Friday Dec 13th Poster Session (4 - 5.20pm & 6.10-8pm)

Ammar Tareen and Justin Kinney. Biophysical models of cis-regulation as interpretable neural networks.

Aakash Sur. A Data Driven Approach to Scaffolding Genomes with Hi-C

Anastasiya Belyaeva, Kaie Kubjas, Lawrence Sun and Caroline Uhler. Inferring 3D Genome Organization in Diploid Organisms

Bowen Dai and Chris Bailey-Kellogg. Protein-Protein Binding Site Prediction by Geometric Deep Learning

Camille Rochefort-Boulanger, Léo Choinière, Jean-Christophe Grenier, Pierre-Luc Carrier and Julie Hussin. Generalization Capability of the Diet Network Model on Genomic Data

Daniel Mas Montserrat, Carlos Bustamante and Alexander Ioannidis. Class-Conditional VAE-GAN for Local-Ancestry Simulation

Etienne Meunier, German Novakovsky and Sara Mostafavi. Interpreting deep learning models in genomics using genetic algorithms

Galip Gurkan Yardimci, Jacob Schreiber, Jeffrey Bilmes and William Stafford Noble. Selecting representative sets of genomic loci

Ian Covert and Su-In Lee. Shapley Feature Utility.

Iman Deznabi, Büşra Arabacı, Mehmet Koyuturk and Oznur Tastan. DeepKinZero: Zero-Shot Learning for Predicting Kinase-Phosphosite Associations

Jack Lanchantin and Yanjun Qi Graph. Convolutional Networks for Epigenetic State Prediction Using Both Sequence and 3D Genome Data

Jay S Stanley III, Scott Gigante, Guy Wolf and Smita Krishnaswamy. Manifold Alignment with Feature Correspondence

Raquel Aoki and Martin Ester. Bayesian Predictive Model combined with Matrix Factorization for Causal Inference Analysis

John Halloran and David Rocke. GPU-Accelerated SVM Learning for Extremely Fast Large-Scale Proteomics Analysis

Julie Jiang, Li-Ping Liu and Soha Hassoun. Predicting Reactions for Biochemical Networks Using Graph Embeddings

Michael Kleyman, Cathy Su, Bilge Esin Ozturk, Jing He, William Stauffer, Leah Byrne and Andreas Pfenning. Nested Tree Cell State Model

Rohit Jammula, Vishnu Tejus and Shreya Shankar. Optimal Transfer Learning Model for Binary Classification of Funduscopies through Simple Heuristics

Shreshth Gandhi, Leo Lee, Andrew Delong, David Duvenaud and Brendan Frey. cDeepbind: A context sensitive deep learning model of RNA-protein binding

Yue Wu and Sriram Sankararaman. Fast estimation of genetic correlation for Biobank-scale data

Saturday Dec 14th Poster Session (11:30 - 1:30 pm)

Nick Bhattacharya, Neil Thomas and Roshan Rao. Can Self-Supervised Models Capture Sequence Conservation?

Abhishek Sarkar, Mengyin Lu and Matthew Stephens. Common pitfalls in the analysis of scRNA-seq data

Adam Gayoso, Romain Lopez, Zoë Steier, Jeffrey Regier, Aaron Streets and Nir Yosef. A Joint Model of RNA Expression and Surface Protein Abundance in Single Cells

Alan Moses, Alex Lu, Amy Lu and Marzyeh Ghassemi. Transfer Learning vs. Batch Effects: what can we expect from neural networks in computational biology?

