Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995264
Xuan Yang, Keqin Li, Yang Zhang, Xi Yu, Junli Deng, Jianxiao Liu
Proposing a more effective and accurate epistatic loci detection method is of great significance in improving crop quality, disease treatment, etc. Due to the characteristics of high accuracy and processing non-linear relationship, Bayesian network (BN) has been widely used in constructing the network of SNPs and phenotypes and thus to mine epistasis. However, the shortcoming of BN is that the search space is too large and unable to process large-scale SNPs. In this work, we propose a kind of epistasis mining method using Markov Chain Monte Carlo (MCMC) sampling optimizing Bayesian network (EpiMCBN). Firstly, we use the space of node order composed of SNPs and phenotype to replace the space of network structure. Then MCMC algorithm is used to do sampling to generate multiple different initial orders in linear space or partial space. We use Markov state transition matrix to transfer the initial samples along the Markov chain, thus obtaining multiple order samples. Then we use the $alpha$-BICBN scoring function to score the Bayesian networks corresponding to these node orders. Through estimating the probability of edge occurrence in the Bayesian networks, we get an approximate Bayesian network of SNPs and phenotype, then obtain the epistatic loci affecting phenotype. Finally, we compare EpiMCBN with the current popular epistasis mining algorithms using both simulated and real age-related macular disease (AMD) datasets. Experiment results show that EpiMCBN has better epistasis detection accuracy, lower false positive rate, and higher F1-score compared to other methods. Availability and implementation: Source code and dataset are available at: http://122.205.95.139/EpiMCBN/.
{"title":"EpiMCBN: A Kind of Epistasis Mining Approach Using MCMC Sampling Optimizing Bayesian Network","authors":"Xuan Yang, Keqin Li, Yang Zhang, Xi Yu, Junli Deng, Jianxiao Liu","doi":"10.1109/BIBM55620.2022.9995264","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995264","url":null,"abstract":"Proposing a more effective and accurate epistatic loci detection method is of great significance in improving crop quality, disease treatment, etc. Due to the characteristics of high accuracy and processing non-linear relationship, Bayesian network (BN) has been widely used in constructing the network of SNPs and phenotypes and thus to mine epistasis. However, the shortcoming of BN is that the search space is too large and unable to process large-scale SNPs. In this work, we propose a kind of epistasis mining method using Markov Chain Monte Carlo (MCMC) sampling optimizing Bayesian network (EpiMCBN). Firstly, we use the space of node order composed of SNPs and phenotype to replace the space of network structure. Then MCMC algorithm is used to do sampling to generate multiple different initial orders in linear space or partial space. We use Markov state transition matrix to transfer the initial samples along the Markov chain, thus obtaining multiple order samples. Then we use the $alpha$-BICBN scoring function to score the Bayesian networks corresponding to these node orders. Through estimating the probability of edge occurrence in the Bayesian networks, we get an approximate Bayesian network of SNPs and phenotype, then obtain the epistatic loci affecting phenotype. Finally, we compare EpiMCBN with the current popular epistasis mining algorithms using both simulated and real age-related macular disease (AMD) datasets. Experiment results show that EpiMCBN has better epistasis detection accuracy, lower false positive rate, and higher F1-score compared to other methods. Availability and implementation: Source code and dataset are available at: http://122.205.95.139/EpiMCBN/.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130747733","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}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995075
J. Shang, Yijun Gu, Y. Sun, Feng Li, Jin-Xin Liu, Boxin Guan
In the genome-wide association studies (GWAS), epistasis detection is of great significance to study the pathogenesis of complex diseases. Epistasis refers to the effect of interactions between multiple single nucleotide polymorphisms (SNPs) on complex diseases. In this paper, an artificial bee colony algorithm based on self-adjusting random grouping (ABC-SRG) is proposed for high-order epistasis detection. ABC-SRG adopts a new self-adjusting random grouping strategy, which realizes the division of the original data according to the fitness value of each grouping. In addition, a variance-based adaptive iteration strategy is proposed, which implements the adaptive iteration through the variance of the fitness value of each iteration of the algorithm. To demonstrate the effectiveness of the algorithm, the experiments on simulated data and real data were conducted. In the simulation experiments, ABC-SRG was compared with the other five methods for second-order and third-order SNP interaction detection. Age-related macular degeneration (AMD) data were selected for the real data experiment, and most of the SNP interactions detected in the experiment have been confirmed to be related to the AMD disease. Therefore, ABC-SRG is an effective method to detect high-order epistasis.
