publications

2024

1. Medical image registration via neural fields

   Shanlin Sun, Kun Han, Chenyu You, and 7 more authors

   Medical Image Analysis, 2024

   Publisher: Elsevier
2. A Deep-Learning Approach to Detect and Classify Heavy-Duty Trucks in Satellite Images

   Xingwei Liu, Yiqiao Li, Langting Sizemore, and 2 more authors

   IEEE Transactions on Intelligent Transportation Systems, 2024

   Publisher: IEEE
3. Identifying dysregulated regions in amyotrophic lateral sclerosis through chromatin accessibility outliers

   Muhammed Hasan Celik, Julien Gagneur, Ryan G. Lim, and 3 more authors

   Human Genetics and Genomics Advances, 2024

   Publisher: Elsevier
4. BiLO: Bilevel Local Operator Learning for PDE inverse problems

   Ray Zirui Zhang, Xiaohui Xie, and John Lowengrub

   arXiv e-prints, 2024
5. HRBP: Hardware-friendly Regrouping towards Block-based Pruning for Sparse CNN Training

   Haoyu Ma, Chengming Zhang, Xiaolong Ma, and 7 more authors

   In Conference on Parsimony and Learning, 2024
6. LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenes

   Shanlin Sun, Bingbing Zhuang, Ziyu Jiang, and 3 more authors

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
7. Integrating efficient optimal transport and functional maps for unsupervised shape correspondence learning

   Tung Le, Khai Nguyen, Shanlin Sun, and 2 more authors

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
8. Maskint: Video editing via interpolative non-autoregressive masked transformers

   Haoyu Ma, Shahin Mahdizadehaghdam, Bichen Wu, and 5 more authors

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
9. DiL-NeRF: Delving into Lidar for Neural Radiance Field on Street Scenes

   Shanlin Sun, Bingbing Zhuang, Ziyu Jiang, and 3 more authors

   arXiv preprint arXiv:2405.00900, 2024
10. LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenes

    Shanlin Sun, Bingbing Zhuang, Ziyu Jiang, and 3 more authors

    May 2024

    arXiv:2405.00900 [cs]

    Abs

    Photorealistic simulation plays a crucial role in applications such as autonomous driving, where advances in neural radiance fields (NeRFs) may allow better scalability through the automatic creation of digital 3D assets. However, reconstruction quality suffers on street scenes due to largely collinear camera motions and sparser samplings at higher speeds. On the other hand, the application often demands rendering from camera views that deviate from the inputs to accurately simulate behaviors like lane changes. In this paper, we propose several insights that allow a better utilization of Lidar data to improve NeRF quality on street scenes. First, our framework learns a geometric scene representation from Lidar, which is fused with the implicit grid-based representation for radiance decoding, thereby supplying stronger geometric information offered by explicit point cloud. Second, we put forth a robust occlusion-aware depth supervision scheme, which allows utilizing densified Lidar points by accumulation. Third, we generate augmented training views from Lidar points for further improvement. Our insights translate to largely improved novel view synthesis under real driving scenes.

11. Identifying dysregulated regions in amyotrophic lateral sclerosis through chromatin accessibility outliers

    Muhammed Hasan Celik, Julien Gagneur, Ryan G. Lim, and 3 more authors

    Human Genetics and Genomics Advances, May 2024

    Publisher: Elsevier
12. BiLO: Bilevel Local Operator Learning for PDE inverse problems

    Ray Zirui Zhang, Xiaohui Xie, and John Lowengrub

    arXiv e-prints, May 2024
13. HRBP: Hardware-friendly Regrouping towards Block-based Pruning for Sparse CNN Training

    Haoyu Ma, Chengming Zhang, Xiaolong Ma, and 7 more authors

    In Conference on Parsimony and Learning, May 2024
14. LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenes

    Shanlin Sun, Bingbing Zhuang, Ziyu Jiang, and 3 more authors

    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, May 2024
15. Integrating efficient optimal transport and functional maps for unsupervised shape correspondence learning

    Tung Le, Khai Nguyen, Shanlin Sun, and 2 more authors

    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, May 2024
16. Maskint: Video editing via interpolative non-autoregressive masked transformers

