Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering最新文献

Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry. 基于变压器的从头肽测序，用于数据独立采集质谱。

Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering Pub Date : 2023-12-01 Epub Date: 2024-02-19 DOI: 10.1109/bibe60311.2023.00013

Shiva Ebrahimi, Xuan Guo

{"title":"Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry.","authors":"Shiva Ebrahimi, Xuan Guo","doi":"10.1109/bibe60311.2023.00013","DOIUrl":"https://doi.org/10.1109/bibe60311.2023.00013","url":null,"abstract":"Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively analyzing protein content within biological samples. This methodology is a cornerstone driving the advancement of proteomics. In recent years, substantial strides have been made in Data-Independent Acquisition (DIA) strategies, facilitating impartial and non-targeted fragmentation of precursor ions. The DIA-generated MS/MS spectra present a formidable obstacle due to their inherent high multiplexing nature. Each spectrum encapsulates fragmented product ions originating from multiple precursor peptides. This intricacy poses a particularly acute challenge in de novo peptide/protein sequencing, where current methods are ill-equipped to address the multiplexing conundrum. In this paper, we introduce Casanovo-DIA, a deep-learning model based on transformer architecture. It deciphers peptide sequences from DIA mass spectrometry data. Our results show significant improvements over existing STOA methods, including DeepNovo-DIA and PepNet. Casanovo-DIA enhances precision by 15.14% to 34.8%, recall by 11.62% to 31.94% at the amino acid level, and boosts precision by 59% to 81.36% at the peptide level. Integrating DIA data and our Casanovo-DIA model holds considerable promise to uncover novel peptides and more comprehensive profiling of biological samples. Casanovo-DIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/Casanovo-DIA.","PeriodicalId":87347,"journal":{"name":"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11044815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140873985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fusion Learning on Multiple-Tag RFID Measurements for Respiratory Rate Monitoring. 用于呼吸频率监测的多标签 RFID 测量融合学习。

Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering Pub Date : 2020-10-01 Epub Date: 2020-12-16 DOI: 10.1109/bibe50027.2020.00082

Stephen Hansen, Daniel Schwartz, Jesse Stover, Md Abu Saleh Tajin, William M Mongan, Kapil R Dandekar

{"title":"Fusion Learning on Multiple-Tag RFID Measurements for Respiratory Rate Monitoring.","authors":"Stephen Hansen, Daniel Schwartz, Jesse Stover, Md Abu Saleh Tajin, William M Mongan, Kapil R Dandekar","doi":"10.1109/bibe50027.2020.00082","DOIUrl":"10.1109/bibe50027.2020.00082","url":null,"abstract":"Future advances in the medical Internet of Things (IoT) will require sensors that are unobtrusive and passively powered. With the use of wireless, wearable, and passive knitted smart garment sensors, we monitor infant respiratory activity. We improve the utility of multi-tag Radio Frequency Identification (RFID) measurements via fusion learning across various features from multiple tags to determine the magnitude and temporal information of the artifacts. In this paper, we develop an algorithm that classifies and separates respiratory activity via a Regime Hidden Markov Model compounded with higher-order features of Minkowski and Mahalanobis distances. Our algorithm improves respiratory rate detection by increasing the Signal to Noise Ratio (SNR) on average from 17.12 dB to 34.74 dB. The effectiveness of our algorithm in increasing SNR shows that higher-order features can improve signal strength detection in RFID systems. Our algorithm can be extended to include more feature sources and can be used in a variety of machine learning algorithms for respiratory data classification, and other applications. Further work on the algorithm will include accurate parameterization of the algorithm's window size.","PeriodicalId":87347,"journal":{"name":"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130190/pdf/nihms-1701065.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39000982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semi-Supervised Classification of Noisy, Gigapixel Histology Images. 噪声、千兆像素组织学图像的半监督分类

Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering Pub Date : 2020-10-01 Epub Date: 2020-12-16 DOI: 10.1109/BIBE50027.2020.00097

J Vince Pulido, Shan Guleria, Lubaina Ehsan, Matthew Fasullo, Robert Lippman, Pritesh Mutha, Tilak Shah, Sana Syed, Donald E Brown

引用次数: 0

Deep Multiview Learning to Identify Population Structure with Multimodal Imaging. 基于多模态成像的深度多视图学习识别种群结构。

Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering Pub Date : 2020-10-01 Epub Date: 2020-12-16 DOI: 10.1109/bibe50027.2020.00057