{"title":"Artificial bee colony algorithm based on self-adjusting random grouping for high-order epistasis detection","authors":"J. Shang, Yijun Gu, Y. Sun, Feng Li, Jin-Xin Liu, Boxin Guan","doi":"10.1109/BIBM55620.2022.9995075","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995075","url":null,"abstract":"In the genome-wide association studies (GWAS), epistasis detection is of great significance to study the pathogenesis of complex diseases. Epistasis refers to the effect of interactions between multiple single nucleotide polymorphisms (SNPs) on complex diseases. In this paper, an artificial bee colony algorithm based on self-adjusting random grouping (ABC-SRG) is proposed for high-order epistasis detection. ABC-SRG adopts a new self-adjusting random grouping strategy, which realizes the division of the original data according to the fitness value of each grouping. In addition, a variance-based adaptive iteration strategy is proposed, which implements the adaptive iteration through the variance of the fitness value of each iteration of the algorithm. To demonstrate the effectiveness of the algorithm, the experiments on simulated data and real data were conducted. In the simulation experiments, ABC-SRG was compared with the other five methods for second-order and third-order SNP interaction detection. Age-related macular degeneration (AMD) data were selected for the real data experiment, and most of the SNP interactions detected in the experiment have been confirmed to be related to the AMD disease. Therefore, ABC-SRG is an effective method to detect high-order epistasis.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131954702","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}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995247
Yanglin Huang, Donghui Tan, Yuan Zhang, Xuanya Li, Kai Hu
Learning how to fully extract global representations and local features is a key factor in improving the performance of polyp segmentation. In this paper, we explore the potential of combined techniques of Transformers and convolutional neural networks (CNNs) to address the challenges of polyp segmentation. Specifically, we present TransMixer, a hybrid interaction fusion architecture of the Transformer branch and the CNN branch, which is able to enhance the local details of global representations and the global context awareness of local features. To achieve this, we first bridge the semantic gap between the Transformer branch and the CNN branch through the Interaction Fusion Module (IFM), and then make full use of both respective properties to enhance polyp feature representations. After that, we further propose the Hierarchical Attention Module (HAM) to collect polyp semantic information from high-level features to gradually guide the recovery of polyp spatial information in low-level features. Quantitative and qualitative results show that the proposed model is more robust to various complex situations compared to existing methods, and achieves state-of-the-art performance in polyp segmentation.
{"title":"TransMixer: A Hybrid Transformer and CNN Architecture for Polyp Segmentation","authors":"Yanglin Huang, Donghui Tan, Yuan Zhang, Xuanya Li, Kai Hu","doi":"10.1109/BIBM55620.2022.9995247","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995247","url":null,"abstract":"Learning how to fully extract global representations and local features is a key factor in improving the performance of polyp segmentation. In this paper, we explore the potential of combined techniques of Transformers and convolutional neural networks (CNNs) to address the challenges of polyp segmentation. Specifically, we present TransMixer, a hybrid interaction fusion architecture of the Transformer branch and the CNN branch, which is able to enhance the local details of global representations and the global context awareness of local features. To achieve this, we first bridge the semantic gap between the Transformer branch and the CNN branch through the Interaction Fusion Module (IFM), and then make full use of both respective properties to enhance polyp feature representations. After that, we further propose the Hierarchical Attention Module (HAM) to collect polyp semantic information from high-level features to gradually guide the recovery of polyp spatial information in low-level features. Quantitative and qualitative results show that the proposed model is more robust to various complex situations compared to existing methods, and achieves state-of-the-art performance in polyp segmentation.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131972200","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}
Late fetal growth restriction (FGR) is a common complication of pregnancy characterized by chronic hypoxia. However, late FGR is in a dilemma of the high incidence but low detection rate. Depending on the non-invasiveness and convenient operation, the routine cardiotocography (CTG) allows continuous monitoring fetal heart rate (FHR) to assess fetal intrauterine stockpiling ability. In this paper, we aimed to explore the FHR pattern of late FGR in routine CTG. For analysis, the FHR features were acquired using routine CTG in a population of 160 healthy and 102 late FGR fetuses published in IEEE Dataport. First, we explored the relationships among FHR features and their importance on late FGR assessment by utilizing hypothesis testing, principal component analysis (PCA) and Spearman correlation analysis. Second, we presented a regression coefficient-based backward-stepwise-selection of association rules analysis (ARA) called backward-stepwise Max-R2 Apriori ARA, to find the optimum itemset that helps diagnose late FGRs from healthy fetuses. The hypothesis testing, PCA and Spearman correlation analysis found eight FHR features were highly relevant to the late FGR. Moreover, the backward-stepwise Max-R2 Apriori ARA validated the correlation and interpretation about FHR features of late FGR. In conclusion, the analysis results are consistent with clinical knowledge on late FGR and help screen late FGR in antepartum fetal monitoring.
