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41 results on '"Fang, Zhaoyuan"'

1. Analogy-Forming Transformers for Few-Shot 3D Parsing

Author

Gkanatsios, Nikolaos, Singh, Mayank, Fang, Zhaoyuan, Tulsiani, Shubham, and Fragkiadaki, Katerina

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

We present Analogical Networks, a model that encodes domain knowledge explicitly, in a collection of structured labelled 3D scenes, in addition to implicitly, as model parameters, and segments 3D object scenes with analogical reasoning: instead of mapping a scene to part segments directly, our model first retrieves related scenes from memory and their corresponding part structures, and then predicts analogous part structures for the input scene, via an end-to-end learnable modulation mechanism. By conditioning on more than one retrieved memories, compositions of structures are predicted, that mix and match parts across the retrieved memories. One-shot, few-shot or many-shot learning are treated uniformly in Analogical Networks, by conditioning on the appropriate set of memories, whether taken from a single, few or many memory exemplars, and inferring analogous parses. We show Analogical Networks are competitive with state-of-the-art 3D segmentation transformers in many-shot settings, and outperform them, as well as existing paradigms of meta-learning and few-shot learning, in few-shot settings. Analogical Networks successfully segment instances of novel object categories simply by expanding their memory, without any weight updates. Our code and models are publicly available in the project webpage: http://analogicalnets.github.io/., Comment: ICLR 2023

Published
2023

2. TIDEE: Tidying Up Novel Rooms using Visuo-Semantic Commonsense Priors

Author

Sarch, Gabriel, Fang, Zhaoyuan, Harley, Adam W., Schydlo, Paul, Tarr, Michael J., Gupta, Saurabh, and Fragkiadaki, Katerina

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We introduce TIDEE, an embodied agent that tidies up a disordered scene based on learned commonsense object placement and room arrangement priors. TIDEE explores a home environment, detects objects that are out of their natural place, infers plausible object contexts for them, localizes such contexts in the current scene, and repositions the objects. Commonsense priors are encoded in three modules: i) visuo-semantic detectors that detect out-of-place objects, ii) an associative neural graph memory of objects and spatial relations that proposes plausible semantic receptacles and surfaces for object repositions, and iii) a visual search network that guides the agent's exploration for efficiently localizing the receptacle-of-interest in the current scene to reposition the object. We test TIDEE on tidying up disorganized scenes in the AI2THOR simulation environment. TIDEE carries out the task directly from pixel and raw depth input without ever having observed the same room beforehand, relying only on priors learned from a separate set of training houses. Human evaluations on the resulting room reorganizations show TIDEE outperforms ablative versions of the model that do not use one or more of the commonsense priors. On a related room rearrangement benchmark that allows the agent to view the goal state prior to rearrangement, a simplified version of our model significantly outperforms a top-performing method by a large margin. Code and data are available at the project website: https://tidee-agent.github.io/.

Published
2022

3. Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?

Author

Harley, Adam W., Fang, Zhaoyuan, Li, Jie, Ambrus, Rares, and Fragkiadaki, Katerina

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Building 3D perception systems for autonomous vehicles that do not rely on high-density LiDAR is a critical research problem because of the expense of LiDAR systems compared to cameras and other sensors. Recent research has developed a variety of camera-only methods, where features are differentiably "lifted" from the multi-camera images onto the 2D ground plane, yielding a "bird's eye view" (BEV) feature representation of the 3D space around the vehicle. This line of work has produced a variety of novel "lifting" methods, but we observe that other details in the training setups have shifted at the same time, making it unclear what really matters in top-performing methods. We also observe that using cameras alone is not a real-world constraint, considering that additional sensors like radar have been integrated into real vehicles for years already. In this paper, we first of all attempt to elucidate the high-impact factors in the design and training protocol of BEV perception models. We find that batch size and input resolution greatly affect performance, while lifting strategies have a more modest effect -- even a simple parameter-free lifter works well. Second, we demonstrate that radar data can provide a substantial boost to performance, helping to close the gap between camera-only and LiDAR-enabled systems. We analyze the radar usage details that lead to good performance, and invite the community to re-consider this commonly-neglected part of the sensor platform.