    Haoyu Ma, Shahin Mahdizadehaghdam, Bichen Wu, and 5 more authors

    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, May 2024
17. AFTer-SAM: Adapting SAM with Axial Fusion Transformer for Medical Imaging Segmentation

    Xiangyi Yan, Shanlin Sun, Kun Han, and 4 more authors

    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, May 2024
18. CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer

    Haoyu Ma, Tong Zhang, Shanlin Sun, and 3 more authors

    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, May 2024
19. Self-sampling meta SAM: enhancing few-shot medical image segmentation with meta-learning

    Tianang Leng, Yiming Zhang, Kun Han, and 1 more author

    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, May 2024
20. Adaptive Image Registration: A Hybrid Approach Integrating Deep Learning and Optimization Functions for Enhanced Precision

    Gabriel De Araujo, Shanlin Sun, and Xiaohui Xie

    Jan 2024

    arXiv:2311.15497 [cs]

    Abs

    Image registration has traditionally been done using two distinct approaches: learning based methods, relying on robust deep neural networks, and optimization-based methods, applying complex mathematical transformations to warp images accordingly. Of course, both paradigms offer advantages and disadvantages, and, in this work, we seek to combine their respective strengths into a single streamlined framework, using the outputs of the learning based method as initial parameters for optimization while prioritizing computational power for the image pairs that offer the greatest loss. Our investigations showed improvements of up to 1.6% in test data, while maintaining the same inference time, and a substantial 1.0% points performance gain in deformation field smoothness.

21. Hybrid neural diffeomorphic flow for shape representation and generation via triplane

    Kun Han, Shanlin Sun, Thanh-Tung Le, and 4 more authors

    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Jan 2024
22. On-the-Fly Guidance Training for Medical Image Registration

    Yuelin Xin, Yicheng Chen, Shengxiang Ji, and 2 more authors

    Jul 2024

    arXiv:2308.15216 [cs]

    Abs

    This study introduces a novel On-the-Fly Guidance (OFG) training framework for enhancing existing learning-based image registration models, addressing the limitations of weakly-supervised and unsupervised methods. Weakly-supervised methods struggle due to the scarcity of labeled data, and unsupervised methods directly depend on image similarity metrics for accuracy. Our method proposes a supervised fashion for training registration models, without the need for any labeled data. OFG generates pseudo-ground truth during training by refining deformation predictions with a differentiable optimizer, enabling direct supervised learning. OFG optimizes deformation predictions efficiently, improving the performance of registration models without sacrificing inference speed. Our method is tested across several benchmark datasets and leading models, it significantly enhanced performance, providing a plug-and-play solution for training learning-based registration models. Code available at: https://github.com/cilix-ai/on-the-fly-guidance

23. AFTer-SAM: Adapting SAM with Axial Fusion Transformer for Medical Imaging Segmentation

    Xiangyi Yan, Shanlin Sun, Kun Han, and 4 more authors

    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Jul 2024
24. CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer

    Haoyu Ma, Tong Zhang, Shanlin Sun, and 3 more authors

    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Jul 2024
25. Self-sampling meta SAM: enhancing few-shot medical image segmentation with meta-learning

    Tianang Leng, Yiming Zhang, Kun Han, and 1 more author

    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Jul 2024
26. Hybrid neural diffeomorphic flow for shape representation and generation via triplane

    Kun Han, Shanlin Sun, Thanh-Tung Le, and 4 more authors

    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Jul 2024
27. Adaptive Image Registration: A Hybrid Approach Integrating Deep Learning and Optimization Functions for Enhanced Precision

    Gabriel De Araujo, Shanlin Sun, and Xiaohui Xie

    Jan 2024

    arXiv:2311.15497 [cs]

    Abs

    Image registration has traditionally been done using two distinct approaches: learning based methods, relying on robust deep neural networks, and optimization-based methods, applying complex mathematical transformations to warp images accordingly. Of course, both paradigms offer advantages and disadvantages, and, in this work, we seek to combine their respective strengths into a single streamlined framework, using the outputs of the learning based method as initial parameters for optimization while prioritizing computational power for the image pairs that offer the greatest loss. Our investigations showed improvements of up to 1.6% in test data, while maintaining the same inference time, and a substantial 1.0% points performance gain in deformation field smoothness.