{"title":"Association rule analysis for fetal heart rate pattern of late FGR","authors":"Liyan Zhong, Shiyao Huang, Xia Li, Guiqing Liu, Qinqun Chen, Xiaomu Luo, Yuexing Hao, Jiaming Hong, Hang Wei","doi":"10.1109/BIBM55620.2022.9995462","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995462","url":null,"abstract":"Late fetal growth restriction (FGR) is a common complication of pregnancy characterized by chronic hypoxia. However, late FGR is in a dilemma of the high incidence but low detection rate. Depending on the non-invasiveness and convenient operation, the routine cardiotocography (CTG) allows continuous monitoring fetal heart rate (FHR) to assess fetal intrauterine stockpiling ability. In this paper, we aimed to explore the FHR pattern of late FGR in routine CTG. For analysis, the FHR features were acquired using routine CTG in a population of 160 healthy and 102 late FGR fetuses published in IEEE Dataport. First, we explored the relationships among FHR features and their importance on late FGR assessment by utilizing hypothesis testing, principal component analysis (PCA) and Spearman correlation analysis. Second, we presented a regression coefficient-based backward-stepwise-selection of association rules analysis (ARA) called backward-stepwise Max-R2 Apriori ARA, to find the optimum itemset that helps diagnose late FGRs from healthy fetuses. The hypothesis testing, PCA and Spearman correlation analysis found eight FHR features were highly relevant to the late FGR. Moreover, the backward-stepwise Max-R2 Apriori ARA validated the correlation and interpretation about FHR features of late FGR. In conclusion, the analysis results are consistent with clinical knowledge on late FGR and help screen late FGR in antepartum fetal monitoring.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"49 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131009264","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}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995438
Yuening Qu, Chengxin He, Jin Yin, Zhenjiang Zhao, Jingyu Chen, Lei Duan
Drug-target interaction (DTI) prediction serves as the foundation of new drug findings and drug repositioning. For drugs/targets, the sequence data contains the biological structural information, while the heterogeneous network contains the biochemical functional information. These two types of information describe different aspects of drugs and targets. Due to the complexity of DTI machinery, it is necessary to learn the representation from multiple perspectives. We hereby try to design a way to leverage information from multi-source data to the maximum extent and find a strategy to fuse them. To address the above challenges, we propose a model, named MOVE (short for integrating multi-source information for predicting DTI via cross-view contrastive 1earning), for learning comprehensive representations of each drug and target from multi-source data. MOVE extracts information from the sequence view and the network view, then utilizes a fusion module with auxiliary contrastive learning to facilitate the fusion of representations. Experimental results on the benchmark dataset demonstrate that MOVE is effective in DTI prediction.