Alexey Strokach, David Becerra, Carles Corbi, Albert Perez-Riba and Philip M. Kim. Designing novel proteins with high accuracy using deep graph neural networks

Alice Yue, Cedric Chauve, Maxwell Libbrecht and Ryan Brinkman. Identifying differential cell populations in flow cytometry data accounting for marker frequency

Anastasiia Razdaibiedina, Jeevaa Velayutham and Miti Modi. Learning from mistakes: reconstruction of biomedical images with GAN-aided dataset augmentation

Anat Etzion-Fuchs, David A. Todd and Mona Singh. dSPRINT: an ensemble approach for predicting interaction sites within protein domains

Andreea Gane, David Belanger, David Dohan, Christof Angermueller, Ramya Deshpande, Suhani Vora, Olivier Chapelle, Babak Alipanahi and Lucy Colwell. A Comparison of Generative Models for Sequence Design

Ayse B. Dincer, Joseph D. Janizek, Safiye Celik, Naozumi Hiranuma, Kamila Naxerova and Su-In Lee. DeepProfile: Interpretable Deep Learning of Latent Variables from a Compendium of Expression Profiles for 18 Human Cancers

Bernard Ng, William Casazza, Farnush Farhadi and Sara Mostafavi. Cascading Epigenomic Model for GWAS

Bowen Chen, Neda Shokraneh Kenari, Habib Daneshpajouh, Kay C. Wiese and Max W Libbrecht. Continuous chromatin state feature annotation of the human epigenome

Cait Harrigan, Yulia Rubanova, Quaid Morris and Alina Selega. TrackSigFreq: subclonal reconstructions based on mutation signatures and allele frequencies

Carles Corbi-Verge, April Muller, Marcus Noyes and Philip Kim. C2H2 Zinc Finger DNA Binding Motif Prediction combining High-throughput experiments and Deep Learning

Caroline Weis, Max Horn, Bastian Rieck and Karsten Borgwardt. Sparse representations for MALDI-TOF based microbial classification

Conor Delaney, Alexandra Schnell, Louis Cammarata, Aaron Yao-Smith, Aviv Regev, Vijay Kuchroo and Meromit Singer. COMET: A tool for predicting multiple gene-marker panels from single-cell transcriptomic data

Gian Marco Visani, Michael Hughes and Soha Hassoun. Classification of Enzyme Promiscuity Using Positive, Unlabeled, and Hard Negative Examples

Haitham Elmarakeby, David Liu, Saud Aldubayan, Justin Hwang and Eliezer Van Allen. Informed sparsity for cancer discovery

Haohan Wang and Nila Ramaswamy. Structured High-dimension Variable Selection with P-value, with Applications of Transcriptome Association Study

Haoyang Zeng, Brandon Carter, Siddhartha Jain, Brooke Huisman, Michael Birnbaum and David Gifford. Machine learning optimization of MHC class II presented peptides

Hassan Kané, Mohamed Coulibali, Ali Abdalla and Pelkins Ajanoh. Combining graph and sequence information to learn protein representations

Hossein Sharifi-Noghabi, Shuman Peng, Olga Zolotareva, Colin Collins and Martin Ester. Adversarial Inductive Transfer Learning for pharmacogenomics datasets

Hui Ting Grace Yeo and David Gifford. Disentangling unwanted sources of variation in single-cell RNA-sequencing data under weak supervision

Hyunmin Lee, Zhen Hao Wu, Carles Corbi-Verges, Mac Mok, Sidney Kang, Shun Liao, Zhaolei Zhang and Michael Garton. De Novo Crystallization Condition Prediction with Deep Learning

Ian Covert, Uygar Sumbul and Su-In Lee. Principal Genes Selection

Iddo Drori, Darshan Thaker, Arjun Srivatsa, Daniel Jeong, Yueqi Wang, Linyong Nan, Fan Wu, Dimitri Leggas, Jinhao Lei, Weiyi Lu, Weilong Fu, Yuan Gao, Sashank Karri, Anand Kannan, Antonio Moretti, Chen Keasar and Itsik Pe'er. Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

Jacob Schreiber, Deepthi Hedge and William Noble. Zero-shot imputations across species are enabled through joint modeling of human and mouse epigenomics

Jeff Wintersinger, Stephanie Dobson, John Dick and Quaid Morris. Pairtree: fast reconstruction of cancer evolutionary history using pairwise mutation relationships

Joseph D. Janizek and Su-In Lee. Imposing smooth graph structure on the attributions of deep neural networks for improved RNA-seq analysis

Kevin Dsouza, Adam Li, Vijay Bhargava and Maxwell Libbrecht. A Cell Type-Agnostic Representation of the Human Epigenome through a Deep Recurrent Neural Network Model