Published
2022

4. Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories

Author

Harley, Adam W., Fang, Zhaoyuan, and Fragkiadaki, Katerina

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Tracking pixels in videos is typically studied as an optical flow estimation problem, where every pixel is described with a displacement vector that locates it in the next frame. Even though wider temporal context is freely available, prior efforts to take this into account have yielded only small gains over 2-frame methods. In this paper, we revisit Sand and Teller's "particle video" approach, and study pixel tracking as a long-range motion estimation problem, where every pixel is described with a trajectory that locates it in multiple future frames. We re-build this classic approach using components that drive the current state-of-the-art in flow and object tracking, such as dense cost maps, iterative optimization, and learned appearance updates. We train our models using long-range amodal point trajectories mined from existing optical flow data that we synthetically augment with multi-frame occlusions. We test our approach in trajectory estimation benchmarks and in keypoint label propagation tasks, and compare favorably against state-of-the-art optical flow and feature tracking methods.

Published
2022

6. Adeno-to-squamous transition drives resistance to KRAS inhibition in LKB1 mutant lung cancer

Author

Tong, Xinyuan, Patel, Ayushi S., Kim, Eejung, Li, Hongjun, Chen, Yueqing, Li, Shuai, Liu, Shengwu, Dilly, Julien, Kapner, Kevin S., Zhang, Ningxia, Xue, Yun, Hover, Laura, Mukhopadhyay, Suman, Sherman, Fiona, Myndzar, Khrystyna, Sahu, Priyanka, Gao, Yijun, Li, Fei, Li, Fuming, Fang, Zhaoyuan, Jin, Yujuan, Gao, Juntao, Shi, Minglei, Sinha, Satrajit, Chen, Luonan, Chen, Yang, Kheoh, Thian, Yang, Wenjing, Yanai, Itai, Moreira, Andre L., Velcheti, Vamsidhar, Neel, Benjamin G., Hu, Liang, Christensen, James G., Olson, Peter, Gao, Dong, Zhang, Michael Q., Aguirre, Andrew J., Wong, Kwok-Kin, and Ji, Hongbin

Published
2024
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7. Review of Iris Presentation Attack Detection Competitions

Author

Yambay, David, Das, Priyanka, Boyd, Aidan, McGrath, Joseph, Fang, Zhaoyuan (Andy), Czajka, Adam, Schuckers, Stephanie, Bowyer, Kevin, Vatsa, Mayank, Singh, Richa, Noore, Afzel, Kohli, Naman, Yadav, Daksha, Trokielewicz, Mateusz, Maciejewicz, Piotr, Mohammadi, Amir, Marcel, Sébastien, Singh, Sameer, Founding Editor, Kang, Sing Bing, Series Editor, Bischof, Horst, Advisory Editor, Bowden, Richard, Advisory Editor, Dickinson, Sven, Advisory Editor, Jia, Jiaya, Advisory Editor, Lee, Kyoung Mu, Advisory Editor, Lin, Zhouchen, Advisory Editor, Sato, Yoichi, Advisory Editor, Schiele, Bernt, Advisory Editor, Sclaroff, Stan, Advisory Editor, Marcel, Sébastien, editor, Fierrez, Julian, editor, and Evans, Nicholas, editor

Published
2023
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8. Review of Iris Presentation Attack Detection Competitions

Author

Yambay, David, primary, Das, Priyanka, additional, Boyd, Aidan, additional, McGrath, Joseph, additional, Fang, Zhaoyuan, additional, Czajka, Adam, additional, Schuckers, Stephanie, additional, Bowyer, Kevin, additional, Vatsa, Mayank, additional, Singh, Richa, additional, Noore, Afzel, additional, Kohli, Naman, additional, Yadav, Daksha, additional, Trokielewicz, Mateusz, additional, Maciejewicz, Piotr, additional, Mohammadi, Amir, additional, and Marcel, Sébastien, additional

Published
2023
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10. Single-cell and spatial proteo-transcriptomic profiling reveals immune infiltration heterogeneity associated with neuroendocrine features in small cell lung cancer.