Yixue Feng, Kefei Liu, Mansu Kim, Qi Long, Xiaohui Yao, Li Shen

{"title":"Deep Multiview Learning to Identify Population Structure with Multimodal Imaging.","authors":"Yixue Feng, Kefei Liu, Mansu Kim, Qi Long, Xiaohui Yao, Li Shen","doi":"10.1109/bibe50027.2020.00057","DOIUrl":"https://doi.org/10.1109/bibe50027.2020.00057","url":null,"abstract":"We present an effective deep multiview learning framework to identify population structure using multimodal imaging data. Our approach is based on canonical correlation analysis (CCA). We propose to use deep generalized CCA (DGCCA) to learn a shared latent representation of non-linearly mapped and maximally correlated components from multiple imaging modalities with reduced dimensionality. In our empirical study, this representation is shown to effectively capture more variance in original data than conventional generalized CCA (GCCA) which applies only linear transformation to the multi-view data. Furthermore, subsequent cluster analysis on the new feature set learned from DGCCA is able to identify a promising population structure in an Alzheimer's disease (AD) cohort. Genetic association analyses of the clustering results demonstrate that the shared representation learned from DGCCA yields a population structure with a stronger genetic basis than several competing feature learning methods.","PeriodicalId":87347,"journal":{"name":"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bibe50027.2020.00057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25422735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Nanopore Guided Assembly of Segmental Duplications near Telomeres. 纳米孔引导组装端粒附近的片段复制。

Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering Pub Date : 2019-10-01 Epub Date: 2019-12-26 DOI: 10.1109/bibe.2019.00020

Eleni Adam, Tunazzina Islam, Desh Ranjan, Harold Riethman

引用次数: 0

Hybrid Modeling of Ebola Propagation. 埃博拉病毒传播的混合模型。

Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering Pub Date : 2019-10-01 Epub Date: 2019-12-26 DOI: 10.1109/bibe.2019.00044

Cyrus Tanade, Nathanael Pate, Elianna Paljug, Ryan A Hoffman, May D Wang

引用次数: 5

Knowledge-Based Biomedical Word Sense Disambiguation with Neural Concept Embeddings 基于知识的生物医学词义消歧与神经概念嵌入

Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering Pub Date : 2017-10-01 Epub Date: 2018-01-11 DOI: 10.1109/BIBE.2017.00-61

Akm Sabbir, Antonio Jimeno-Yepes, Ramakanth Kavuluru

{"title":"Knowledge-Based Biomedical Word Sense Disambiguation with Neural Concept Embeddings","authors":"Akm Sabbir, Antonio Jimeno-Yepes, Ramakanth Kavuluru","doi":"10.1109/BIBE.2017.00-61","DOIUrl":"10.1109/BIBE.2017.00-61","url":null,"abstract":"Biomedical word sense disambiguation (WSD) is an important intermediate task in many natural language processing applications such as named entity recognition, syntactic parsing, and relation extraction. In this paper, we employ knowledge-based approaches that also exploit recent advances in neural word/concept embeddings to improve over the state-of-the-art in biomedical WSD using the public MSH WSD dataset [1] as the test set. Our methods involve weak supervision - we do not use any hand-labeled examples for WSD to build our prediction models; however, we employ an existing concept mapping program, MetaMap, to obtain our concept vectors. Over the MSH WSD dataset, our linear time (in terms of numbers of senses and words in the test instance) method achieves an accuracy of 92.24% which is a 3% improvement over the best known results [2] obtained via unsupervised means. A more expensive approach that we developed relies on a nearest neighbor framework and achieves accuracy of 94.34%, essentially cutting the error rate in half. Employing dense vector representations learned from unlabeled free text has been shown to benefit many language processing tasks recently and our efforts show that biomedical WSD is no exception to this trend. For a complex and rapidly evolving domain such as biomedicine, building labeled datasets for larger sets of ambiguous terms may be impractical. Here, we show that weak supervision that leverages recent advances in representation learning can rival supervised approaches in biomedical WSD. However, external knowledge bases (here sense inventories) play a key role in the improvements achieved.","PeriodicalId":87347,"journal":{"name":"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5792196/pdf/nihms919324.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35792371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data. 基于术前电子健康记录数据的机器学习模型预测谵妄。

Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering Pub Date : 2017-10-01 Epub Date: 2018-01-11 DOI: 10.1109/BIBE.2017.00014

Anis Davoudi, Ashkan Ebadi, Parisa Rashidi, Tazcan Ozrazgat-Baslanti, Azra Bihorac, Alberto C Bursian

