Poster Presentations Schedule
DREAM Poster Session - Monday, 6:30 pm - 8:00 pm
Location: Rooms 104
RSG Poster Session Odd Numbers - Tuesday, 5:30 pm - 7:00 pm
Location: Rooms 104 & 116
RSG Poster Session Even Numbers - Wednesday, 5:30 pm - 7:00 pm
Location: Rooms 104 & 116
Presenters:
- If you are in the Odd Number Session, you may set up your poster any time between 8:30 am and Noon on Tuesday and must remove your poster by 8pm on Tuesday. All remaining posters will be discarded.
- If you are in the Even Number Session, you may set up your poster any time between 8:30 am and Noon on Wednesday and must remove your poster by 8pm on Wednsday. All remaining posters will be discarded.
Poster Display Size: When preparing accepted posters please note that your poster should not exceed the following dimensions: 46 inches wide by 45 inches high. There will be 2 posters per side on the each poster board.
As of October 8, 2019. Subject to change without notice.
DREAM Posters Schedule
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| # | Author(s) | Title |
| D1 | Pratheepa Jeganathan, Anne-maud Ferreira, Jathushan Rajasegaran, Susan Holmes | Predicting Gestational Age Using Transcriptomic data |
| D2 | HaoYang Zhang, Hanhui Li, Mingpeng Zhao, Yuedong Yang | A fusion model to predict gestational age prediction by integrating blood gene expression data |
| D3 | Harpreet Kaur, Sumeet Patiyal, Anjali Dhall, Neelam Sharma, Gajendra Raghava | Preterm Birth prediction from the Gene-expression profiles of Pregnant Women using Machine Learning Techniques |
| D4 | Hyelim Jung, Dawoon Leem, Hyungyu Lee, Woong Jeong, Junyoung Park, Bogyu Park | Ensemble Regression Method for Prediction Gestational Age |
| D5 | Houriiyah Tegally, Malawi Kiran Anmol, Shakuntala Baichoo | Using computational modelling to predict Artemisinin drug resistance from Plasmodium transcriptomics data for improved malaria therapeutics |
| D6 | Nicola Lawford, Jonathan Chan, Narumol Noungpan, Worrawat Engchuan | Functional Pathway-Based Feature Transformation of \textit{P. falciparum} Gene Expression Data for Artemisinin Resistance Prediction |
| D7 | Monica Gomez Orozco, Jahir Guitierrez Bugarin | Kernel Ridge Regression and Voting XGBoost Models for Prediction of Artemisin Resistance in P. falciparium parasites |
| D8 | Colby Ford | Ensemble Machine Learning Modeling for the Prediction of Artemisinin Resistance in Malaria |
| D9 | Jovan Tanevski, Thin Nguyen, Buu Truong, Nikos Karaiskos, Mehmet Eren Ahsen, Xinyu Zhang, Chang Shu, Ke Xu, Xiaoyu Liang, Ying Hu, Hoang V.V. Pham, Li Xiaomei, Thuc D. Le, Adi L. Tarca, Gaurav Bhatti, Roberto Romero, Nestoras Karathanasis, Phillipe Loher, Yang Chen, Zhengqing Ouyang, Disheng Mao, Yuping Zhang, Maryam Zand, Jianhua Ruan, Christoph Hafemeister, Peng Qiu, Duc Tran, Thin Nguyen, Attila Gabor, Thomas Yu, Enrico Glaab, Roland Krause, Peter Banda, Dream Sctc Consortium, Gustavo Stolovitzky, Nikolaus Rajewsky, Julio Saez-Rodriguez and Pablo Meyer | Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data |
| D10 | Adi Tarca |
Preterm Birth Prediction: Transcriptomics Challenge Overview Talk |
RSG Posters Schedule
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| RSG Posters - |
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| Go directly to: DREAM Posters | ||
| # | Author(s) | Title |
| 1 | Lautaro Soler | Image processing on the brain: 3D printing MRI images for further observation |