28. Diffeomorphic Mesh Deformation via Efficient Optimal Transport for Cortical Surface Reconstruction

    Tung Le, Khai Nguyen, Shanlin Sun, and 3 more authors

    Mar 2024

    arXiv:2305.17555 [cs]

    Abs

    Mesh deformation plays a pivotal role in many 3D vision tasks including dynamic simulations, rendering, and reconstruction. However, defining an efficient discrepancy between predicted and target meshes remains an open problem. A prevalent approach in current deep learning is the set-based approach which measures the discrepancy between two surfaces by comparing two randomly sampled point-clouds from the two meshes with Chamfer pseudo-distance. Nevertheless, the set-based approach still has limitations such as lacking a theoretical guarantee for choosing the number of points in sampled point-clouds, and the pseudo-metricity and the quadratic complexity of the Chamfer divergence. To address these issues, we propose a novel metric for learning mesh deformation. The metric is defined by sliced Wasserstein distance on meshes represented as probability measures that generalize the set-based approach. By leveraging probability measure space, we gain flexibility in encoding meshes using diverse forms of probability measures, such as continuous, empirical, and discrete measures via varifold representation. After having encoded probability measures, we can compare meshes by using the sliced Wasserstein distance which is an effective optimal transport distance with linear computational complexity and can provide a fast statistical rate for approximating the surface of meshes. To the end, we employ a neural ordinary differential equation (ODE) to deform the input surface into the target shape by modeling the trajectories of the points on the surface. Our experiments on cortical surface reconstruction demonstrate that our approach surpasses other competing methods in multiple datasets and metrics.

2023

1. Localized Region Contrast for Enhancing Self-supervised Learning in Medical Image Segmentation

   Xiangyi Yan, Junayed Naushad, Chenyu You, and 6 more authors

   In Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Mar 2023

   Series Title: Lecture Notes in Computer Science
2. MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation

   Kun Han, Yifeng Xiong, Chenyu You, and 5 more authors

   In Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Mar 2023

   Series Title: Lecture Notes in Computer Science
3. Identifying dysregulated regions in amyotrophic lateral sclerosis through DNA accessibility outliers

   M. H. Celik, J. Gagneur, R. G. Lim, and 3 more authors

   Mar 2023
4. On-the-Fly Guidance Training for Medical Image Registration

   Yuelin Xin, Yicheng Chen, Shengxiang Ji, and 2 more authors

   arXiv e-prints, Mar 2023
5. Peeling the onion: Hierarchical reduction of data redundancy for efficient vision transformer training

   Zhenglun Kong, Haoyu Ma, Geng Yuan, and 7 more authors

   In Proceedings of the AAAI Conference on Artificial Intelligence, Mar 2023

   Issue: 7
6. Light field diffusion for single-view novel view synthesis

   Yifeng Xiong, Haoyu Ma, Shanlin Sun, and 3 more authors

   arXiv preprint arXiv:2309.11525, Mar 2023
7. Diffeomorphic Deformation via Sliced Wasserstein Distance Optimization for Cortical Surface Reconstruction.

   Tung Le, Khai Nguyen, Shanlin Sun, and 3 more authors

   arXiv preprint arXiv:2305.17555, Mar 2023
8. Self-sampling meta sam: Enhancing few-shot medical image segmentation with meta-learning

   Yiming Zhang, Tianang Leng, Kun Han, and 1 more author

   arXiv preprint arXiv:2308.16466, Mar 2023
9. Diffeomorphic image registration with neural velocity field

   Kun Han, Shanlin Sun, Xiangyi Yan, and 6 more authors

   In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Mar 2023
10. Representation recovering for self-supervised pre-training on medical images

    Xiangyi Yan, Junayed Naushad, Shanlin Sun, and 6 more authors

    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Mar 2023
11. Hybrid-csr: Coupling explicit and implicit shape representation for cortical surface reconstruction

    Shanlin Sun, Thanh-Tung Le, Chenyu You, and 6 more authors

    arXiv preprint arXiv:2307.12299, Mar 2023
12. Diffeomorphic mesh deformation via efficient optimal transport for cortical surface reconstruction