Khawla Seddiki, Philippe Saudemont, Frederic Precioso, Nina Ogrinc, Maxence Wisztorski, Michel Salzet, Isabelle Fournier and Arnaud Droit. Feature learning with Deep Neural Networks for MS-based clinical diagnosis

Lan Huong Nguyen and Susan Holmes. Diffusion t-SNE for multiscale data visualization

Linhua Wang, Jeffrey Law, T. M. Murali and Gaurav Pandey. Data integration through heterogeneous ensembles for protein function prediction

Matthew Ploenzke and Peter Koo. Improving Convolutional Network Interpretability with Divergent Activations

Maxwell Libbrecht and Faezeh Bayat. Variance-stabilized units for sequencing-based genomic signals

Maxwell W Libbrecht, Rachel C W Chan and Michael M Hoffman. Segmentation and genome annotation algorithms

Meseret Bayeleygne. Speculative Scientific Inference via Synergetic Combination of Probabilistic Logic and Evolutionary Pattern Recognition

Michael Dimmick, Leo J Lee and Brendan J Frey. HiCSR: A Hi-C Super-Resolution Framework for Producing Highly Realistic Contact Maps

Nadav Brandes, Nathan Linial and Michal Linial. Powering Genetic Association Studies with Machine Learning

Nelson Johansen and Gerald Quon. A deep deconvolution approach for combining the high-resolution of single cell atlases with the scale of bulk genomics

Nicasia Beebe-Wang, Safiye Celik, Pascal Sturmfels, Sara Mostafavi and Su-In Lee. MD-AD: Multi-task deep learning for Alzheimer’s disease neuropathology

Nil Sahin, Mojca Mattiazzi-Ušaj, Matej Ušaj, Myra Paz Masinas, Charlie Boone, Brenda Andrews and Quaid Morris. Automated outlier detection and mutant phenotype discovery on single cell images

Oliver Snow, Hossein Sharifi-Noghabi, Jialin Lu, Olga Zolotareva, Mark Lee and Martin Ester. BDKANN - Biological Domain Knowledge-based Artificial Neural Network for drug response prediction

Oscar Clivio, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan and Nir Yosef. Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data

Paul Bertin, Mohammad Hashir, Martin Weiss, Vincent Frappier, Theodore Perkins, Geneviève Boucher and Joseph Paul Cohen. Is graph biased feature selection of genes better than random?

Peter Koo and Matthew Ploenzke. Interpreting Deep Neural Networks Beyond Attribution Methods: Quantifying Global Importance of Features

Pierre Boyeau, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan and Nir Yosef. Deep Generative Models for Detecting Differential Expression in Single Cells

Samuel Goldman, David Yang, Eli Weinstein and Debora Marks. Generative models for codon prediction and optimization

Sarvesh Nikumbh and Boris Lenhard. archR: Identification of Different Promoter Architectures Using Non-negative Matrix Factorization

Teresa Maria Rosaria Noviello, Michele Ceccarelli and Luigi Cerulo. DeepRFAM: a deep learning architecture for non-coding RNA functional prediction

Tyler Funnell, Allen Zhang, Diljot Grewal, Steven Mckinney, Ali Bashashati, Yi Kan Wang and Sohrab Shah. Integrated structural variation and point mutation signatures in cancer genomes using correlated topic models

Wei Qiu, Jiaming Guo, Xiang Li, Mengjia Xu, Mo Zhang, Ning Guo and Quanzheng Li. Multi-label Detection and Classification of Red Blood Cells in Microscopic Images

William Chen, Joseph D. Janizek and Su-In. Lee. EXPERT: Explainable Prediction of Transcription Factor Binding based on Histone Modification Data

Wout Bittremieux, Damon H. May, Jeffrey Bilmes and William S Noble. A learned embedding for efficient joint analysis of millions of mass spectra

Yiliang Zhang, Kexuan Liang, Molei Liu, Yue Li, Hao Ge and Hongyu Zhao. SCRIBE: a new approach to dropout imputation and batch effects correction for single-cell RNA-seq data