| 2 | Jia-Ying Su, En-Yu Lai, Chun-Houh Chen and Yen-Tsung Huang | Visualization for high-dimensional mediation effects (HDMV) with application to (epi)genome-wide mediation |
| 3 | Muhammad Muzammal Adeel | VARIATION OF THE 3D GENOME ARCHITECTURE IN CERVICAL CANCER DEVELOPMENT |
| 4 | Rania Hassan, Nourhan Abu-Shahba, Marwa Mahmoud, Ahmed M. H. Abdel-Fattah, Wael Zakaria and Mahmoud Elhefnawi | Co-regulatory Network of Oncosuppressor miRNAs and Transcription Factors for Pathology of Human Hepatic Cancer Stem Cells (HCSC) |
| 5 | Niels Nguedia Kaze, Wilfred Mbacham and Jean Paul Chedjou | CHARACTERIZATION OF 331G/A POLYMORPHISM OF RP GENE AND IDENTIFICATION OF VIRAL ONCOGENE HMTV VIRUS AS GENETIC MARKERS FOR THE IMPROVEMENT OF BREAST CANCER MANAGEMENT IN CAMEROON |
| 6 | Rohit Arora, Harry M. Burke and Ramy Arnaout | Immunological Diversity with Similarity |
| 7 | Qian Li, Hemang Parikh, Martha Butterworth, Åke Lernmark, William Hagopian, Marian Rewers, Jin-Xiong She, Jorma Toppari, Anette-G. Ziegler, Beena Akolkar, Oliver Fiehn, Sili Fan and Jeffrey Krischer | Longitudinal metabolome-wide signals prior to the appearance of pancreatic islet autoantibodies in children at genetic risk for type 1 diabetes: the TEDDY study |
| 8 | Eleonora Achrak, Jennifer Fred, Jessica Schulman | Unlocking the glow: characterize of bioluminescent genes in fireworm Odontosyllis enopla |
| 9 | Siddharth Krishnakumar | VCFDataPy: A Software tool to analyze human genome variation data to discover chromosomal abnormalities in Autism and other genetic brain disorders. |
| 10 | Kiley Graim and Olga Troyanskaya | Time-series gene interaction networks of fetal brain development predict genetic drivers of neurodevelopmental disorders |
| 11 | Weizhong Li | Combined alignments of sequences and domains characterize unknown proteins with remotely related protein search PSISearch2D |
| 12 | Olaitan Awe, Angela Makolo, Segun Fatumo | Computational Genomic Analysis of Bacteriophages in Typhoidal Salmonella Sequences |
| 13 | Douglas Phanstiel | Visualizing data within the context of human kinase, phosphatase, and transcription factor families |
| 14 | Amartya Singh, Hossein Khiabanian and Gyan Bhanot | Tunable biclustering algorithm for integrative analysis of tumor transcriptomic and epigenomic data |
| 15 | Bobbie-Jo Webb-Robertson, Lisa Bramer, Bryan Stanfill, Sarah Reehl, Ernesto Nakayasu, Thomas Metz, Brigitte Frohnert, Jill Norris, Randi Johnson, Stephen Rich and Marian Rewers | Discovery of Disparate Biological Features Predicting Islet Autoantibodies via Integrated Machine Learning Feature Selection |
| 16 | Avyay Varadarajan, Avanti Shrikumar and Anshul Kundaje | Using Deep Learning to Understand the Sequence Determinants of CTCF Binding from CUT&RUN data |
| 17 | Tarun Chiruvolu, Avanti Shrikumar, Daniel Kim and Anshul Kundaje | A Computational Dissection of Genome-Wide Transcription Factor Binding Sites Using Deep Learning Models of Chromatin Accessibility in Skin Differentiation |
| 18 | Nicole Kramer and Douglas Phanstiel | BentoBox.R: customizable plotting and arranging of genomic data sets using R grid Graphics |
| 19 | Kenny Ye Liang, Feng Bao, Yue Deng and Qionghai Dai | Fast and scalable identification of rare cell subpopulations from large-scale single-cell transcriptomics |
| 20 | Vu Viet Hoang Pham, Lin Liu, Cameron Bracken, Gregory Goodall, Qi Long, Jiuyong Li and Thuc Duy Le | Methods for identifying single and group-based coding/non-coding cancer drivers |
| 21 | Joshua Wetzel, Mona Singh | Inferring DNA-binding specificities jointly across structurally similar proteins |