    Tung Le, Khai Nguyen, Shanlin Sun, and 3 more authors

    arXiv preprint arXiv:2305.17555, Mar 2023
13. Localized Region Contrast for Enhancing Self-supervised Learning in Medical Image Segmentation

    Xiangyi Yan, Junayed Naushad, Chenyu You, and 6 more authors

    In Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Mar 2023

    Series Title: Lecture Notes in Computer Science
14. MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation

    Kun Han, Yifeng Xiong, Chenyu You, and 5 more authors

    In Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Mar 2023

    Series Title: Lecture Notes in Computer Science
15. Identifying dysregulated regions in amyotrophic lateral sclerosis through DNA accessibility outliers

    M. H. Celik, J. Gagneur, R. G. Lim, and 3 more authors

    Mar 2023
16. On-the-Fly Guidance Training for Medical Image Registration

    Yuelin Xin, Yicheng Chen, Shengxiang Ji, and 2 more authors

    arXiv e-prints, Mar 2023
17. Peeling the onion: Hierarchical reduction of data redundancy for efficient vision transformer training

    Zhenglun Kong, Haoyu Ma, Geng Yuan, and 7 more authors

    In Proceedings of the AAAI Conference on Artificial Intelligence, Mar 2023

    Issue: 7
18. Light field diffusion for single-view novel view synthesis

    Yifeng Xiong, Haoyu Ma, Shanlin Sun, and 3 more authors

    arXiv preprint arXiv:2309.11525, Mar 2023
19. Diffeomorphic Deformation via Sliced Wasserstein Distance Optimization for Cortical Surface Reconstruction.

    Tung Le, Khai Nguyen, Shanlin Sun, and 3 more authors

    arXiv preprint arXiv:2305.17555, Mar 2023
20. Self-sampling meta sam: Enhancing few-shot medical image segmentation with meta-learning

    Yiming Zhang, Tianang Leng, Kun Han, and 1 more author

    arXiv preprint arXiv:2308.16466, Mar 2023
21. Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for Cortical Surface Reconstruction

    Shanlin Sun, Thanh-Tung Le, Chenyu You, and 6 more authors

    Jul 2023

    arXiv:2307.12299 [cs]

    Abs

    We present Hybrid-CSR, a geometric deep-learning model that combines explicit and implicit shape representations for cortical surface reconstruction. Specifically, Hybrid-CSR begins with explicit deformations of template meshes to obtain coarsely reconstructed cortical surfaces, based on which the oriented point clouds are estimated for the subsequent differentiable poisson surface reconstruction. By doing so, our method unifies explicit (oriented point clouds) and implicit (indicator function) cortical surface reconstruction. Compared to explicit representation-based methods, our hybrid approach is more friendly to capture detailed structures, and when compared with implicit representation-based methods, our method can be topology aware because of end-to-end training with a mesh-based deformation module. In order to address topology defects, we propose a new topology correction pipeline that relies on optimization-based diffeomorphic surface registration. Experimental results on three brain datasets show that our approach surpasses existing implicit and explicit cortical surface reconstruction methods in numeric metrics in terms of accuracy, regularity, and consistency.

22. Diffeomorphic image registration with neural velocity field

    Kun Han, Shanlin Sun, Xiangyi Yan, and 6 more authors

    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Jul 2023
23. Optron: Better Medical Image Registration via Training in the Loop

    Yicheng Chen, Shengxiang Ji, Yuelin Xin, and 2 more authors

    arXiv preprint arXiv:2308.15216, Jul 2023

2022

1. PPT: Token-Pruned Pose Transformer for Monocular and Multi-view Human Pose Estimation

   Haoyu Ma, Zhe Wang, Yifei Chen, and 6 more authors

   In Computer Vision – ECCV 2022, Jul 2022

   Series Title: Lecture Notes in Computer Science
2. Integrated analysis of multimodal single-cell data with structural similarity

   Yingxin Cao, Laiyi Fu, Jie Wu, and 4 more authors

   Nucleic acids research, Jul 2022

   Publisher: Oxford University Press
3. Identity-Aware Hand Mesh Estimation and Personalization from RGB Images

