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IRIS:A method for predicting in vivo RNA secondary structures using PARIS data

Quantitative Biology 2020年第4期8卷 369-381页

作者： Jianyu Zhou Pan Li Wanwen Zeng Wenxiu Ma Zhipeng Lu Rui Jiang Qiangfeng Cliff Zhang Tao Jiang Bioinformatics Division BNRISTTsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural BiologyCenter for Synthetic and Systems BiologyTsinghua-Peking Center for Life SciencesSchool of Life SciencesTsinghua UniversityBeijing 100084China College of Software Nankai UniversityTianjin 300071China Department of Statistics University of CaliforniaRiversideCA 92521USA Department of Pharmacology and Pharmaceutical Sciences University of Southern CaliforniaCA 90089USA Department of Automation Tsinghua UniversityBeijing 100084China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA

Background:RNA secondary structures play a pivotal role in posttranscriptional regulation and the functions of non-coding RNAs,yet in vivo RNA secondary structures remain ***(Psoralen Analysis of RNA Interactions and Structures)is a recently developed high-throughput sequencing-based approach that enables direct capture of RNA duplex structures in ***,the existence of incompatible,fuzzy pairing information obstructs the integration of PARIS data with the existing tools for reconstructing RNA secondary structure models at the single-base ***:We introduce IRIS,a method for predicting RNA secondary structure ensembles based on PARIS *** generates a large set of candidate RNA secondary structure models under the guidance of redistributed PARIS reads and then uses a Bayesian model to identify the optimal ensemble,according to both thermodynamic principles and PARIS ***:The predicted RNA structure ensembles by IRIS have been verified based on evolutionary conservation information and consistency with other experimental RNA structural *** is implemented in Python and freely available at http://***:IRIS capitalizes upon PARIS data to improve the prediction of in vivo RNA secondary structure *** expect that IRIS will enhance the application of the PARIS technology and shed more insight on in vivo RNA secondary structures.

关键词： RNA secondary structure PARIS data in vivo structure ensembles incompatible reads

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Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

arXiv

引用

arXiv 2023年

作者： Holste, Gregory Zhou, Yiliang Wang, Song Jaiswal, Ajay Lin, Mingquan Zhuge, Sherry Yang, Yuzhe Kim, Dongkyun Nguyen-Mau, Trong-Hieu Tran, Minh-Triet Jeong, Jaehyup Park, Wongi Ryu, Jongbin Hong, Feng Verma, Arsh Yamagishi, Yosuke Kim, Changhyun Seo, Hyeryeong Kang, Myungjoo Celi, Leo Anthony Lu, Zhiyong Summers, Ronald M. Shih, George Wang, Zhangyang Peng, Yifan Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY10065 United States School of Information Systems Carnegie Mellon University PittsburghPA15213 United States Department of Electrical Engineering and Computer Science Massachussetts Institute of Technology CambridgeMA02139 United States School of Computer Science Carnegie Mellon University PittsburghPA15213 United States University of Science VNU-HCM Ho Chi Minh City70000 Viet Nam KT Research & Development Center KT Corporation Seoul06763 Korea Republic of Department of Software and Computer Engineering Ajou University Suwon16499 Korea Republic of Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China Wadhwani Institute for Artificial Intelligence Mumbai400079 India Division of Radiology and Biomedical Engineering Graduate School of Medicine The University of Tokyo Tokyo113-0033 Japan BioMedical AI Team AIX Future R&D Center SK Telecom Seoul04539 Korea Republic of Seoul National University Seoul02504 Korea Republic of Department of Mathematical Sciences Seoul National University Seoul02504 Korea Republic of Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA02139 United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA02215 United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States National Center for Biotechnology Information National Library of Medicine BethesdaMD20894 United States Clinical Center National Institutes of Health BethesdaMD20892 United States Department of Radiology Weill Cornell Medicine New YorkNY10065 United States

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" – there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification. © 2023, CC BY-NC-SA.

关键词： computer aided diagnosis

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A Hybrid TLBO-Particle Filter Algorithm Applied to Remaining Useful Life Prediction in the Presence of Multiple Degradation Factors

A Hybrid TLBO-Particle Filter Algorithm Applied to Remaining...

引用

Congress on Evolutionary Computation

作者： Leonardo R. Rodrigues Daniel B. P. Coelho João Paulo P. Gomes Electronics Engineering Division Aeronautics Institute of Technology São José dos Campos Brazil Performance Software Department Embraer S.A. Belo Horizonte Brazil Department of Computer Science Federal University of Ceará Fortaleza Brazil

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

One of the end goals of a Prognostic and Health Monitoring (PHM) algorithm is to provide accurate Remaining Useful Life (RUL) predictions for the monitored component or system. Most of the PHM algorithms found in the literature are based on the assumption that the degradation process is governed by only one degradation factor. However, some components and systems may be subject to multiple degradation factors. In this paper, we propose a hybrid algorithm that incorporates a Teaching-Learning Based optimization (TLBO) step into a Particle Filter (PF) framework. PF is an algorithm that can handle multiple degradation factors. However, it has some drawbacks such as sample degeneracy and sample impoverishment. The hybrid TLBO-PF algorithm proposed in this paper improves the performance of the standard PF algorithm by reducing the effects of sample degeneracy and sample impoverishment. A case study is presented to evaluate the performance of the proposed algorithm for estimating the degradation factors and predicting the RUL of a Lithium-ion battery, which is affected by two degradation factors. The results show that the proposed algorithm presented a better performance for both the tasks (degradation factor estimation and RUL prediction) when compared with the standard Particle Filter algorithm.