Author

Jin, Ying, Wu, Yuefeng, Reuben, Alexandre, Zhu, Liang, Gay, Carl M., Wu, Qingzhe, Zhou, Xintong, Mo, Haomin, Zheng, Qi, Ren, Junyu, Fang, Zhaoyuan, Peng, Teng, Wang, Nan, Ma, Liang, Fan, Yun, Song, Hai, Zhang, Jianjun, and Chen, Ming

Subjects
SMALL cell lung cancer, B cells, RNA metabolism, HETEROGENEITY, NEST predation
Abstract

Small cell lung cancer (SCLC) is an aggressive pulmonary neuroendocrine malignancy featured by cold tumor immune microenvironment (TIME), limited benefit from immunotherapy, and poor survival. The spatial heterogeneity of TIME significantly associated with anti-tumor immunity has not been systemically studied in SCLC. We performed ultra-high-plex Digital Spatial Profiling on 132 tissue microarray cores from 44 treatment-naive limited-stage SCLC tumors. Incorporating single-cell RNA-sequencing data from a local cohort and published SCLC data, we established a spatial proteo-transcriptomic landscape covering over 18,000 genes and 60 key immuno-oncology proteins that participate in signaling pathways affecting tumorigenesis, immune regulation, and cancer metabolism across 3 pathologically defined spatial compartments (pan-CK-positive tumor nest; CD45/CD3-positive tumor stroma; para-tumor). Our study depicted the spatial transcriptomic and proteomic TIME architecture of SCLC, indicating clear intra-tumor heterogeneity dictated via canonical neuroendocrine subtyping markers; revealed the enrichment of innate immune cells and functionally impaired B cells in tumor nest and suggested potentially important immunoregulatory roles of monocytes/macrophages. We identified RE1 silencing factor (REST) as a potential biomarker for SCLC associated with low neuroendocrine features, more active anti-tumor immunity, and prolonged survival. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Figure S2 from In Vivo Epigenetic CRISPR Screen Identifies Asf1a as an Immunotherapeutic Target in Kras-Mutant Lung Adenocarcinoma

Author

Li, Fei, primary, Huang, Qingyuan, primary, Luster, Troy A., primary, Hu, Hai, primary, Zhang, Hua, primary, Ng, Wai-Lung, primary, Khodadadi-Jamayran, Alireza, primary, Wang, Wei, primary, Chen, Ting, primary, Deng, Jiehui, primary, Ranieri, Michela, primary, Fang, Zhaoyuan, primary, Pyon, Val, primary, Dowling, Catríona M., primary, Bagdatlioglu, Ece, primary, Almonte, Christina, primary, Labbe, Kristen, primary, Silver, Heather, primary, Rabin, Alexandra R., primary, Jani, Kandarp, primary, Tsirigos, Aristotelis, primary, Papagiannakopoulos, Thales, primary, Hammerman, Peter S., primary, Velcheti, Vamsidhar, primary, Freeman, Gordon J., primary, Qi, Jun, primary, Miller, George, primary, and Wong, Kwok-Kin, primary

Published
2023
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17. Data from In Vivo Epigenetic CRISPR Screen Identifies Asf1a as an Immunotherapeutic Target in Kras-Mutant Lung Adenocarcinoma

Author

Li, Fei, primary, Huang, Qingyuan, primary, Luster, Troy A., primary, Hu, Hai, primary, Zhang, Hua, primary, Ng, Wai-Lung, primary, Khodadadi-Jamayran, Alireza, primary, Wang, Wei, primary, Chen, Ting, primary, Deng, Jiehui, primary, Ranieri, Michela, primary, Fang, Zhaoyuan, primary, Pyon, Val, primary, Dowling, Catríona M., primary, Bagdatlioglu, Ece, primary, Almonte, Christina, primary, Labbe, Kristen, primary, Silver, Heather, primary, Rabin, Alexandra R., primary, Jani, Kandarp, primary, Tsirigos, Aristotelis, primary, Papagiannakopoulos, Thales, primary, Hammerman, Peter S., primary, Velcheti, Vamsidhar, primary, Freeman, Gordon J., primary, Qi, Jun, primary, Miller, George, primary, and Wong, Kwok-Kin, primary

Published
2023
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18. Table S1 from In Vivo Epigenetic CRISPR Screen Identifies Asf1a as an Immunotherapeutic Target in Kras-Mutant Lung Adenocarcinoma

