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RECOMB 2019

일시 : 2019. 05. 05~08

장소 :  Washington, DC, USA.

주관 : RECOMB

 About

RECOMB 2019 is the 23rd edition of a series of  algorithmic computational biology conferences bridging the areas of computational, mathematical, statistical and biological sciences. The conference features keynote talks by preeminent scientists in life sciences, presentations of ground breaking research in computational biology, selected over the course of a highly-rigorous peer-review process, and poster sessions on the latest research progress.

RECOMB 2019 will be held in The George Washington University on May, 5th through 8th of 2019, and be preceded by a series of satellite workshops on the 3rd-4th May.

Program

Accepted Papers

1.Fatemeh Almodaresi, Prashant Pandey, Michael Ferdman, Rob Johnson and Rob Patro.

An Efficient and Scalable Representation of High-Dimensional Color Information Enabled via de Bruijn Graph Search

2.Aryan Arbabi, David Adams, Sanja Fidler and Michael Brudno.

Identifying clinical terms in free-text notes using ontology-guided machine learning

3.Metin Balaban, Shahab Sarmashghi and Siavash Mirarab.

APPLES: Fast Distance Based Phylogenetic Placement

4.Bahar Behsaz, Hosein Mohimani, Alexey Gurevich, Andrey Prjibelski, Mark F Fisher, Larry Smarr, Pieter C. Dorrestein, Joshua S. Mylne and Pavel A. Pevzner.

De Novo Peptide Sequencing Reveals a Vast Cyclopeptidome in Human Gut and Other Environments

5.Philipp Benner and Martin Vingron.

ModHMM: A modular supra-Bayesian genome segmentation method

6.Lodewijk Brand, Liu Kai, Saad Elbeleidy, Hua Wang and Hao Zhang.

Learning Robust Multi-Label Sample Specific Distances for Identifying HIV-1 Drug Resistance

7.Dexiong Chen, Laurent Jacob and Julien Mairal.

Biological Sequence Modeling with Convolutional Kernel Networks

8.Van Hoan Do, Mislav Bla?evi?, Pablo Monteagudo, Luka Borozan, Khaled Elbassioni, Soeren Laue, Francisca Rojas Ringeling, Domagoj Matijevic and Stefan Canzar.

Dynamic pseudo-time warping of complex single-cell trajectories

9.Rebecca Elyanow, Bianca Dumitrascu, Barbara E. Engelhardt, and Benjamin J. Raphael.

netNMF: A network regularization algorithm for dimensionality reduction and imputation of single-cell expression data

10.Boying Gong and Elizabeth Purdom.

MethCP: Differentially Methylated Region Detection with Change Point Models

11.Brian Hie, Hyunghoon Cho, Benjamin DeMeo, Bryan Bryson and Bonnie Berger.

Geometric sketching of single-cell data preserves transcriptional structure

12.Chirag Jain, Haowen Zhang, Yu Gao and Srinivas Aluru.

On the Complexity of Sequence to Graph Alignment

13.Jonathan Jou, Graham Holt, Anna Lowegard and Bruce Donald.

Minimization-Aware Recursive K* (MARK*): A Novel, Provable Algorithm that Accelerates Ensemble-based Protein Design and Provably Approximates the Energy Landscape

14.Mikhail Karasikov, Harun Mustafa, Amir Joudaki, Sara Javadzadeh No, Gunnar Ratsch and Andre Kahles.

Sparse Binary Relation Representations for Genome Graph Annotation

15.Younhun Kim, Frederic Koehler, Ankur Moitra, Elchanan Mossel and Govind Ramnarayan.

How Many Subpopulations is Too Many? Exponential Lower Bounds for Inferring Population Histories

16.Can Kockan, Kaiyuan Zhu, Natnatee Dokmai, Nikolai Karpov, Oguzhan Kulekci, David Woodruff and Cenk Sahinalp.

Sketching Algorithms for Genomic Data Analysis and Querying in a Secure Enclave

17.Alan Kuhnle, Taher Mun, Christina Boucher, Travis Gagie, Ben Langmead and Giovanni Manzini.

Efficient Construction of a Complete Index for Pan-Genomics Read Alignment

18.Haoyun Lei, Bochuan Lyu, E. Michael Gertz, Alejandro A. Schaffer, Xulian Shi, Kui Wu, Guibo Li, Liquin Xu, Yong Hu, Michael Dean and Russell Schwartz.

Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data

19.Yunan Luo, Jianzhu Ma, Xiaoming Zhao, Yufeng Su, Yang Liu, Trey Ideker and Jian Peng.

Mitigating Data Scarcity in Protein Binding Prediction Using Meta-Learning

20.Joel Mefford, Danny Park, Arthur Ko, Zhili Zheng, Markku Laakso, Paivi Pajukanta, Jian Yang, John Witte and Noah Zaitlen.

Efficient estimation and applications of cross-validated genetic predictions

21.Matthew Myers, Gryte Satas and Benjamin Raphael.

Inferring tumor evolution from longitudinal samples

22.Weihua Pan, Tao Jiang and Stefano Lonardi.

OMGS: Optical Map-based Genome Scaffolding

23.Ali Pazokitoroudi, Yue Wu, Kathryn S. Burch, Kangcheng Hou, Bogdan Pasaniuc and Sriram Sankararaman.

Scalable multi-component linear mixed models with application to SNP heritability estimation

24.Leonardo Pellegrina, Cinzia Pizzi and Fabio Vandin.

Fast Approximation of Frequent k-mers and Applications to Metagenomics

25.Kristoffer Sahlin and Paul Medvedev.

De novo clustering of long-read transcriptome data using a greedy, quality-value based algorithm

26.Shahab Sarmashghi and Vineet Bafna.

A Note on Computing Interval Overlap Statistics

27.Itay Sason, Damian Wojtowicz, Welles Robinson, Mark Leiserson, Teresa Przytycka and Roded Sharan.

A sticky multinomial mixture model of strand-coordinated mutational processes in cancer

28.Mike Thompson, Zeyuan Johnson Chen, Elior Rahmani and Eran Halperin.

Recovery of cell-type composition in methylation data using canonical correlation analysis

29.Sheng Wang, Emily Flynn and Russ Altman.

GRep: Gene Set Representation via Gaussian Embedding

30.Yijie Wang, Jan Hoinka and Teresa M. Przytycka.

Accurate sub-population detection and mapping across single cell experiments with PopCorn

31.Ziheng Wang, Grace Ht Yeo, Richard Sherwood and David Gifford.

Disentangled Representations of Cellular Identity

32.Ye Wu, Ruibang Luo, Henry C.M. Leung, Hing-Fung Ting and Tak-Wah Lam.

RENET: A Deep Learning Approach for Extracting Gene-Disease Associations from Literature

33.Yue Wu, Anna Yaschenko, Mohammadreza Hajy Heydary, and Sriram Sankararaman.

Fast estimation of genetic correlation for Biobank-scale data

34.Jinbo Xu.
Distance-based Protein Folding Powered by Deep Learning

35.Yang Yang, Yang Zhang, Bing Ren, Jesse Dixon and Jian Ma.

Comparing 3D Genome Organization in Multiple Species using Phylo-HMRF

36.Jesse Zhang, Govinda Kamath and David Tse.

Towards a post-clustering test for differential expression

37.Martin Zhang, Fei Xia and James Zou.

AdaFDR: a Fast, Powerful and Covariate-Adaptive Approach for Multiple Hypothesis Testing

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