{"title":"Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data.","authors":"Anis Davoudi, Ashkan Ebadi, Parisa Rashidi, Tazcan Ozrazgat-Baslanti, Azra Bihorac, Alberto C Bursian","doi":"10.1109/BIBE.2017.00014","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00014","url":null,"abstract":"Electronic Health Records (EHR) are mainly designed to record relevant patient information during their stay in the hospital for administrative purposes. They additionally provide an efficient and inexpensive source of data for medical research, such as patient outcome prediction. In this study, we used preoperative Electronic Health Records to predict postoperative delirium. We compared the performance of seven machine learning models on delirium prediction: linear models, generalized additive models, random forests, support vector machine, neural networks, and extreme gradient boosting. Among the models evaluated in this study, random forests and generalized additive model outperformed the other models in terms of the overall performance metrics for prediction of delirium, particularly with respect to sensitivity. We found that age, alcohol or drug abuse, socioeconomic status, underlying medical issue, severity of medical problem, and attending surgeon can affect the risk of delirium.","PeriodicalId":87347,"journal":{"name":"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBE.2017.00014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36647396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 37

In silico assessment of the effects of material on stent deployment. 材料对支架展开影响的计算机评估。

Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering Pub Date : 2017-10-01 Epub Date: 2018-01-11 DOI: 10.1109/BIBE.2017.00-11

Georgia S Karanasiou, Nikolaos S Tachos, Antonios Sakellarios, Lampros K Michalis, Claire Conway, Elazer R Edelman, Dimitrios I Fotiadis

{"title":"In silico assessment of the effects of material on stent deployment.","authors":"Georgia S Karanasiou, Nikolaos S Tachos, Antonios Sakellarios, Lampros K Michalis, Claire Conway, Elazer R Edelman, Dimitrios I Fotiadis","doi":"10.1109/BIBE.2017.00-11","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-11","url":null,"abstract":"Coronary stents are expandable scaffolds that are used to widen occluded diseased arteries and restore blood flow. Because of the strain they are exposed to and forces they must resist as well as the importance of surface interactions, material properties are dominant. Indeed, a common differentiating factors amongst commercially available stents is their material. Several performance requirements relate to stent materials including radial strength for adequate arterial support post-deployment. This study investigated the effect of the stent material in three finite element models using different stents made of: (i) Cobalt-Chromium (CoCr), (ii) Stainless Steel (SS316L), and (iii) Platinum Chromium (PtCr). Deployment was investigated in a patient specific arterial geometry, created based on a fusion of angiographic data and intravascular ultrasound images. In silico results show that: (i) the maximum von Mises stress occurs for the CoCr, however the curved areas of the stent links present higher stresses compared to the straight stent segments for all stents, (ii) more areas of high inner arterial stress exist in the case of the CoCr stent deployment, (iii) there is no significant difference in the percentage of arterial stress volume distribution among all models.","PeriodicalId":87347,"journal":{"name":"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBE.2017.00-11","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36371103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Identification of signaling pathways related to drug efficacy in hepatocellular carcinoma via integration of phosphoproteomic, genomic and clinical data. 通过整合磷蛋白组学、基因组学和临床数据，鉴定与肝细胞癌药物疗效相关的信号通路。

Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering Pub Date : 2013-11-01 DOI: 10.1109/BIBE.2013.6701683

Ioannis N Melas, Douglas A Lauffenburger, Leonidas G Alexopoulos

{"title":"Identification of signaling pathways related to drug efficacy in hepatocellular carcinoma via integration of phosphoproteomic, genomic and clinical data.","authors":"Ioannis N Melas, Douglas A Lauffenburger, Leonidas G Alexopoulos","doi":"10.1109/BIBE.2013.6701683","DOIUrl":"https://doi.org/10.1109/BIBE.2013.6701683","url":null,"abstract":"Hepatocellular Carcinoma (HCC) is one of the leading causes of death worldwide, with only a handful of treatments effective in unresectable HCC. Most of the clinical trials for HCC using new generation interventions (drug-targeted therapies) have poor efficacy whereas just a few of them show some promising clinical outcomes [1]. This is amongst the first studies where the mode of action of some of the compounds extensively used in clinical trials is interrogated on the phosphoproteomic level, in an attempt to build predictive models for clinical efficacy. Signaling data are combined with previously published gene expression and clinical data within a consistent framework that identifies drug effects on the phosphoproteomic level and translates them to the gene expression level. The interrogated drugs are then correlated with genes differentially expressed in normal versus tumor tissue, and genes predictive of patient survival. Although the number of clinical trial results considered is small, our approach shows potential for discerning signaling activities that may help predict drug efficacy for HCC.","PeriodicalId":87347,"journal":{"name":"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBE.2013.6701683","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33094824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5