Author

Li, Fei, primary, Huang, Qingyuan, primary, Luster, Troy A., primary, Hu, Hai, primary, Zhang, Hua, primary, Ng, Wai-Lung, primary, Khodadadi-Jamayran, Alireza, primary, Wang, Wei, primary, Chen, Ting, primary, Deng, Jiehui, primary, Ranieri, Michela, primary, Fang, Zhaoyuan, primary, Pyon, Val, primary, Dowling, Catríona M., primary, Bagdatlioglu, Ece, primary, Almonte, Christina, primary, Labbe, Kristen, primary, Silver, Heather, primary, Rabin, Alexandra R., primary, Jani, Kandarp, primary, Tsirigos, Aristotelis, primary, Papagiannakopoulos, Thales, primary, Hammerman, Peter S., primary, Velcheti, Vamsidhar, primary, Freeman, Gordon J., primary, Qi, Jun, primary, Miller, George, primary, and Wong, Kwok-Kin, primary

Published
2023
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19. Supplementary Figure Legend from The Use of Quantitative Real-Time Reverse Transcriptase PCR for 5′ and 3′ Portions of ALK Transcripts to Detect ALK Rearrangements in Lung Cancers

Author

Wang, Rui, primary, Pan, Yunjian, primary, Li, Chenguang, primary, Hu, Haichuan, primary, Zhang, Yang, primary, Li, Hang, primary, Luo, Xiaoyang, primary, Zhang, Jie, primary, Fang, Zhaoyuan, primary, Li, Yuan, primary, Shen, Lei, primary, Ji, Hongbin, primary, Garfield, David, primary, Sun, Yihua, primary, and Chen, Haiquan, primary

Published
2023
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20. Table S1 from Epigenetic CRISPR Screens Identify Npm1 as a Therapeutic Vulnerability in Non–Small Cell Lung Cancer

Author

Li, Fei, primary, Ng, Wai-Lung, primary, Luster, Troy A., primary, Hu, Hai, primary, Sviderskiy, Vladislav O., primary, Dowling, Catríona M., primary, Hollinshead, Kate E.R., primary, Zouitine, Paula, primary, Zhang, Hua, primary, Huang, Qingyuan, primary, Ranieri, Michela, primary, Wang, Wei, primary, Fang, Zhaoyuan, primary, Chen, Ting, primary, Deng, Jiehui, primary, Zhao, Kai, primary, So, Hon-Cheong, primary, Khodadadi-Jamayran, Alireza, primary, Xu, Mousheng, primary, Karatza, Angeliki, primary, Pyon, Val, primary, Li, Shuai, primary, Pan, Yuanwang, primary, Labbe, Kristen, primary, Almonte, Christina, primary, Poirier, John T., primary, Miller, George, primary, Possemato, Richard, primary, Qi, Jun, primary, and Wong, Kwok-Kin, primary

Published
2023
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21. Supplementary Table 1 from The Use of Quantitative Real-Time Reverse Transcriptase PCR for 5′ and 3′ Portions of ALK Transcripts to Detect ALK Rearrangements in Lung Cancers

Author

Wang, Rui, primary, Pan, Yunjian, primary, Li, Chenguang, primary, Hu, Haichuan, primary, Zhang, Yang, primary, Li, Hang, primary, Luo, Xiaoyang, primary, Zhang, Jie, primary, Fang, Zhaoyuan, primary, Li, Yuan, primary, Shen, Lei, primary, Ji, Hongbin, primary, Garfield, David, primary, Sun, Yihua, primary, and Chen, Haiquan, primary

Published
2023
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22. Supplementary Figure 2 from The Use of Quantitative Real-Time Reverse Transcriptase PCR for 5′ and 3′ Portions of ALK Transcripts to Detect ALK Rearrangements in Lung Cancers

Author

Wang, Rui, primary, Pan, Yunjian, primary, Li, Chenguang, primary, Hu, Haichuan, primary, Zhang, Yang, primary, Li, Hang, primary, Luo, Xiaoyang, primary, Zhang, Jie, primary, Fang, Zhaoyuan, primary, Li, Yuan, primary, Shen, Lei, primary, Ji, Hongbin, primary, Garfield, David, primary, Sun, Yihua, primary, and Chen, Haiquan, primary