{"title":"MOVE: Integrating Multi-source Information for Predicting DTI via Cross-view Contrastive Learning","authors":"Yuening Qu, Chengxin He, Jin Yin, Zhenjiang Zhao, Jingyu Chen, Lei Duan","doi":"10.1109/BIBM55620.2022.9995438","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995438","url":null,"abstract":"Drug-target interaction (DTI) prediction serves as the foundation of new drug findings and drug repositioning. For drugs/targets, the sequence data contains the biological structural information, while the heterogeneous network contains the biochemical functional information. These two types of information describe different aspects of drugs and targets. Due to the complexity of DTI machinery, it is necessary to learn the representation from multiple perspectives. We hereby try to design a way to leverage information from multi-source data to the maximum extent and find a strategy to fuse them. To address the above challenges, we propose a model, named MOVE (short for integrating multi-source information for predicting DTI via cross-view contrastive 1earning), for learning comprehensive representations of each drug and target from multi-source data. MOVE extracts information from the sequence view and the network view, then utilizes a fusion module with auxiliary contrastive learning to facilitate the fusion of representations. Experimental results on the benchmark dataset demonstrate that MOVE is effective in DTI prediction.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127612824","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}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995597
M. N. I. Suvon, P. C. Tripathi, S. Alabed, A. Swift, Haiping Lu
Pulmonary Arterial Hypertension (PAH) is a lifethreatening disorder. The prediction of mortality in PAH patients can play a crucial role in the clinical management of this disease. The prediction of mortality from one modality is a difficult task that may only provide limited performance. Therefore, we propose a multimodal learning approach in this work to predict one-year mortality in PAH patients. We have utilised three modalities, which include extracted numerical imaging features, echo report categorical features, and echo report text features from Electronic Health Records (EHRs) of patients. We have proposed a feature integration module to combine features from multiple modalities. The text features have been extracted from the echo reports using the Bidirectional Encoder Representations from Transformers (BERT). An attention mechanism and a weighted summation method are also adopted during the process of feature integration. We have performed different experiments to evaluate the performance of the proposed framework for mortality prediction. The experimental results indicate that we can achieve the best AUC score of 0.89 for predicting one-year mortality by combining all three modalities. The source code of this paper is available at https://github.com/Mdnaimulislam/MultimodalTab.
{"title":"Multimodal Learning for Predicting Mortality in Patients with Pulmonary Arterial Hypertension","authors":"M. N. I. Suvon, P. C. Tripathi, S. Alabed, A. Swift, Haiping Lu","doi":"10.1109/BIBM55620.2022.9995597","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995597","url":null,"abstract":"Pulmonary Arterial Hypertension (PAH) is a lifethreatening disorder. The prediction of mortality in PAH patients can play a crucial role in the clinical management of this disease. The prediction of mortality from one modality is a difficult task that may only provide limited performance. Therefore, we propose a multimodal learning approach in this work to predict one-year mortality in PAH patients. We have utilised three modalities, which include extracted numerical imaging features, echo report categorical features, and echo report text features from Electronic Health Records (EHRs) of patients. We have proposed a feature integration module to combine features from multiple modalities. The text features have been extracted from the echo reports using the Bidirectional Encoder Representations from Transformers (BERT). An attention mechanism and a weighted summation method are also adopted during the process of feature integration. We have performed different experiments to evaluate the performance of the proposed framework for mortality prediction. The experimental results indicate that we can achieve the best AUC score of 0.89 for predicting one-year mortality by combining all three modalities. The source code of this paper is available at https://github.com/Mdnaimulislam/MultimodalTab.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"538 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127647165","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}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995365
Baihan Lin
The absence of a conventional association between the cell-cell cohabitation and its emergent dynamics into cliques during development has hindered our understanding of how cell populations proliferate, differentiate, and compete, i.e. the cell ecology. With the recent advancement of the single-cell RNA-sequencing (RNA-seq), we can potentially describe such a link by constructing network graphs that characterize the similarity of the gene expression profiles of the cell-specific transcriptional programs, and analyzing these graphs systematically using the summary statistics informed by the algebraic topology. We propose the single-cell topological simplicial analysis (scTSA). Applying this approach to the single-cell gene expression profiles from local networks of cells in different developmental stages with different outcomes reveals a previously unseen topology of cellular ecology. These networks contain an abundance of cliques of single-cell profiles bound into cavities that guide the emergence of more complicated habitation forms. We visualize these ecological patterns with topological simplicial architectures of these networks, compared with the null models. Benchmarked on the single-cell RNA-seq data of zebrafish embryogenesis spanning 38,731 cells, 25 cell types and 12 time steps, our approach highlights the gastrulation as the most critical stage, consistent with consensus in developmental biology. As a nonlinear, model-independent, and unsupervised framework, our approach can also be applied to tracing multi-scale cell lineage, identifying critical stages, or creating pseudo-time series.11For an extended version of this work and a systematic evaluation of our approach, please refer to [1] for more details.
