avoid many moral problems. In this article, IPS therapy on treating heart disease on different species and its application on AD are summarized. Though there are disadvantages of IPS therapy, IPS therapy is promising based on the current progress.
{"title":"The Progress of IPS Therapy on the Heart and Neural Disease","authors":"Chang-Quan Sun","doi":"10.1145/3448340.3448347","DOIUrl":"https://doi.org/10.1145/3448340.3448347","url":null,"abstract":"Heart and brain (AD) diseases are easier to happen to people who are aged. Traditional therapies are not able to cure the disease and the stem cell is chosen to be applied as new therapy. However, there are many moral problems related to stem cells, so another typical therapy is developed, which is IPS therapy. IPS therapy is mainly about inducing stem cells from the somatic cells of patients then deriving them into typical cells we need to treat diseases, which avoid many moral problems. In this article, IPS therapy on treating heart disease on different species and its application on AD are summarized. Though there are disadvantages of IPS therapy, IPS therapy is promising based on the current progress.","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116612944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gene expression level reflects the active biological processes in a live cell. It is of great importance to quantify gene expression levels across multiple environments. However, for technical reasons, the expression level in some environments/strains of species may not be measured correctly because of sequence diversity or technical reasons in mRNA-seq, qPCR, or microarray. Therefore, it would be highly beneficial if we could infer the missing expression level from existing data, and this process of filling in such missing values is called imputation. Imputation is a very active field in machine learning, and many tech companies use imputation to infer customer preferences for products/movies, etc. Here we apply multiple state-of-the-art imputation methods and compare their performance in predicting gene expression levels across multiple environments. Using a multi-environment expression dataset of Saccharomyces cerevisiae across 13 environments, we randomly removed 5%, 20%, 50%, and 75% of the expression level from the dataset and applied various imputation methods to predict the missing values and use root mean squared error for comparison of model performances. We found that SVD works the best among the five methods, followed by KNN with five nearest neighbors and KNN with two nearest neighbors. In contrast, univariate mean and univariate median works the worse and perform similarly. Although the latter two univariate methods were very commonly used in practice, our result highlights the benefit of using machine learning methods for imputation for better predictions of expression levels across environments.
{"title":"Machine Learning Facilitates Imputation of Gene Expression Levels across Multiple Environments","authors":"Ziang Xu, H. Qi","doi":"10.1145/3448340.3448342","DOIUrl":"https://doi.org/10.1145/3448340.3448342","url":null,"abstract":"Gene expression level reflects the active biological processes in a live cell. It is of great importance to quantify gene expression levels across multiple environments. However, for technical reasons, the expression level in some environments/strains of species may not be measured correctly because of sequence diversity or technical reasons in mRNA-seq, qPCR, or microarray. Therefore, it would be highly beneficial if we could infer the missing expression level from existing data, and this process of filling in such missing values is called imputation. Imputation is a very active field in machine learning, and many tech companies use imputation to infer customer preferences for products/movies, etc. Here we apply multiple state-of-the-art imputation methods and compare their performance in predicting gene expression levels across multiple environments. Using a multi-environment expression dataset of Saccharomyces cerevisiae across 13 environments, we randomly removed 5%, 20%, 50%, and 75% of the expression level from the dataset and applied various imputation methods to predict the missing values and use root mean squared error for comparison of model performances. We found that SVD works the best among the five methods, followed by KNN with five nearest neighbors and KNN with two nearest neighbors. In contrast, univariate mean and univariate median works the worse and perform similarly. Although the latter two univariate methods were very commonly used in practice, our result highlights the benefit of using machine learning methods for imputation for better predictions of expression levels across environments.","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128726891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bruton's Tyrosine Kinase (BTK) is one of the non-receptor intracellular kinases expressing mainly in B cells and it regulates cell proliferation, apoptosis, and several cellular activities. Abnormal BTK activation is known to play a pivotal role in B cell malignant tumors. Herein, we used computer-aided drug design (CADD) to discover potential inhibitors against the BTK protein. By first acquiring ligand resources from the SPECS library, the ligand preparation and protein preparation on schrödinger were performed. Then the multi-stage docking from high-throughput virtual screening to extra precision were processed through virtual screening workflow. Subsequently, the interaction between the top four satisfied compounds with high docking scores and the BTK protein were analyzed based on results of multi-stage docking and comprehensive details were also discussed accordingly. This paper might lay a foundation for the following study in designing small molecules targeted B cell malignant tumors.