Published
2023
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23. Supplementary Figure 4 from The Use of Quantitative Real-Time Reverse Transcriptase PCR for 5′ and 3′ Portions of ALK Transcripts to Detect ALK Rearrangements in Lung Cancers

Author

Wang, Rui, primary, Pan, Yunjian, primary, Li, Chenguang, primary, Hu, Haichuan, primary, Zhang, Yang, primary, Li, Hang, primary, Luo, Xiaoyang, primary, Zhang, Jie, primary, Fang, Zhaoyuan, primary, Li, Yuan, primary, Shen, Lei, primary, Ji, Hongbin, primary, Garfield, David, primary, Sun, Yihua, primary, and Chen, Haiquan, primary

Published
2023
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24. Supplementary Table 2 from The Use of Quantitative Real-Time Reverse Transcriptase PCR for 5′ and 3′ Portions of ALK Transcripts to Detect ALK Rearrangements in Lung Cancers

Author

Wang, Rui, primary, Pan, Yunjian, primary, Li, Chenguang, primary, Hu, Haichuan, primary, Zhang, Yang, primary, Li, Hang, primary, Luo, Xiaoyang, primary, Zhang, Jie, primary, Fang, Zhaoyuan, primary, Li, Yuan, primary, Shen, Lei, primary, Ji, Hongbin, primary, Garfield, David, primary, Sun, Yihua, primary, and Chen, Haiquan, primary

Published
2023
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25. Supplementary Figure 1 from The Use of Quantitative Real-Time Reverse Transcriptase PCR for 5′ and 3′ Portions of ALK Transcripts to Detect ALK Rearrangements in Lung Cancers

Author

Wang, Rui, primary, Pan, Yunjian, primary, Li, Chenguang, primary, Hu, Haichuan, primary, Zhang, Yang, primary, Li, Hang, primary, Luo, Xiaoyang, primary, Zhang, Jie, primary, Fang, Zhaoyuan, primary, Li, Yuan, primary, Shen, Lei, primary, Ji, Hongbin, primary, Garfield, David, primary, Sun, Yihua, primary, and Chen, Haiquan, primary

Published
2023
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26. Data from Epigenetic CRISPR Screens Identify Npm1 as a Therapeutic Vulnerability in Non–Small Cell Lung Cancer

Author

Li, Fei, primary, Ng, Wai-Lung, primary, Luster, Troy A., primary, Hu, Hai, primary, Sviderskiy, Vladislav O., primary, Dowling, Catríona M., primary, Hollinshead, Kate E.R., primary, Zouitine, Paula, primary, Zhang, Hua, primary, Huang, Qingyuan, primary, Ranieri, Michela, primary, Wang, Wei, primary, Fang, Zhaoyuan, primary, Chen, Ting, primary, Deng, Jiehui, primary, Zhao, Kai, primary, So, Hon-Cheong, primary, Khodadadi-Jamayran, Alireza, primary, Xu, Mousheng, primary, Karatza, Angeliki, primary, Pyon, Val, primary, Li, Shuai, primary, Pan, Yuanwang, primary, Labbe, Kristen, primary, Almonte, Christina, primary, Poirier, John T., primary, Miller, George, primary, Possemato, Richard, primary, Qi, Jun, primary, and Wong, Kwok-Kin, primary

Published
2023
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27. Supplementary Table 3 from The Use of Quantitative Real-Time Reverse Transcriptase PCR for 5′ and 3′ Portions of ALK Transcripts to Detect ALK Rearrangements in Lung Cancers

Author

Wang, Rui, primary, Pan, Yunjian, primary, Li, Chenguang, primary, Hu, Haichuan, primary, Zhang, Yang, primary, Li, Hang, primary, Luo, Xiaoyang, primary, Zhang, Jie, primary, Fang, Zhaoyuan, primary, Li, Yuan, primary, Shen, Lei, primary, Ji, Hongbin, primary, Garfield, David, primary, Sun, Yihua, primary, and Chen, Haiquan, primary

Published
2023
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28. Supplementary Data from Epigenetic CRISPR Screens Identify Npm1 as a Therapeutic Vulnerability in Non–Small Cell Lung Cancer

