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ORCID12th BCB 2021: Gainesville, Florida, USA
- Hongmei Jiang, Xiuzhen Huang, Jiajie Zhang:
BCB '21: 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, Gainesville, Florida, USA, August 1-4, 2021. ACM 2021, ISBN 978-1-4503-8450-6
Sequence analysis
- Hang Su, Ziwei Chen, Maya L. Najarian, Martin T. Ferris, Fernando Pardo-Manuel de Villena, Leonard McMillan
:
A k-mer query tool for assessing population diversity in pangenomes. 1:1-1:9 - Marie Hoffmann, Michael T. Monaghan, Knut Reinert:
PriSeT: efficient de novo primer discovery. 2:1-2:12 - Elizabeth R. Koning, Malachi Phillips, Tandy J. Warnow:
ppIacerDC: a new scalable phylogenetic placement method. 3:1-3:9 - Yael Ben-Ari, Dan Flomin, Lianrong Pu, Yaron Orenstein, Ron Shamir:
Improving the efficiency of de Bruijn graph construction using compact universal hitting sets. 4:1-4:9
Electronic health records
- Aishwarya Mandyam, Elizabeth C. Yoo, Jeff Soules, Krzysztof Laudanski, Barbara E. Engelhardt:
COP-E-CAT: cleaning and organization pipeline for EHR computational and analytic tasks. 5:1-5:9 - Ziyang Song, Xavier Sumba Toral, Yixin Xu, Aihua Liu, Liming Guo, Guido Powell, Aman Verma, David L. Buckeridge, Ariane Marelli, Yue Li:
Supervised multi-specialist topic model with applications on large-scale electronic health record data. 6:1-6:26 - Mehak Gupta, Thao-Ly T. Phan, H. Timothy Bunnell, Rahmatollah Beheshti:
Concurrent imputation and prediction on EHR data using bi-directional GANs: Bi-GANs for EHR imputation and prediction. 7:1-7:9 - Tanbir Ahmed, Md Momin Al Aziz, Noman Mohammed, Xiaoqian Jiang:
Privacy preserving neural networks for electronic health records de-identification. 8:1-8:6 - Kai Zhang, Xiaoqian Jiang, Mahboubeh Madadi, Luyao Chen, Sean I. Savitz, Shayan Shams:
DBNet: a novel deep learning framework for mechanical ventilation prediction using electronic health records. 9:1-9:8
System biology
- Xiaofei Zhang, Ye Yu, Chan Hee Mok, James N. MacLeod, Jinze Liu:
Gazelle: transcript abundance query against large-scale RNA-seq experiments. 10:1-10:8 - Jonathan Karin, Hagai Michel, Yaron Orenstein:
MultiRBP: multi-task neural network for protein-RNA binding prediction. 11:1-11:9 - Chunrui Xu, Yang Cao:
A spatiotemporal model of polarity and spatial gradient establishment in caulobacter crescentus. 12:1-12:10 - Bradley Feiger, Erick Lorenzana, David Ranney, Muath Bishawi, Julie Doberne, Andrew Vekstein, Soraya Voigt, G. Chad Hughes, Amanda Randles:
Predicting aneurysmal degeneration of type B aortic dissection with computational fluid dynamics. 13:1-13:6
Genomic variation
- Anwica Kashfeen, Leonard McMillan:
Frontier: finding the boundaries of novel transposable element insertions in genomes. 14:1-14:10 - Meijun Gao, Kevin J. Liu:
Statistical analysis of GC-biased gene conversion and recombination hotspots in eukaryotic genomes: a phylogenetic hidden Markov model-based approach. 15:1-15:24 - Jaroslaw Paszek, Oliver Eulenstein, Pawel Górecki:
Novel genomic duplication models through integer linear programming. 16:1-16:11
Health monitoring & phenotyping
- Xianlong Zeng, Simon M. Lin, Chang Liu:
Transformer-based unsupervised patient representation learning based on medical claims for risk stratification and analysis. 17:1-17:9 - Zongxing Xie, Bing Zhou, Fan Ye:
Signal quality detection towards practical non-touch vital sign monitoring. 18:1-18:9 - Sara Nouri Golmaei, Xiao Luo:
DeepNote-GNN: predicting hospital readmission using clinical notes and patient network. 19:1-19:9 - Youjia Zhou, Methun Kamruzzaman, Patrick S. Schnable, Bala Krishnamoorthy, Ananth Kalyanaraman, Bei Wang:
Pheno-mapper: an interactive toolbox for the visual exploration of phenomics data. 20:1-20:10
Structural bioinformatics
- Russell B. Davidson, Mathialakan Thavappiragasam, T. Chad Effler, Jess Woods, Dwayne A. Elias, Jerry M. Parks, Ada Sedova:
Modeling protein structures from predicted contacts with modern molecular dynamics potentials: accuracy, sensitivity, and refinement. 21:1-21:10 - Linkel Boateng, Anita Nag, Homayoun Valafar:
Computational modeling of SARS-CoV-2 Nsp1 binding to human ribosomal 40S complex. 22:1-22:6
Single cell omics
- Jinpeng Liu, Xinan Liu, Ye Yu, Chi Wang, Jinze Liu:
FastCount: a fast gene count software for single-cell RNA-seq data. 23:1-23:8 - Daniel N. Baker, Nathan Dyjack, Vladimir Braverman, Stephanie C. Hicks, Ben Langmead:
Fast and memory-efficient scRNA-seq k-means clustering with various distances. 24:1-24:8 - Russell A. Li, Zhandong Liu:
A hybrid deep neural network for robust single-cell genome-wide DNA methylation detection. 25:1-25:6 - Fatima Zare, Jacob Stark, Sheida Nabavi:
Copy number variation detection using single cell sequencing data. 26:1-26:6
Machine learning & drug design
- Tianfan Fu, Cao Xiao, Kexin Huang, Lucas M. Glass, Jimeng Sun:
SPEAR: self-supervised post-training enhancer for molecule optimization. 27:1-27:10 - Natalia Khuri, Sarah Parsons:
A value-based approach for training of classifiers with high-throughput small molecule screening data. 28:1-28:10 - Amir Hosein Safari, Nafiseh Sedaghat, Hooman Zabeti, Alpha Forna, Leonid Chindelevitch, Maxwell W. Libbrecht:
Predicting drug resistance in M. tuberculosis using a long-term recurrent convolutional network. 29:1-29:10 - Lizhen Shi, Bo Chen:
LSHvec: a vector representation of DNA sequences using locality sensitive hashing and FastText word embeddings. 30:1-30:10
Medical imaging
- Hugo Michard, Bertrand Luvison, Quoc Cuong Pham, Antonio J. Morales-Artacho, Gaël Guilhem:
AW-Net: automatic muscle structure analysis on B-mode ultrasound images for injury prevention. 31:1-31:9 - Anthony Rios, Eric B. Durbin, Isaac Hands, Ramakanth Kavuluru:
Assigning ICD-O-3 codes to pathology reports using neural multi-task training with hierarchical regularization. 32:1-32:10 - Zhao Li, Rongbin Li, Kendall J. Kiser, Luca Giancardo, W. Jim Zheng:
Segmenting thoracic cavities with neoplastic lesions: a head-to-head benchmark with fully convolutional neural networks. 33:1-33:8 - Martha Rebeca Canales-Fiscal, Rocío Ortiz López, Regina Barzilay, Victor Treviño, Servando Cardona-Huerta, Luis Javier Ramírez-Treviño, Adam Yala, José G. Tamez-Peña:
COVID-19 classification using thermal images: thermal images capability for identifying COVID-19 using traditional machine learning classifiers. 34:1-34:5 - Kenji Fujimoto, Tsubasa Mizugaki, Utkrisht Rajkumar, Hironori Shigeta, Shigeto Seno, Yutaka Uchida, Masaru Ishii, Vineet Bafna, Hideo Matsuda:
A CNN-based cell tracking method for multi-slice intravital imaging data. 35:1-35:7
Graphs & networks
- Jack Lanchantin, Tom Weingarten, Arshdeep Sekhon, Clint Miller, Yanjun Qi:
Transfer learning for predicting virus-host protein interactions for novel virus sequences. 36:1-36:10 - Anuj Godase, Md. Khaledur Rahman, Ariful Azad:
GNNfam: utilizing sparsity in protein family predictions using graph neural networks. 37:1-37:10 - Lisa Oh, Bowen Dai, Chris Bailey-Kellogg:
A multi-resolution graph convolution network for contiguous epitope prediction. 38:1-38:10 - Erman Ayday, Youngjin Yoo, Anisa Halimi:
ShareTrace: an iterative message passing algorithm for efficient and effective disease risk assessment on an interaction graph. 39:1-39:6 - Zican Li, Wooyoung Kim:
Investigating statistical analysis for network motifs. 40:1-40:6
COVID-19
- Shayom Debopadhaya, John S. Erickson, Kristin P. Bennett:
Temporal analysis of social determinants associated with COVID-19 mortality. 41:1-41:10 - Tarun Naren, Yuanda Zhu, May Dongmei Wang:
COVID-19 diagnosis using model agnostic meta-learning on limited chest X-ray images. 42:1-42:9 - Christopher Whitfield, Yang Liu, Mohd Anwar:
Surveillance of COVID-19 pandemic using social media: a reddit study in North Carolina. 43:1-43:8 - Lodewijk Brand, Lauren Zoe Baker, Hua Wang:
A multi-instance support vector machine with incomplete data for clinical outcome prediction of COVID-19. 44:1-44:6
Clinical trials & outcome prediction
- Eric V. Strobl, Thomas A. Lasko:
Synthesized difference in differences. 45:1-45:10 - Xiao Shou, Tian Gao, Dharmashankar Subramanian, Kristin P. Bennett:
Match2: hybrid self-organizing map and deep learning strategies for treatment effect estimation. 46:1-46:10 - Haidong Yi, Natalie Stanley:
CytoSet: predicting clinical outcomes via set-modeling of cytometry data. 47:1-47:8 - Robert Lyons, Geoffrey Ross Low, Clare Bates Congdon, Melissa Ceruolo, Marissa Ballesteros, Steven Cambria, Paolo DePetrillo:
Towards an extensible ontology for streaming sensor data for clinical trials. 48:1-48:6 - Shubo Tian, Arslan Erdengasileng, Xi Yang, Yi Guo, Yonghui Wu, Jinfeng Zhang, Jiang Bian, Zhe He:
Transformer-based named entity recognition for parsing clinical trial eligibility criteria. 49:1-49:6
Cancer
- Bingjun Li, Tianyu Wang, Sheida Nabavi:
Cancer molecular subtype classification by graph convolutional networks on multi-omics data. 50:1-50:9 - Priyankar Bose, William C. Sleeman IV, Khajamoinuddin Syed, Michael Hagan, Jatinder Palta, Rishabh Kapoor, Preetam Ghosh:
Deep neural network models to automate incident triage in the radiation oncology incident learning system. 51:1-51:10 - Yibin Wang, William Neil Duggar, Toms V. Thomas, P. Russell Roberts, Linkan Bian, Haifeng Wang:
Extracapsular extension identification for head and neck cancer using multi-scale 3D deep neural network. 52:1-52:5
Ontologies & databases
- Lucas Jing Liu, Victor Ortiz-Soriano, Javier A. Neyra, Jin Chen:
KGDAL: knowledge graph guided double attention LSTM for rolling mortality prediction for AKI-D patients. 53:1-53:10 - Sunil Mohan, Rico Angell, Nicholas Monath, Andrew McCallum:
Low resource recognition and linking of biomedical concepts from a large ontology. 54:1-54:10 - Jiho Noh, Ramakanth Kavuluru:
Joint learning for biomedical NER and entity normalization: encoding schemes, counterfactual examples, and zero-shot evaluation. 55:1-55:10 - Zhuoyan Li, Sheng Wang:
HYPON: embedding biomedical ontology with entity sets. 56:1-56:7 - Ye Wu, Hing-Fung Ting, Tak Wah Lam, Ruibang Luo:
BioNumQA-BERT: answering biomedical questions using numerical facts with a deep language representation model. 57:1-57:6
BCB conference poster presentations
- Gun Woo (Warren) Park, Kevin Bryson:
LDEncoder: reference deep learning-based feature detector for transfer learning in the field of epigenomics. 58:1 - Christopher R. Beal, John G. Peters, Ronald J. Nowling:
Sequence model evaluation framework for STARR-seq peak calling. 59:1 - Md. Sadek Hossain Asif:
Developing a modified version of generative adversarial network to predict the potential anti-viral drug of COVID-19. 60:1 - Alice Feng:
Using electronic health records to accurately predict COVID-19 health outcomes through a novel machine learning pipeline. 61:1 - Rahmatullah Roche, Sutanu Bhattacharya, Debswapna Bhattacharya:
Hybridized distance- and contact- based hierarchical protein structure modeling using DConStruct. 62:1 - Viktor Zenkov, James O'Connor, Hayley McNamara, Ian A. Cockburn, Vitaly V. Ganusov:
Do microscopy imaging frequency and experiment duration impact the analysis of T cell movement? 63:1 - Yuhan Du, Anthony R. Rafferty, Fionnuala M. McAuliffe, Catherine Mooney:
Explaining large-for-gestational-age births: a random forest classifier with a novel local interpretation method. 64:1 - Mike Wong, Nayana Laxmeshwar, Rachit Joshi, Anagha Kulkarni:
Browsing weighted interactome models using GeneDive. 65:1 - Mike Wong, Saeideh Ghahghaei, Arvind Chandna, Anagha Kulkarni:
Scalable non-invasive pediatric cerebral visual impairment screening with the higher visual function question inventory (HVFQI). 66:1 - Seoungdeok Jeon, Zachary Colburn, Joshua Sakai, Ling-Hong Hung, Ka Yee Yeung:
Application of natural language processing and machine learning to radiology reports. 67:1 - Riya Gupta, Aditya M. Rao, Lara Murphy Jones, Purvesh Khatri:
Formulating a gene signature for diagnosis of autoimmune and infectious diseases. 68:1 - Oleg Shpynov, Roman Chernyatchik, Petr Tsurinov, Maxim N. Artyomov:
SPAN and JBR: analysis and visualization toolkit for peak calling. 69:1 - Petr Tsurinov, Oleg Shpynov, Nina Lukashina, Daria Likholetova, Maxim N. Artyomov:
FARM: hierarchical association rule mining and visualization method. 70:1 - Wangui Mbuguiro, Feilim Mac Gabhann:
Mechanistic model demonstrates importance of autocrine IL-8 secretion by neutrophils. 71:1 - Oleg Shpynov, Nikolai Kapralov:
PubTrends: a scientific literature explorer. 72:1 - Garrett Yoon, Vincent J. Major:
Probing automated treatment of urinary tract infections for bias: a case-study where machine learning perpetuates structural differences and racial disparities. 73:1 - Bahá El Kassaby, Kunde Ramamoorthy Govindarajan, Francisco Castellanos, Carol Bult:
MVAR: a mouse variation registry. 74:1 - Caroline Cannistra, Alex Yuan, Wenying Shou:
Inferring interaction networks from microbial time series data: it's not just finding a statistic. 75:1 - Cheng Chen, Stephen K. Grady, Sally R. Ellingson, Michael A. Langston:
Gene-disease-drug link prediction using tripartite graphs. 76:1 - Lauren Losin, Daniel Veltri:
Exploring target specificity of antimicrobial peptides through deep learning embeddings. 77:1 - Shravani Bobde, Fahad Alsaab, Guangshun Wang, Monique L. van Hoek:
Designing novel antimicrobial peptides against multi-drug resistant bacteria. 78:1 - Hannah Guan:
The genetics of human aging: predicting age and age-related diseases by deep mining high dimensional biomarker data. 79:1
ParBio workshop paper presentations
- Femi William, Feng Zhu:
CNN models for eye state classification using EEG with temporal ordering. 80:1-80:8 - Rossana Mancuso, Marzia Settino, Mario Cannataro:
Data mining for electroencephalogram signal processing and analysis. 81:1-81:10 - Jae-Seung Yeom, Konstantia Georgouli, Robert Blake, Ali Navid:
Towards dynamic simulation of a whole cell model. 82:1-82:10
HPC-BOD workshop paper presentations
- Ziynet Nesibe Kesimoglu, Serdar Bozdag:
SUPREME: a cancer subtype prediction methodology integrating multiple biological datatypes using graph convolutional neural networks. 83:1 - Yashu Vashishath, Serdar Bozdag:
GWAS analysis to compute genetic markers of progression to Alzheimer's disease. 84:1 - Umair Mohammad, Fahad Saeed:
Search feasibility in distributed MS-proteomics big data. 85:1 - Sumesh Kumar, Fahad Saeed:
Real-time peptide identification from high-throughput mass-spectrometry data. 86:1
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