Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics最新文献_第6页

Scoliosis management through apps 通过应用程序管理脊柱侧凸

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3535508.3545592

Lorella Bottino, Marzia Settino, M. Cannataro

{"title":"Scoliosis management through apps","authors":"Lorella Bottino, Marzia Settino, M. Cannataro","doi":"10.1145/3535508.3545592","DOIUrl":"https://doi.org/10.1145/3535508.3545592","url":null,"abstract":"Scoliosis is a curvature of the spine often found in adolescents. Commonly the management of patients with scoliosis is done through manual methods or web applications. Web applications require the doctor or patient to upload data, after taking scoliosis measurements with some separate instrument. More recently, applications that can be downloaded on smartphones (the so called apps), have been integrated into the clinical practice of scoliosis management. These applications allow to take scoliosis measurements directly, without the need to upload data by the user, thanks to the use of the smartphone sensors. In this paper, we first define some qualitative criteria to evaluate such apps and then we evaluate some relevant apps for scoliosis management. The criteria of evaluation taken into consideration include: Availability, Technology, Measurement, Functions and Qualitative evaluation. Each criterion represents an aspect of the apps and serves to characterize them. Apps-based scoliosis management offers several advantages both on the doctor and on the patient side. For example, from the patient's point of view the app may be useful to continuously and easily monitor scoliosis at home, while the doctor can monitor the scoliosis evolution along the time reducing the number of visits to the patient.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116654326","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}

引用次数: 1

Greedy and speedy: optimal vaccination strategies in multi-region heterogeneous networks 贪婪与快速:多区域异质网络中的最优疫苗接种策略

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3535508.3545107

Jhonatan Tavori, H. Levy

引用次数: 0

rCom

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3535508.3545514

Honglin Wang, Pujan Joshi, Chenyu Zhang, Peter F. Maye, D. W. Rowe, Dong-Guk Shin

{"title":"rCom","authors":"Honglin Wang, Pujan Joshi, Chenyu Zhang, Peter F. Maye, D. W. Rowe, Dong-Guk Shin","doi":"10.1145/3535508.3545514","DOIUrl":"https://doi.org/10.1145/3535508.3545514","url":null,"abstract":"With recent advances of single cell RNA (scRNA) sequencing technology, several methods have been proposed to infer cell-cell communication by analyzing ligand-receptor pairs. However, existing methods have limited ways of using what we call \"prior knowledge\", i.e., what are already known (albeit incompletely) about the upstream for the ligand and the downstream for the receptor. In this paper, we present a novel framework, called rCom, capable of inferring cell-cell interactions by considering portions of pathways that would be associated with upstream of the ligand and downstream of receptors under examination. The rCom framework integrates knowledge from multiple biological databases including transcription factor-target database, ligand-receptor database and publicly available curated signaling pathway databases. We combine both algorithmic methods and heuristic rules to score how each putative ligand-receptor pair may matchup between all possible cell subtype pairs. Permutation test is performed to rank the hypothesized cell-cell communication routes. We performed a case study using single cell transcriptomic data from bone biology. Our literature survey suggests that rCom could be effective in discovering novel cell-cell communication relationships that have been only partially known in the field.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123182452","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}

引用次数: 0

The impact of gene sequence alignment and gene tree estimation error on summary-based species network estimation 基因序列比对和基因树估计误差对基于摘要的物种网络估计的影响

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3535508.3545559

Meijun Gao, Wei Wang, Kevin J. Liu

{"title":"The impact of gene sequence alignment and gene tree estimation error on summary-based species network estimation","authors":"Meijun Gao, Wei Wang, Kevin J. Liu","doi":"10.1145/3535508.3545559","DOIUrl":"https://doi.org/10.1145/3535508.3545559","url":null,"abstract":"Thanks in part to rapid advances in next-generation sequencing technologies, recent phylogenomic studies have demonstrated the pivotal role that non-tree-like evolution plays in many parts of the Tree of Life - the evolutionary history of all life on Earth. As such, the Tree of Life is not necessarily a tree at all, but is better described by more general graph structures such as a phylogenetic network. Another key ingredient in these advances consists of the computational methods needed for reconstructing phylogenetic networks from large-scale genomic sequence data. But virtually all of these methods either require multiple sequence alignments (MSAs) as input or utilize gene trees or other inputs that are computed using MSAs. All of the input MSAs and gene trees must be estimated on empirical data. The methods themselves do not directly account for upstream estimation error, and, apart from prior studies of phylogenetic tree reconstruction and anecdotal evidence, little is understood about the impact of estimated MSA and gene tree error on downstream species network reconstruction. We therefore undertake a performance study to quantify the impact of MSA error and gene tree error on state-of-the-art phylogenetic network inference methods. Our study utilizes synthetic benchmarking data as well as genomic sequence data from mosquito and yeast. We find that upstream MSA and gene tree estimation error can have first-order effects on the accuracy of downstream network reconstruction and, to a lesser extent, its computational runtime. The effects become more pronounced on more challenging datasets with greater evolutionary divergence and more sampled taxa. Our findings highlight an important need for computational methods development: namely, scalable methods are needed to account for estimated MSA and gene tree error when reconstructing phylogenetic networks using unaligned biomolecular sequence data.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123624343","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}