| 22 | Hannah Zhou, Avanti Shrikumar and Anshul Kundaje | A head-to-head benchmarking of reverse-complement-aware architectures for genomics |
| 23 | Chen Su, William Pastor and Amin Emad | An integrative approach for identification of lineage-relevant transcriptional regulatory networks in human embryogenesis |
| 24 | Jennifer Ferd, Trami Dang, Eleonora Achrak, Jessica Schulman, Konstantinos Krampis, Mande Holford | Developing a bioinformatics pipeline for characterizing venom peptides from terebrid snails |
| 25 | Eileen Li, Avanti Shrikumar, Georgi Marinov, Connor Horton, Polly Fordyce and Anshul Kundaje | Training and interpreting a deep learning model to understand the fine-grained sequence determinants of Pho4 binding from high-resolution PB-exo data |
| 26 | Joonas Tuominen, Ebrahim Afyounian, Francesco Tabaro, Tomi Häkkinen, Anastasia Shcherban, Matti Annala, Riikka Nurminen, Kati Kivinummi, Teuvo Tammela, Alfonso Urbanucci, Leena Latonen, Juha Kesseli, Kirsi Granberg, Tapio Visakorpi and Matti Nykter | Chromatin accessibility in human prostate cancer progression |
| 27 | Sungjoon Park, Minji Jeon, Sunkyu Kim, Junhyun Lee, Seongjun Yun, Bumsoo Kim, Buru Chang and Jaewoo Kang | In Silico Molecular Binding Affinity Prediction with Multi-Task Graph Neural Networks |
| 28 | Chris Jackson, David Gresham and Richard Bonneau | Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments |
| 29 | Christopher Magnano and Anthony Gitter | Automating parameter selection to avoid implausible biological pathway models |
| 30 | Emily Ackerman, Ericka Mochan and Jason Shoemaker | Mathematical Model of the Strain-Specific Immune Response to Influenza Virus |
| 31 | Anjun Ma, Cankun Wang, Yuzhou Chang and Qin Ma | Identification of cell-type-specific alternative regulons from single-cell RNA-Seq |
| 32 | Mervin Fansler, Gang Zhen and Christine Mayr | 3’ UTR isoform usage is cell type-specific and switches during differentiation predominantly in genes without expression changes |
| 33 | Qian Zhu, Ruben Dries, Chee-Huat Linus Eng, Arpan Sarkar, Feng Bao, Rani George, Nico Pierson, Long Cai and Guo-Cheng Yuan | Giotto, a pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data |
| 34 | Qian Zhu, Nan Liu, Stuart Orkin and Guo-Cheng Yuan | CUT&RUNTools: a flexible pipeline for CUT&RUN processing and footprint analysis |
| 35 | Gabrielle Perron and Hamed Shateri Najafabadi | A unified framework for comparing ratios in sequencing count data |
| 36 | Rachel Hovde, Gus Zeiner, Jay Danao, Charlotte Davis, Melissa Fardy, Nicole Grant, Dianna Lester-Zeiner, David Mai, Krista McNally, Michon Pinnix, Erin Riegler, Daniel Roche and Ben Wang | A new approach to arm T cell therapies with conditional, transgenic payload outputs |
| 37 | Merve Sahin, Mark Carty, Lee Zamparo and Christina Leslie | HiC-DC+: A Robust Statistical Tool to Detect Significant Interactions from Hi-C, HiChIP and CHiC data |
| 38 | Erik Ladewig, Eneda Toska and Maurizio Scaltriti | PI3K pathway mediated splicing defects in ER+ breast cancer. |
| 39 | Vincentius Martin, Farica Zhuang and Raluca Gordân | Cooperative binding of transcription factors to clusters of DNA binding sites |
| 40 | Joseph Wayman, Diep Nguyen, Peter DeWeirdt, Tareian Cazares, Bryan Bryson and Emily Miraldi | Gene regulatory network inference from single-cell RNA-seq uncovers transcriptional programs controlling human macrophages |