{"title":"Single-Cell Topological Simplicial Analysis Reveals Higher-Order Cellular Complexity","authors":"Baihan Lin","doi":"10.1109/BIBM55620.2022.9995365","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995365","url":null,"abstract":"The absence of a conventional association between the cell-cell cohabitation and its emergent dynamics into cliques during development has hindered our understanding of how cell populations proliferate, differentiate, and compete, i.e. the cell ecology. With the recent advancement of the single-cell RNA-sequencing (RNA-seq), we can potentially describe such a link by constructing network graphs that characterize the similarity of the gene expression profiles of the cell-specific transcriptional programs, and analyzing these graphs systematically using the summary statistics informed by the algebraic topology. We propose the single-cell topological simplicial analysis (scTSA). Applying this approach to the single-cell gene expression profiles from local networks of cells in different developmental stages with different outcomes reveals a previously unseen topology of cellular ecology. These networks contain an abundance of cliques of single-cell profiles bound into cavities that guide the emergence of more complicated habitation forms. We visualize these ecological patterns with topological simplicial architectures of these networks, compared with the null models. Benchmarked on the single-cell RNA-seq data of zebrafish embryogenesis spanning 38,731 cells, 25 cell types and 12 time steps, our approach highlights the gastrulation as the most critical stage, consistent with consensus in developmental biology. As a nonlinear, model-independent, and unsupervised framework, our approach can also be applied to tracing multi-scale cell lineage, identifying critical stages, or creating pseudo-time series.11For an extended version of this work and a systematic evaluation of our approach, please refer to [1] for more details.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127989598","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}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995322
Seokwoo Lee, Myounghoon Cho, Wook Lee, B. Park, Kyungsook Han
As the most common cause of cancer death, metastasis is a complex process that involves the spread of cancer cells from the original site to other parts of the body. Diagnosis of metastasis is usually confirmed by clinical examinations and imaging, but such diagnosis is made after metastasis occurs. Early detection of metastasis plays an important role in treatment planning, which in turn has an impact on the survival of patients. So far a few methods have been developed to predict lymph node metastasis, but few methods are available for predicting distant metastasis. Motivated by a recently known gene regulation mechanism involving miRNAs, we developed a new method for predicting both lymph node metastasis and distant metastasis. We identified differential correlations of miRNAs and their target RNAs in cancer, and built prediction models using the differential correlations. Testing the method on several types of cancer showed that differential correlations of miRNAs and their target RNAs are much more powerful than expressions of known metastasis predictive genes in predicting distant metastasis as well as lymph node metastasis. Although preliminary, the method developed in this study will be useful in predicting metastasis and thereby in determining treatment options for cancer patients.