Author

Li, Fei, primary, Ng, Wai-Lung, primary, Luster, Troy A., primary, Hu, Hai, primary, Sviderskiy, Vladislav O., primary, Dowling, Catríona M., primary, Hollinshead, Kate E.R., primary, Zouitine, Paula, primary, Zhang, Hua, primary, Huang, Qingyuan, primary, Ranieri, Michela, primary, Wang, Wei, primary, Fang, Zhaoyuan, primary, Chen, Ting, primary, Deng, Jiehui, primary, Zhao, Kai, primary, So, Hon-Cheong, primary, Khodadadi-Jamayran, Alireza, primary, Xu, Mousheng, primary, Karatza, Angeliki, primary, Pyon, Val, primary, Li, Shuai, primary, Pan, Yuanwang, primary, Labbe, Kristen, primary, Almonte, Christina, primary, Poirier, John T., primary, Miller, George, primary, Possemato, Richard, primary, Qi, Jun, primary, and Wong, Kwok-Kin, primary

Published
2023
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29. Supplementary Figure 3 from The Use of Quantitative Real-Time Reverse Transcriptase PCR for 5′ and 3′ Portions of ALK Transcripts to Detect ALK Rearrangements in Lung Cancers

Author

Wang, Rui, primary, Pan, Yunjian, primary, Li, Chenguang, primary, Hu, Haichuan, primary, Zhang, Yang, primary, Li, Hang, primary, Luo, Xiaoyang, primary, Zhang, Jie, primary, Fang, Zhaoyuan, primary, Li, Yuan, primary, Shen, Lei, primary, Ji, Hongbin, primary, Garfield, David, primary, Sun, Yihua, primary, and Chen, Haiquan, primary

Published
2023
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30. Data from The CRTC1-NEDD9 Signaling Axis Mediates Lung Cancer Progression Caused by LKB1 Loss

Author

Feng, Yan, primary, Wang, Ye, primary, Wang, Zuoyun, primary, Fang, Zhaoyuan, primary, Li, Fei, primary, Gao, Yijun, primary, Liu, Hongyan, primary, Xiao, Tian, primary, Li, Fuming, primary, Zhou, Yang, primary, Zhai, Qiwei, primary, Liu, Xiaolong, primary, Sun, Yihua, primary, Bardeesy, Nabeel, primary, Wong, Kwok-kin, primary, Chen, Haiquan, primary, Xiong, Zhi-qi, primary, and Ji, Hongbin, primary

Published
2023
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31. Supplementary Figure Legends 1-12 from The CRTC1-NEDD9 Signaling Axis Mediates Lung Cancer Progression Caused by LKB1 Loss

Author

Feng, Yan, primary, Wang, Ye, primary, Wang, Zuoyun, primary, Fang, Zhaoyuan, primary, Li, Fei, primary, Gao, Yijun, primary, Liu, Hongyan, primary, Xiao, Tian, primary, Li, Fuming, primary, Zhou, Yang, primary, Zhai, Qiwei, primary, Liu, Xiaolong, primary, Sun, Yihua, primary, Bardeesy, Nabeel, primary, Wong, Kwok-kin, primary, Chen, Haiquan, primary, Xiong, Zhi-qi, primary, and Ji, Hongbin, primary

Published
2023
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32. Supplementary Figures 1-12 from The CRTC1-NEDD9 Signaling Axis Mediates Lung Cancer Progression Caused by LKB1 Loss

Author

Feng, Yan, primary, Wang, Ye, primary, Wang, Zuoyun, primary, Fang, Zhaoyuan, primary, Li, Fei, primary, Gao, Yijun, primary, Liu, Hongyan, primary, Xiao, Tian, primary, Li, Fuming, primary, Zhou, Yang, primary, Zhai, Qiwei, primary, Liu, Xiaolong, primary, Sun, Yihua, primary, Bardeesy, Nabeel, primary, Wong, Kwok-kin, primary, Chen, Haiquan, primary, Xiong, Zhi-qi, primary, and Ji, Hongbin, primary

Published
2023
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33. Supplementary Methods from The CRTC1-NEDD9 Signaling Axis Mediates Lung Cancer Progression Caused by LKB1 Loss