{"title":"Rapid Identification of Potential Inhibitors of Bruton's Tyrosine Kinase (BTK) by Structure-based Virtual Screening","authors":"Yuhao Wang","doi":"10.1145/3448340.3448345","DOIUrl":"https://doi.org/10.1145/3448340.3448345","url":null,"abstract":"Bruton's Tyrosine Kinase (BTK) is one of the non-receptor intracellular kinases expressing mainly in B cells and it regulates cell proliferation, apoptosis, and several cellular activities. Abnormal BTK activation is known to play a pivotal role in B cell malignant tumors. Herein, we used computer-aided drug design (CADD) to discover potential inhibitors against the BTK protein. By first acquiring ligand resources from the SPECS library, the ligand preparation and protein preparation on schrödinger were performed. Then the multi-stage docking from high-throughput virtual screening to extra precision were processed through virtual screening workflow. Subsequently, the interaction between the top four satisfied compounds with high docking scores and the BTK protein were analyzed based on results of multi-stage docking and comprehensive details were also discussed accordingly. This paper might lay a foundation for the following study in designing small molecules targeted B cell malignant tumors.","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133758112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Extracting disease characteristics from large-scale Electronic Medical Records and achieving disease-assisted diagnoses have significant research value. Due to the complex multi-feature items and unbalanced data distribution of Electronic Medical Records, feature representation and disease diagnosis are difficult. Our study proposes a deep feature fusion (DFF) model based on the feature partition and deep feature extraction. First, the feature partition is performed, and different feature representation algorithms are adopted for different types of data. The discrete feature items are directly mapped into real-valued vectors, and the continuous feature items are represented by GCNN-based VAE. Then, the two parts are fused. Finally, the assisted diagnosis results are output through a supervised learning classification method based on the XGBoost framework. The dataset of our study is from the 18,590 real and effective clinical Electronic Medical Record of Huangshi Central Hospital. The experimental results show that the method can perform clinical Assisted diagnosis accurately and efficiently, which are superior to some other state-of-the-art approaches, can better meet the needs of practical clinical diagnosis applications.
{"title":"Automatic Assistance Method for Disease Diagnosis Based on a Deep Learning Fusion Model and Chinese Electronic Medical Record","authors":"Yiping Wang, Guixia Kang, Lijun Liu, Qingsong Huang","doi":"10.1145/3448340.3448341","DOIUrl":"https://doi.org/10.1145/3448340.3448341","url":null,"abstract":"Extracting disease characteristics from large-scale Electronic Medical Records and achieving disease-assisted diagnoses have significant research value. Due to the complex multi-feature items and unbalanced data distribution of Electronic Medical Records, feature representation and disease diagnosis are difficult. Our study proposes a deep feature fusion (DFF) model based on the feature partition and deep feature extraction. First, the feature partition is performed, and different feature representation algorithms are adopted for different types of data. The discrete feature items are directly mapped into real-valued vectors, and the continuous feature items are represented by GCNN-based VAE. Then, the two parts are fused. Finally, the assisted diagnosis results are output through a supervised learning classification method based on the XGBoost framework. The dataset of our study is from the 18,590 real and effective clinical Electronic Medical Record of Huangshi Central Hospital. The experimental results show that the method can perform clinical Assisted diagnosis accurately and efficiently, which are superior to some other state-of-the-art approaches, can better meet the needs of practical clinical diagnosis applications.","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126506242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Obesity is a worldwide problem that causes damage to quality of life and the national health care system. There is no effective treatment for obesity, and the development of novel treatment is necessary. Thermogenesis is a process that happens in thermogenic adipose tissue by transferring energy storage into heat and is a potential target for obesity treatment. In this paper, I screened for potential thermogenic inhibitors by cross comparing human and mouse unbiased proteomic profiling to get 3 out of 7 potential inhibitors. By designing genetic engineering approaches, I acquired gain-of-function and loss-of-function tools for further molecular biology regulation studies of my candidates
{"title":"Investigation of Potential Inhibitors of Brown Adipose Tissue Induced Thermogenesis","authors":"Chang-xun Chen","doi":"10.1145/3448340.3448349","DOIUrl":"https://doi.org/10.1145/3448340.3448349","url":null,"abstract":"Obesity is a worldwide problem that causes damage to quality of life and the national health care system. There is no effective treatment for obesity, and the development of novel treatment is necessary. Thermogenesis is a process that happens in thermogenic adipose tissue by transferring energy storage into heat and is a potential target for obesity treatment. In this paper, I screened for potential thermogenic inhibitors by cross comparing human and mouse unbiased proteomic profiling to get 3 out of 7 potential inhibitors. By designing genetic engineering approaches, I acquired gain-of-function and loss-of-function tools for further molecular biology regulation studies of my candidates","PeriodicalId":365447,"journal":{"name":"2021 11th International Conference on Bioscience, Biochemistry and Bioinformatics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122667030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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