引用次数: 0

Robust self-training strategy for various molecular biology prediction tasks 各种分子生物学预测任务的鲁棒自我训练策略

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3535508.3545998

Hehuan Ma, Feng Jiang, Yu Rong, Yuzhi Guo, Junzhou Huang

{"title":"Robust self-training strategy for various molecular biology prediction tasks","authors":"Hehuan Ma, Feng Jiang, Yu Rong, Yuzhi Guo, Junzhou Huang","doi":"10.1145/3535508.3545998","DOIUrl":"https://doi.org/10.1145/3535508.3545998","url":null,"abstract":"Molecular biology prediction tasks suffer the limited labeled data problem since it normally demands a series of professional experiments to label the target molecule. Self-training is one of the semi-supervised learning paradigms that utilizes both labeled and unlabeled data. It trains a teacher model on labeled data, and uses it to generate pseudo labels for unlabeled data. The labeled and pseudo-labeled data are then combined to train a student model. However, the pseudo labels generated from the teacher model are not sufficiently accurate. Thus, we propose a robust self-training strategy by exploring robust loss function to handle such noisy labels, which is model and task agnostic, and can be easily embedded with any prediction tasks. We have conducted molecular biology prediction tasks to gradually evaluate the performance of proposed robust self-training strategy. The results demonstrate that the proposed method consistently boosts the prediction performance, especially for molecular regression tasks, which have gained a 41.5% average improvement.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116334389","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}

引用次数: 0

Examining post-pandemic behaviors influencing human mobility trends 审查影响人类流动趋势的大流行后行为

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3535508.3545552

Satyaki Roy, Preetam Ghosh

{"title":"Examining post-pandemic behaviors influencing human mobility trends","authors":"Satyaki Roy, Preetam Ghosh","doi":"10.1145/3535508.3545552","DOIUrl":"https://doi.org/10.1145/3535508.3545552","url":null,"abstract":"COVID-19 unleashed a global pandemic that has resulted in human, economic, and social crises of unprecedented scale. While the efficacy of mobility restrictions in curbing contagion has been scientifically and empirically acknowledged, a deeper understanding of the human behavioral trends driving the mixed adoption of mobility restrictions will aid future policymaking. In this paper, we employ associative rule-mining and regression to pinpoint socioeconomic and demographic factors influencing the evolving mobility trends. We compare and contrast short-distance and long-distance trips by analyzing Chicago county-level and US state-level mobility. Our study yields rules that explain the changing propensity in trip length and the collective effect of population density, economic standing, COVID testing, and the number of infected cases on mobility decisions. Through regression and correlation analysis, we show the influence of ethnic and demographic factors and perception of infection on short and long-distance trips. We find that the new mobility rules correspond to reduced long- and short-distance trip frequencies. We graphically demonstrate a marked decline in the proportion of long county-level trips but a minor change in the distribution of state-level trips. Our correlation study highlights it is hard to characterize the effect of perception of infection spread on mobility decisions. We conclude the paper with a discussion on the overlap between the analysis in the existing literature on both during- and post-lockdown mobility trends and our findings.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114155489","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}