Author

Feng, Yan, primary, Wang, Ye, primary, Wang, Zuoyun, primary, Fang, Zhaoyuan, primary, Li, Fei, primary, Gao, Yijun, primary, Liu, Hongyan, primary, Xiao, Tian, primary, Li, Fuming, primary, Zhou, Yang, primary, Zhai, Qiwei, primary, Liu, Xiaolong, primary, Sun, Yihua, primary, Bardeesy, Nabeel, primary, Wong, Kwok-kin, primary, Chen, Haiquan, primary, Xiong, Zhi-qi, primary, and Ji, Hongbin, primary

Published
2023
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34. Supplementary Table 1 from The CRTC1-NEDD9 Signaling Axis Mediates Lung Cancer Progression Caused by LKB1 Loss

Author

Feng, Yan, primary, Wang, Ye, primary, Wang, Zuoyun, primary, Fang, Zhaoyuan, primary, Li, Fei, primary, Gao, Yijun, primary, Liu, Hongyan, primary, Xiao, Tian, primary, Li, Fuming, primary, Zhou, Yang, primary, Zhai, Qiwei, primary, Liu, Xiaolong, primary, Sun, Yihua, primary, Bardeesy, Nabeel, primary, Wong, Kwok-kin, primary, Chen, Haiquan, primary, Xiong, Zhi-qi, primary, and Ji, Hongbin, primary

Published
2023
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35. Counteracting lineage-specific transcription factor network finely tunes lung adeno-to-squamous transdifferentiation through remodeling tumor immune microenvironment

Author

Tang, Shijie, primary, Xue, Yun, additional, Qin, Zhen, additional, Fang, Zhaoyuan, additional, Sun, Yihua, additional, Yuan, Chongzhe, additional, Pan, Yunjian, additional, Zhao, Yue, additional, Tong, Xinyuan, additional, Zhang, Jian, additional, Huang, Hsinyi, additional, Chen, Yuting, additional, Hu, Liang, additional, Huang, Dasong, additional, Wang, Ruiqi, additional, Zou, Weiguo, additional, Li, Yuan, additional, Thomas, Roman K, additional, Ventura, Andrea, additional, Wong, Kwok-Kin, additional, Chen, Haiquan, additional, Chen, Luonan, additional, and Ji, Hongbin, additional

Published
2023
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37. Loss of TSC1/TSC2 sensitizes immune checkpoint blockade in non–small cell lung cancer

Author

Huang, Qingyuan, primary, Li, Fei, additional, Hu, Hai, additional, Fang, Zhaoyuan, additional, Gao, Zhendong, additional, Xia, Guozhan, additional, Ng, Wai-Lung, additional, Khodadadi-Jamayran, Alireza, additional, Chen, Ting, additional, Deng, Jiehui, additional, Zhang, Hua, additional, Almonte, Christina, additional, Labbe, Kristen, additional, Han, Han, additional, Geng, Ke, additional, Tang, Sittinon, additional, Freeman, Gordon J., additional, Li, Yuan, additional, Chen, Haiquan, additional, and Wong, Kwok-Kin, additional

Published
2022
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38. Integrative analysis of multi‐omics data reveals the heterogeneity and signatures of immune therapy for small cell lung cancer

Author

Chen, Yabin, primary, Fang, Zhaoyuan, additional, Tang, Ying, additional, Jin, Yujuan, additional, Guo, Chenchen, additional, Hu, Liang, additional, Xu, Yang, additional, Ma, Xidong, additional, Gao, Jie, additional, Xie, Mei, additional, Zang, Xuelei, additional, Liu, Sanhong, additional, Chen, Haiquan, additional, Thomas, Roman K., additional, Xue, Xinying, additional, Ji, Hongbin, additional, and Chen, Luonan, additional

Published
2021
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39. TransRef enables accurate transcriptome assembly by redefining accurate neo-splicing graphs.