| 41 | Cynthia Ma and Michael R. Brent | Transcription Factor Activity Inference: Does it really work? |
| 42 | Osama Arshad, Vincent Danna, Vladislav Petyuk, Paul Piehowski, Tao Liu, Karin Rodland and Jason McDermott | An Integrative Analysis of Tumor Proteomic and Phosphoproteomic Profiles to Examine the Relationships Between Kinase Activity and Phosphorylation |
| 43 |
Ziynet Nesibe Kesimoglu and Serdar Bozdag |
Inferring competing endogenous RNA (ceRNA) interactions in cancer |
| 44 | Matthew Stone, Sunnie Grace McCalla, Viswesh Periyasamy, Alireza Fotuhi Siahpirani and Sushmita Roy | Benchmarking regulatory network inference algorithms for single-cell RNA-sequencing datasets |
| 45 | Joost Groot, Catherine Nezich, Eric Marshall, Anne Campbell, Patrick Cullen, Chao Sun and Warren Hirst | An RNA-Seq approach to translate gene and pathway impacts of cellular clearance activator TFEB for drug discovery in Parkinson’s Disease |
| 46 | Roger Pique-Regi, Roberto Romero, Adi Tarca, Edward Sendler, Yi Xu, Valeria Garcia-Flores, Yaozhu Leng, Francesca Luca, Sonia Hassan and Nardhy Gomez-Lopez | Single Cell Analysis of the Human Placenta Transcriptome in Parturition |
| 47 | Antonina Mitrofanova, Nusrat Epsi, Sukanya Panja and Sharon Pine | pathCHEMO: a generalizable computational framework to uncover molecular pathways of chemoresistance in lung adenocarcinoma |
| 48 | Justyna Resztak, Julong Wei, Peijun Wu, Shiquan Sun, Edward Sendler, Adnan Alazizi, Henriette Mair-Meijers, Allison Farrell, Richard Slatcher, Samuele Zilioli, Xiang Zhou, Francesca Luca and Roger Pique-Regi | Genetics of gene expression response in asthma at single cell resolution |
| 49 | Wei Zhang and Jiao Sun | Network-based Learning Methods to Explore the Role of Post-Transcriptional Regulation in Cancer |
| 50 | Irem Celen and Robert Kueffner | Gene and domain specific interpretation of missense variant pathogenicity prediction |
| 51 | Nidia E. BeltrÁn HernÁndez and Heriberto Manuel Rivera | The role of Voltage-Gated Ion Channels Subunits in Osteosarcoma Metastasis |
| 52 | Faiz Rizvi, Tariean Cazares, Iyer Balaji, Matt Weirauch, Leah Kottyan, Surya Prasath and Emily R. Miraldi | Using Deep Learning to Predict Cell Type-specific Chromatin Accessibility Based on Genotype Alone |
| 53 | Mariano I. Gabitto, Anders Rasmussen, Orly Wapinski, Kathryn Allaway, Nicholas Carriero, Gordon J. Fishell and Richard Bonneau | Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling. |
| 54 | Lili Blumenberg, Vladislav Sviderskiy, Richard Possemato and Kelly Ruggles | Data-driven discovery of phosphorylation modules in cancer |
| 55 | Wojciech Rosikiewicz, Xiaowen Chen, Pilar M. Dominguez, Ari Melnick and Sheng Li | TET2 Deficiency Reprograms the Germinal Center B-cell Epigenome and Silences Genes Linked to Lymphomagenesis |
| 56 | Shu Wang, Manimozhi Manivannan, Saurabh Gulati, Sombeet Sahu, Dong Kim, Nianzhen Li and Nigel Beard | Using Machine Learning to Optimize Assays for Single-Cell Targeted DNA Sequencing |
| 57 | Yue Qiu, Tianhuan Lu, Hansaim Lim and Lei Xie | A Bayesian approach to accurate and robust signature detection on LINCS L1000 data |
| 58 | Saurabh V Laddha, Edaise M M da Silva, Kenneth Robzyk, Brian R Untch, Hua Ke, Natasha Rekhtman, John T Poirier, William D Travis, Laura H Tang and Chang S Chan | Integrative Genomic Characterization Identifies Molecular Subtypes of Lung Carcinoids |
| 59 | Luis Santos, Sydney Hart, Prashant Rabhhandari | Single Nuclei Adipocyte RNA sequencing (SNAP-Seq) Reveals Immune Cell-Adipocyte Crosstalk |
