`

Timezone: »

 
Poster
Unsupervised Learning from Noisy Networks with Applications to Hi-C Data
Bo Wang · Junjie Zhu · Armin Pourshafeie · Oana Ursu · Serafim Batzoglou · Anshul Kundaje

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #4 #None

Complex networks play an important role in a plethora of disciplines in natural sciences. Cleaning up noisy observed networks, poses an important challenge in network analysis Existing methods utilize labeled data to alleviate the noise effect in the network. However, labeled data is usually expensive to collect while unlabeled data can be gathered cheaply. In this paper, we propose an optimization framework to mine useful structures from noisy networks in an unsupervised manner. The key feature of our optimization framework is its ability to utilize local structures as well as global patterns in the network. We extend our method to incorporate multi-resolution networks in order to add further resistance to high-levels of noise. We also generalize our framework to utilize partial labels to enhance the performance. We specifically focus our method on multi-resolution Hi-C data by recovering clusters of genomic regions that co-localize in 3D space. Additionally, we use Capture-C-generated partial labels to further denoise the Hi-C network. We empirically demonstrate the effectiveness of our framework in denoising the network and improving community detection results.

Author Information

Bo Wang (Stanford University)
Junjie Zhu (Stanford University)
Armin Pourshafeie (Stanford University)
Oana Ursu (Dept. of Genetics)
Serafim Batzoglou (Dept. of Computer Science)
Anshul Kundaje (Stanford University)

More from the Same Authors

  • 2021 Workshop: Learning Meaningful Representations of Life (LMRL) »
    Elizabeth Wood · Adji Bousso Dieng · Aleksandrina Goeva · Anshul Kundaje · Barbara Engelhardt · Chang Liu · David Van Valen · Debora Marks · Edward Boyden · Eli N Weinstein · Lorin Crawford · Mor Nitzan · Romain Lopez · Tamara Broderick · Ray Jones · Wouter Boomsma · Yixin Wang
  • 2020 Workshop: Learning Meaningful Representations of Life (LMRL.org) »
    Elizabeth Wood · Debora Marks · Ray Jones · Adji Bousso Dieng · Alan Aspuru-Guzik · Anshul Kundaje · Barbara Engelhardt · Chang Liu · Edward Boyden · Kresten Lindorff-Larsen · Mor Nitzan · Smita Krishnaswamy · Wouter Boomsma · Yixin Wang · David Van Valen · Orr Ashenberg
  • 2020 Poster: Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics »
    Alex Tseng · Avanti Shrikumar · Anshul Kundaje
  • 2018 : Lunch & Posters »
    Haytham Fayek · German Parisi · Brian Xu · Pramod Kaushik Mudrakarta · Sophie Cerf · Sarah Wassermann · Davit Soselia · Rahaf Aljundi · Mohamed Elhoseiny · Frantzeska Lavda · Kevin J Liang · Arslan Chaudhry · Sanmit Narvekar · Vincenzo Lomonaco · Wesley Chung · Michael Chang · Ying Zhao · Zsolt Kira · Pouya Bashivan · Banafsheh Rafiee · Oleksiy Ostapenko · Andrew Jones · Christos Kaplanis · Sinan Kalkan · Dan Teng · Xu He · Vincent Liu · Somjit Nath · Sungsoo Ahn · Ting Chen · Shenyang Huang · Yash Chandak · Nathan Sprague · Martin Schrimpf · Tony Kendall · Jonathan Schwarz · Michael Li · Yunshu Du · Yen-Chang Hsu · Samira Abnar · Bo Wang
  • 2014 Workshop: Machine Learning in Computational Biology »
    Oliver Stegle · Sara Mostafavi · Anna Goldenberg · Su-In Lee · Michael Leung · Anshul Kundaje · Mark B Gerstein · Martin Renqiang Min · Hannes Bretschneider · Francesco Paolo Casale · Loïc Schwaller · Amit G Deshwar · Benjamin A Logsdon · Yuanyang Zhang · Ali Punjani · Derek C Aguiar · Samuel Kaski
  • 2006 Poster: Training Conditional Random Fields for Maximum Parse Accuracy »
    Samuel Gross · Olga Russakovsky · Chuong B Do · Serafim Batzoglou