   Deying Kong, Linguang Zhang, Liangjian Chen, and 6 more authors

   In Computer Vision – ECCV 2022, Jul 2022

   Series Title: Lecture Notes in Computer Science
4. Sparse logits suffice to fail knowledge distillation

   Haoyu Ma, Yifan Huang, Hao Tang, and 3 more authors

   In ICLR 2022 Workshop on PAIR {}backslashtextasciicircum} 2Struct: Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data, Jul 2022
5. UFold: fast and accurate RNA secondary structure prediction with deep learning

   Laiyi Fu, Yingxin Cao, Jie Wu, and 3 more authors

   Nucleic acids research, Jul 2022

   Publisher: Oxford University Press
6. Ei-clip: Entity-aware interventional contrastive learning for e-commerce cross-modal retrieval

   Haoyu Ma, Handong Zhao, Zhe Lin, and 6 more authors

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jul 2022
7. Topology-preserving shape reconstruction and registration via neural diffeomorphic flow

   Shanlin Sun, Kun Han, Deying Kong, and 3 more authors

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jul 2022
8. Deep learning assisted mechanotyping of individual cells through repeated deformations and relaxations in undulating channels

   Cody Combs, Daniel D. Seith, Matthew J. Bovyn, and 3 more authors

   Biomicrofluidics, Jul 2022

   Publisher: AIP Publishing
9. Stingy teacher: Sparse logits suffice to fail knowledge distillation

   Haoyu Ma, Yifan Huang, Tianlong Chen, and 4 more authors

   Jul 2022
10. After-unet: Axial fusion transformer unet for medical image segmentation

    Xiangyi Yan, Hao Tang, Shanlin Sun, and 3 more authors

    In Proceedings of the IEEE/CVF winter conference on applications of computer vision, Jul 2022
11. Medical image registration via neural fields

    Shanlin Sun, Kun Han, Hao Tang, and 4 more authors

    arXiv preprint arXiv:2206.03111, Jul 2022

2021

1. Learning Sample-Specific Policies for Sequential Image Augmentation

   Pu Li, Xiaobai Liu, and Xiaohui Xie

   In Proceedings of the 29th ACM International Conference on Multimedia, Oct 2021
2. Recurrent mask refinement for few-shot medical image segmentation

   Hao Tang, Xingwei Liu, Shanlin Sun, and 2 more authors

   In Proceedings of the IEEE/CVF international conference on computer vision, Oct 2021
3. PD-Net: Quantitative Motor Function Evaluation for Parkinson’s Disease via Automated Hand Gesture Analysis

   Yifei Chen, Haoyu Ma, Jiangyuan Wang, and 3 more authors

   In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Aug 2021
4. Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure

   Yi Sun, Xingzhi Wang, Jiayin Zhu, and 6 more authors

   Science of The Total Environment, Aug 2021

   Publisher: Elsevier
5. Transfusion: Cross-view fusion with transformer for 3d human pose estimation