Author

Yu, Ting, Han, Renmin, Fang, Zhaoyuan, Mu, Zengchao, Zheng, Hongyu, and Liu, Juntao

Subjects
TRANSCRIPTOMES, DYNAMIC programming, RNA sequencing
Abstract

RNA-seq technology is widely employed in various research areas related to transcriptome analyses, and the identification of all the expressed transcripts from short sequencing reads presents a considerable computational challenge. In this study, we introduce TransRef, a new computational algorithm for accurate transcriptome assembly by redefining a novel graph model, the neo-splicing graph, and then iteratively applying a constrained dynamic programming to reconstruct all the expressed transcripts for each graph. When TransRef is utilized to analyze both real and simulated datasets, its performance is notably better than those of several state-of-the-art assemblers, including StringTie2, Cufflinks and Scallop. In particular, the performance of TransRef is notably strong in identifying novel transcripts and transcripts with low-expression levels, while the other assemblers are less effective. [ABSTRACT FROM AUTHOR]

Published
2021
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40. EML4-ALK fusions drive lung adeno-to-squamous transition through JAK-STAT activation

Author

Qin, Zhen, Yue, Meiting, Tang, Shijie, Wu, Fengying, Sun, Honghua, Li, Yuan, Zhang, Yongchang, Izumi, Hiroki, Huang, Hsinyi, Wang, Wanying, Xue, Yun, Tong, Xinyuan, Mori, Shunta, Taki, Tetsuro, Goto, Koichi, Jin, Yujuan, Li, Fei, Li, Fu-Ming, Gao, Yijun, Fang, Zhaoyuan, Fang, Yisheng, Hu, Liang, Yan, Xiumin, Xu, Guoliang, Chen, Haiquan, Kobayashi, Susumu S., Ventura, Andrea, Wong, Kwok-Kin, Zhu, Xueliang, Chen, Liang, Ren, Shengxiang, Chen, Luo-Nan, and Ji, Hongbin

Abstract

Human lung adenosquamous cell carcinoma (LUAS), containing both adenomatous and squamous pathologies, exhibits strong cancer plasticity. We find that ALK rearrangement is detectable in 5.1–7.5% of human LUAS, and transgenic expression of EML4-ALK drives lung adenocarcinoma (LUAD) formation initially and squamous transition at late stage. We identify club cells as the main cell-of-origin for squamous transition. Through recapitulating lineage transition in organoid system, we identify JAK-STAT signaling, activated by EML4-ALK phase separation, significantly promotes squamous transition. Integrative study with scRNA-seq and immunostaining identify a plastic cell subpopulation in ALK-rearranged human LUAD showing squamous biomarker expression. Moreover, those relapsed ALK-rearranged LUAD show notable upregulation of squamous biomarkers. Consistently, mouse squamous tumors or LUAD with squamous signature display certain resistance to ALK inhibitor, which can be overcome by combined JAK1/2 inhibitor treatment. This study uncovers strong plasticity of ALK-rearranged tumors in orchestrating phenotypic transition and drug resistance and proposes a potentially effective therapeutic strategy.

Published
2024
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41. HMPA: a pioneering framework for the noncanonical peptidome from discovery to functional insights.

Author

Su X, Shi C, Liu F, Tan M, Wang Y, Zhu L, Chen Y, Yu M, Wang X, Liu J, Liu Y, Lin W, Fang Z, Sun Q, Zhou T, and Lin A

Subjects
Humans, Proteome metabolism, Protein Interaction Maps, Deep Learning, Proteomics methods, Peptides metabolism, Peptides genetics, Peptides chemistry, Neoplasms metabolism, Neoplasms genetics, Computational Biology methods
Abstract

Advancements in peptidomics have revealed numerous small open reading frames with coding potential and revealed that some of these micropeptides are closely related to human cancer. However, the systematic analysis and integration from sequence to structure and function remains largely undeveloped. Here, as a solution, we built a workflow for the collection and analysis of proteomic data, transcriptomic data, and clinical outcomes for cancer-associated micropeptides using publicly available datasets from large cohorts. We initially identified 19 586 novel micropeptides by reanalyzing proteomic profile data from 3753 samples across 8 cancer types. Further quantitative analysis of these micropeptides, along with associated clinical data, identified 3065 that were dysregulated in cancer, with 370 of them showing a strong association with prognosis. Moreover, we employed a deep learning framework to construct a micropeptide-protein interaction network for further bioinformatics analysis, revealing that micropeptides are involved in multiple biological processes as bioactive molecules. Taken together, our atlas provides a benchmark for high-throughput prediction and functional exploration of micropeptides, providing new insights into their biological mechanisms in cancer. The HMPA is freely available at http://hmpa.zju.edu.cn., (© The Author(s) 2024. Published by Oxford University Press.)

Published
2024
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