引用次数: 2

Statistical methodology for ribosomal frameshift detection 核糖体移码检测的统计方法

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3535508.3545529

Alisa Yurovsky, Justin Gardin, B. Futcher, S. Skiena

{"title":"Statistical methodology for ribosomal frameshift detection","authors":"Alisa Yurovsky, Justin Gardin, B. Futcher, S. Skiena","doi":"10.1145/3535508.3545529","DOIUrl":"https://doi.org/10.1145/3535508.3545529","url":null,"abstract":"During normal protein synthesis, the ribosome shifts along the messenger RNA (mRNA) by exactly three nucleotides for each amino acid added to the protein being translated. However, in special cases, the sequence of the mRNA somehow induces the ribosome to slip, which shifts the \"reading frame\" in which the mRNA is translated, and gives rise to an otherwise unexpected protein. Such \"programmed frameshifts\" are well-known in viruses, including coronavirus, and a few cases of programmed frameshifting are also known in cellular genes. However, there is no good way, either experimental or informatic, to identify novel cases of programmed frameshifting. Thus it is possible that substantial numbers of cellular proteins generated by programmed frameshifting in human and other organisms remain unknown. Here, we build on prior works observing that data from ribosome profiling can be analyzed for anomalies in mRNA reading frame periodicity to identify putative programmed frameshifts. We develop a statistical framework to identify all likely (even for very low frameshifting rates) frameshift positions in a genome. We also develop a frameshift simulator for ribosome profiling data to verify our algorithm. We show high sensitivity of prediction on the simulated data, retrieving 97.4% of the simulated frameshifts. Furthermore, our method found all three of the known yeast genes with programmed frameshifts. Our results suggest there could be a large number of un-annotated alternative proteins in the yeast genome, generated by programmed frameshifting. This motivates further study and parallel investigations in the human genome.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115540787","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}

引用次数: 0

DeepViFi DeepViFi

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3535508.3545551

Utkrisht Rajkumar, Sara Javadzadeh, Mihir Bafna, D. Wu, Rose Yu, Jingbo Shang, V. Bafna

引用次数: 3

rG4detector rG4detector

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3535508.3545534

Maor Turner, Mira Barshai, Yaron Orenstein

{"title":"rG4detector","authors":"Maor Turner, Mira Barshai, Yaron Orenstein","doi":"10.1145/3535508.3545534","DOIUrl":"https://doi.org/10.1145/3535508.3545534","url":null,"abstract":"RNA G-quadruplexes (rG4s) are RNA secondary structures, which are formed by guanine-rich sequences and have important cellular functions. Thus, researchers would like to know where and when rG4s are formed throughout the transcriptome. Measuring rG4s experimentally is a long and lobarious process, and hence researchers often rely on computational methods to predict the rG4 propensity of a given RNA sequence. However, existing computational methods for rG4 propensity prediction are sub-optimal since they rely on specific sequence features and/or were trained on small datasets and without considering rG4 stability information. Here, we developed rG4detector, a convolutional neural network to predict the rG4 propensity of any given RNA sequence. We demonstrated that rG4detector outperforms existing methods over various transcriptomic datasets. In addition, we used rG4detector to detect potential rG4s in transcriptomic data, and showed that it improves detection performance compared to existing methods. Last, we interrogated rG4detector for the important features it learned and discovered known and novel molecular principles behind rG4 formation. We expect rG4detector to advance future rG4 research by accurate detection and propensity prediction of rG4s. The code, trained models, and processed datasets are publicly available via github.com/OrensteinLab/rG4detector.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121967270","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}

引用次数: 2

Deep graph learning to estimate protein model quality using structural constraints from multiple sequence alignments 利用多序列比对的结构约束来估计蛋白质模型质量的深度图学习

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics Pub Date : 2022-08-07 DOI: 10.1145/3535508.3545558

Mahdi Rahbar, R. Chauhan, Pankil Nimeshbhai Shah, Renzhi Cao, Dong Si, Jie Hou

{"title":"Deep graph learning to estimate protein model quality using structural constraints from multiple sequence alignments","authors":"Mahdi Rahbar, R. Chauhan, Pankil Nimeshbhai Shah, Renzhi Cao, Dong Si, Jie Hou","doi":"10.1145/3535508.3545558","DOIUrl":"https://doi.org/10.1145/3535508.3545558","url":null,"abstract":"Our perception of protein's function is highly related to our understanding of the protein's three-dimensional (3D) structure and how the structure is computationally predicted. Evaluating the quality of a predicted 3D structural model is crucial for protein structure prediction. In recent years, many research works have leveraged deep learning architectures for the protein structure prediction alongside combinations of massive protein features to evaluate the predicted model's quality. Most recent works have proven that the inter-residue distance and alignment-based coevolutionary information significantly improve the accuracy of protein structure prediction tasks. This work utilizes the structural constraints derived from multiple sequence alignments, powered by the deep graph convolutional neural network, to estimate the protein model accuracy (EMA). The method models protein structure as a connected graph, in which each node encodes the residue's structural information, and the edge represents the structural relationship between any pair of residues in a structure. We incorporate a new feature embedding block in deep graph learning that utilizes the convolution and self-attention technique to leverage sequence alignment information for high-accurate protein quality estimation. We benchmark our methods to other state-of-the-art quality assessment approaches on the CASP13 and CASP14 datasets. The results indicate the effectiveness of alignment-based features and attention-based graph learning in EMA problems and show an improvement of our method among the previous works.","PeriodicalId":354504,"journal":{"name":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122262371","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}

引用次数: 1