   Haoyu Ma, Liangjian Chen, Deying Kong, and 7 more authors

   arXiv preprint arXiv:2110.09554, Aug 2021

2019

1. AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy

   Wentao Zhu, Yufang Huang, Liang Zeng, and 6 more authors

   Medical Physics, Feb 2019

   Abs

   Purpose Radiation therapy (RT) is a common treatment option for head and neck (HaN) cancer. An important step involved in RT planning is the delineation of organs‐at‐risks (OARs) based on HaN computed tomography (CT). However, manually delineating OARs is time‐consuming as each slice of CT images needs to be individually examined and a typical CT consists of hundreds of slices. Automating OARs segmentation has the benefit of both reducing the time and improving the quality of RT planning. Existing anatomy autosegmentation algorithms use primarily atlas‐based methods, which require sophisticated atlas creation and cannot adequately account for anatomy variations among patients. In this work, we propose an end‐to‐end, atlas‐free three‐dimensional (3D) convolutional deep learning framework for fast and fully automated whole‐volume HaN anatomy segmentation. Methods Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end‐to‐end fashion, receiving whole‐volume HaN CT images as input and generating masks of all OARs of interest in one shot. AnatomyNet is built upon the popular 3D U‐net architecture, but extends it in three important ways: (a) a new encoding scheme to allow autosegmentation on whole‐volume CT images instead of local patches or subsets of slices, (b) incorporating 3D squeeze‐and‐excitation residual blocks in encoding layers for better feature representation, and (c) a new loss function combining Dice scores and focal loss to facilitate the training of the neural model. These features are designed to address two main challenges in deep learning‐based HaN segmentation: (a) segmenting small anatomies (i.e., optic chiasm and optic nerves) occupying only a few slices, and (b) training with inconsistent data annotations with missing ground truth for some anatomical structures. Results We collected 261 HaN CT images to train AnatomyNet and used MICCAI Head and Neck Auto Segmentation Challenge 2015 as a benchmark dataset to evaluate the performance of AnatomyNet. The objective is to segment nine anatomies: brain stem, chiasm, mandible, optic nerve left, optic nerve right, parotid gland left, parotid gland right, submandibular gland left, and submandibular gland right. Compared to previous state‐of‐the‐art results from the MICCAI 2015 competition, AnatomyNet increases Dice similarity coefficient by 3.3% on average. AnatomyNet takes about 0.12 s to fully segment a head and neck CT image of dimension 178 × 302 × 225, significantly faster than previous methods. In addition, the model is able to process whole‐volume CT images and delineate all OARs in one pass, requiring little pre‐ or postprocessing. Conclusion Deep learning models offer a feasible solution to the problem of delineating OARs from CT images. We demonstrate that our proposed model can improve segmentation accuracy and simplify the autosegmentation pipeline. With this method, it is possible to delineate OARs of a head and neck CT within a fraction of a second.

2018

1. Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification

   Wentao Zhu, Chaochun Liu, Wei Fan, and 1 more author

   In 2018 IEEE winter conference on applications of computer vision (WACV), Feb 2018

2016

1. Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks

   Wentao Zhu, Cuiling Lan, Junliang Xing, and 4 more authors

   In Proceedings of the AAAI conference on artificial intelligence, Feb 2016

   Issue: 1
2. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences

   Daniel Quang, and Xiaohui Xie

   Nucleic acids research, Feb 2016

   Publisher: Oxford University Press
3. Gene expression inference with deep learning

   Yifei Chen, Yi Li, Rajiv Narayan, and 2 more authors

   Bioinformatics, Feb 2016

   Publisher: Oxford University Press

2014

1. DANN: a deep learning approach for annotating the pathogenicity of genetic variants

   Daniel Quang, Yifei Chen, and Xiaohui Xie

   Bioinformatics, Feb 2014

   Publisher: Oxford University Press

2011

1. A high-resolution map of human evolutionary constraint using 29 mammals

   Kerstin Lindblad-Toh, Manuel Garber, Or Zuk, and 7 more authors

   Nature, Feb 2011

   Publisher: Nature Publishing Group UK London

2010

1. Comparative genomics reveals mobile pathogenicity chromosomes in Fusarium

   Li-Jun Ma, H. Charlotte Van Der Does, Katherine A. Borkovich, and 7 more authors

   Nature, Feb 2010

   Publisher: Nature Publishing Group UK London

2009

1. Identifying novel constrained elements by exploiting biased substitution patterns

   Manuel Garber, Mitchell Guttman, Michele Clamp, and 3 more authors

   Bioinformatics, Feb 2009

   Publisher: Oxford University Press

2008

1. Genomewide analysis of PRC1 and PRC2 occupancy identifies two classes of bivalent domains

   Manching Ku, Richard P. Koche, Esther Rheinbay, and 7 more authors

   PLoS genetics, Feb 2008

   Publisher: Public Library of Science San Francisco, USA

2007

1. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells

   Tarjei S. Mikkelsen, Manching Ku, David B. Jaffe, and 7 more authors

   Nature, Feb 2007

   Publisher: Nature Publishing Group UK London
2. Genome-wide detection and characterization of positive selection in human populations

   Pardis C. Sabeti, Patrick Varilly, Ben Fry, and 7 more authors

   Nature, Feb 2007

   Publisher: Nature Publishing Group UK London
3. Distinguishing protein-coding and noncoding genes in the human genome