{"title":"Predicting Lymph Node Metastasis and Distant Metastasis using Differential Correlations of miRNAs and Their Target RNAs in Cancer","authors":"Seokwoo Lee, Myounghoon Cho, Wook Lee, B. Park, Kyungsook Han","doi":"10.1109/BIBM55620.2022.9995322","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995322","url":null,"abstract":"As the most common cause of cancer death, metastasis is a complex process that involves the spread of cancer cells from the original site to other parts of the body. Diagnosis of metastasis is usually confirmed by clinical examinations and imaging, but such diagnosis is made after metastasis occurs. Early detection of metastasis plays an important role in treatment planning, which in turn has an impact on the survival of patients. So far a few methods have been developed to predict lymph node metastasis, but few methods are available for predicting distant metastasis. Motivated by a recently known gene regulation mechanism involving miRNAs, we developed a new method for predicting both lymph node metastasis and distant metastasis. We identified differential correlations of miRNAs and their target RNAs in cancer, and built prediction models using the differential correlations. Testing the method on several types of cancer showed that differential correlations of miRNAs and their target RNAs are much more powerful than expressions of known metastasis predictive genes in predicting distant metastasis as well as lymph node metastasis. Although preliminary, the method developed in this study will be useful in predicting metastasis and thereby in determining treatment options for cancer patients.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131352261","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}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9994977
Gabrielle Dagasso, M. Wilms, N. Forkert
Medical images, such as magnetic resonance or computed tomography, are increasingly being used to investigate the genetic architecture of neurological diseases like Alzheimer’s disease, or psychiatric disorders like attention-deficit hyperactivity disorder. The quantified global or regional brain imaging measures are commonly known as imaging-specific or -derived phenotypes (IDPs) when conducting genotype-phenotype association studies. Inclusion of whole medical images rather than derived tabular data as IDPs has been done by either a voxelwise approach or a global approach of whole medical images via principal component analysis. Limitations with multiple testing and inability to isolate high variation regions within the principal components arise with either of these approaches. This work proposes a principal component analysis-like localised approach of dimensionality reduction using diffeomorphic morphometry allowing for the selection of distances to model more regional effects. The main benefit of the proposed method is that it can can reduce the dimensionality of the problem considerably in comparison to the medical image’s variability it is describing while grouping spatial information potentially lost in dimensionality reduction techniques like principal component analyses. Moreover, the approach not only allows to include locality in the analysis but can also be used as a generative model to explore the morphometric changes across an axis of particular components of interest. To demonstrate the feasibility of this pipeline for inclusion in a multivariate genome-wide association study, it was applied to 1,359 subjects from the Adolescent Brain Cognitive Development Study for traits related to attention-deficit disorder. The results show that the proposed method can identify more specific morphometric features associated with genome regions.
{"title":"A morphometrics approach for inclusion of localised characteristics from medical imaging studies into genome-wide association studies","authors":"Gabrielle Dagasso, M. Wilms, N. Forkert","doi":"10.1109/BIBM55620.2022.9994977","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9994977","url":null,"abstract":"Medical images, such as magnetic resonance or computed tomography, are increasingly being used to investigate the genetic architecture of neurological diseases like Alzheimer’s disease, or psychiatric disorders like attention-deficit hyperactivity disorder. The quantified global or regional brain imaging measures are commonly known as imaging-specific or -derived phenotypes (IDPs) when conducting genotype-phenotype association studies. Inclusion of whole medical images rather than derived tabular data as IDPs has been done by either a voxelwise approach or a global approach of whole medical images via principal component analysis. Limitations with multiple testing and inability to isolate high variation regions within the principal components arise with either of these approaches. This work proposes a principal component analysis-like localised approach of dimensionality reduction using diffeomorphic morphometry allowing for the selection of distances to model more regional effects. The main benefit of the proposed method is that it can can reduce the dimensionality of the problem considerably in comparison to the medical image’s variability it is describing while grouping spatial information potentially lost in dimensionality reduction techniques like principal component analyses. Moreover, the approach not only allows to include locality in the analysis but can also be used as a generative model to explore the morphometric changes across an axis of particular components of interest. To demonstrate the feasibility of this pipeline for inclusion in a multivariate genome-wide association study, it was applied to 1,359 subjects from the Adolescent Brain Cognitive Development Study for traits related to attention-deficit disorder. The results show that the proposed method can identify more specific morphometric features associated with genome regions.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128766219","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}
Pub Date : 2022-12-06DOI: 10.1109/bibm55620.2022.9995500
{"title":"Computational Solutions to Explore Genomic 3D Organization","authors":"","doi":"10.1109/bibm55620.2022.9995500","DOIUrl":"https://doi.org/10.1109/bibm55620.2022.9995500","url":null,"abstract":"","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125359277","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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