   Michele Clamp, Ben Fry, Mike Kamal, and 6 more authors

   Proc. Natl. Acad. Sci. U.S.A., Dec 2007

   Abs

   Although the Human Genome Project was completed 4 years ago, the catalog of human protein-coding genes remains a matter of controversy. Current catalogs list a total of ≈24,500 putative protein-coding genes. It is broadly suspected that a large fraction of these entries are functionally meaningless ORFs present by chance in RNA transcripts, because they show no evidence of evolutionary conservation with mouse or dog. However, there is currently no scientific justification for excluding ORFs simply because they fail to show evolutionary conservation: the alternative hypothesis is that most of these ORFs are actually valid human genes that reflect gene innovation in the primate lineage or gene loss in the other lineages. Here, we reject this hypothesis by carefully analyzing the nonconserved ORFs—specifically, their properties in other primates. We show that the vast majority of these ORFs are random occurrences. The analysis yields, as a by-product, a major revision of the current human catalogs, cutting the number of protein-coding genes to ≈20,500. Specifically, it suggests that nonconserved ORFs should be added to the human gene catalog only if there is clear evidence of an encoded protein. It also provides a principled methodology for evaluating future proposed additions to the human gene catalog. Finally, the results indicate that there has been relatively little true innovation in mammalian protein-coding genes.

4. Genome of the marsupial Monodelphis domestica reveals innovation in non-coding sequences

   Tarjei S. Mikkelsen, Matthew J. Wakefield, Bronwen Aken, and 7 more authors

   Nature, Dec 2007

   Publisher: Nature Publishing Group UK London

2006

1. A bivalent chromatin structure marks key developmental genes in embryonic stem cells

   Bradley E. Bernstein, Tarjei S. Mikkelsen, Xiaohui Xie, and 7 more authors

   Cell, Dec 2006

   Publisher: Elsevier
2. A mammalian organelle map by protein correlation profiling

   Leonard J. Foster, Carmen L. Hoog, Yanling Zhang, and 4 more authors

   Cell, Dec 2006

   Publisher: Elsevier
3. Systematic identification of human mitochondrial disease genes through integrative genomics

   Sarah Calvo, Mohit Jain, Xiaohui Xie, and 7 more authors

   Nature genetics, Dec 2006

   Publisher: Nature Publishing Group US New York

2005

1. Genome sequence, comparative analysis and haplotype structure of the domestic dog

   Kerstin Lindblad-Toh, Claire M. Wade, Tarjei S. Mikkelsen, and 7 more authors

   Nature, Dec 2005

   Publisher: Nature Publishing Group UK London
2. Systematic discovery of regulatory motifs in human promoters and 3′ UTRs by comparison of several mammals

   Xiaohui Xie, Jun Lu, E. J. Kulbokas, and 5 more authors

   Nature, Dec 2005

   Publisher: Nature Publishing Group UK London

2004

1. Errα and Gabpa/b specify PGC-1α-dependent oxidative phosphorylation gene expression that is altered in diabetic muscle

   Vamsi K. Mootha, Christoph Handschin, Dan Arlow, and 12 more authors

   Proc. Natl. Acad. Sci. U.S.A., Apr 2004

   Abs

   Recent studies have shown that genes involved in oxidative phosphorylation (OXPHOS) exhibit reduced expression in skeletal muscle of diabetic and prediabetic humans. Moreover, these changes may be mediated by the transcriptional coactivator peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α). By combining PGC-1α-induced genome-wide transcriptional profiles with a computational strategy to detect cis-regulatory motifs, we identified estrogen-related receptor α (Errα) and GA repeat-binding protein α as key transcription factors regulating the OXPHOS pathway. Interestingly, the genes encoding these two transcription factors are themselves PGC-1α-inducible and contain variants of both motifs near their promoters. Cellular assays confirmed that Errα and GA-binding protein a partner with PGC-1α in muscle to form a double-positive-feedback loop that drives the expression of many OXPHOS genes. By using a synthetic inhibitor of Errα, we demonstrated its key role in PGC-1α-mediated effects on gene regulation and cellular respiration. These results illustrate the dissection of gene regulatory networks in a complex mammalian system, elucidate the mechanism of PGC-1α action in the OXPHOS pathway, and suggest that Errα agonists may ameliorate insulin-resistance in individuals with type 2 diabetes mellitus.