关键词： Degradation Prediction algorithms Prognostics and health management Monitoring Optimization Standards Particle filters

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Harmonia: A high throughput B+tree for GPUs 19

Harmonia: A high throughput B+tree for GPUs

引用

24th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2019

作者： Yan, Zhaofeng Lin, Yuzhe Peng, Lu Zhang, Weihua Software School Fudan University China Shanghai Key Laboratory of Data Science Fudan University China Shanghai Institute of Intelligent Electronics and Systems Shanghai China Division of Electrical and Computer Engineering Louisiana State University United States

ISBN: (纸本)9781450362252

B+tree is one of the most important data structures and has been widely used in different fields. With the increase of concurrent queries and data-scale in storage, designing an eficient B+tree structure has become critical. Due to abundant computation resources, GPUs provide potential opportunities to achieve high query throughput for B+tree. However, prior methods cannot achieve satisfactory performance results due to low resource utilization and poor memory performance. In this paper, we first identify the gaps between B+tree and GPUs. Concurrent B+tree queries involve many global memory accesses and different divergences, which mismatch with GPU features. Based on this observation, we propose Harmonia, a novel B+tree structure to bridge the gap. In Harmonia, a B+tree structure is divided into a key region and a child region. The key region stores the nodes with its keys in a breadth-first order. The child region is organized as a prefix-sum array, which only stores each node's first child index in the key region. Since the prefix-sum child region is small and the children's index can be retrieved through index computations, most of it can be stored in on-chip caches, which can achieve good cache locality. To make it more eficient, Harmonia also includes two optimizations: partially-sorted aggregation and narrowed thread-group traversal, which can mitigate memory and warp divergence and improve resource utilization. Evaluations on a TITAN V GPU show that Harmonia can achieve up to 3.6 billion queries per second, which is about 3.4X faster than that of HB+Tree [39], a recent state-of-the-art GPU solution. © 2019 Association for Computing Machinery.

关键词： Trees (mathematics)

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Deep3BPP: Identification of blood-brain barrier penetrating peptides using word embedding feature extraction method and CNN-LSTM

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Rahman, Md. Ashikur Ali, Md Mamun Ahmed, Kawsar Mahmud, Imran Bui, Francis M. Chen, Li Moni, Mohammad Ali Daffodil International University Daffodil Smart City Department of Software Engineering Birulia Dhaka1216 Bangladesh University of Saskatchewan Division of Biomedical Engineering SaskatoonSKS7N 5A9 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSKS7N 5A9 Canada Health Informatics Research Laboratory Department of Computer Science and Engineering Birulia Savar Dhaka1216 Bangladesh Mawlana Bhashani Science and Technology University Group of Bio-photomatiχ Information and Communication Technology Santosh Tangail1902 Bangladesh Charles Sturt University Centre for AI & Digital Health Technology Artificial Intelligence & Cyber Futures Institute BathurstNSW2795 Australia

To prevent different chemicals from entering the brain, the blood-brain barrier penetrating peptide (3BPP) acts as a vital barrier between the bloodstream and the central nervous system (CNS). This barrier significantly hinders the treatment of neurological and CNS disorders. 3BPP can get beyond this barrier, making it easier to enter the brain and essential for treating CNS and neurological diseases and disorders. Computational techniques are being explored because traditional laboratory tests for 3BPP identification are costly and time-consuming. In this work, we introduced a novel technique for 3BPP prediction with a hybrid deep learning model. Our proposed model, Deep3BPP, leverages the LSA, a word embedding method for peptide sequence extraction, and integrates CNN with LSTM (CNN-LSTM) for the final prediction model. Deep3BPP performance metrics show a remarkable accuracy of 97.42%, a Kappa value of 0.9257, and an MCC of 0.9362. These findings indicate a more efficient and cost-effective method of identifying 3BPP, which has important implications for researchers in the pharmaceutical and medical industries. Thus, this work offers insightful information that can advance both scientific research and the well-being of people overall. © 2020 IEEE.

关键词： Convolutional neural networks

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GWSkyNet-Multi II: an updated deep learning model for rapid classification of gravitational-wave events

arXiv

引用

arXiv 2025年

作者： Raza, Nayyer Chan, Man Leong Haggard, Daryl Mahabal, Ashish McIver, Jess Durand, Audrey Larouche, Alexandre Moazen, Hadi Department of Physics McGill University 3600 rue University MontréalQCH3A2T8 Canada Trottier Space Institute at McGill 3550 rue University MontréalQCH3A2A7 Canada Department of Physics and Astronomy University of British Columbia VancouverBCV6T1Z1 Canada Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Department of Computer Science and Software Engineering Université Laval QuébecQCG1V0A6 Canada Canada CIFAR AI Chair Mila MontréalQCH2S3H1 Canada

Multi-messenger observations of gravitational waves and electromagnetic emission from compact object mergers offer unique insights into the structure of neutron stars, the formation of heavy elements, and the expansion rate of the Universe. With the LIGO-Virgo-KAGRA (LVK) gravitational-wave detectors currently in their fourth observing run (O4), it is an exciting time for detecting these mergers. However, assessing whether to follow up a candidate gravitational-wave event given limited telescope time and resources is challenging;the candidate can be a false alert due to detector glitches, or may not have any detectable electromagnetic counterpart even if it is real. GWSkyNet-Multi is a deep learning model developed to facilitate follow-up decisions by providing real-time classification of candidate events, using localization information released in LVK rapid public alerts. Here we introduce GWSkyNet-Multi II, an updated model targeted towards providing more robust and informative predictions during O4 and beyond. Specifically, the model now provides normalized probability scores and associated uncertainties for each of the four corresponding source categories released by the LVK: glitch, binary black hole, neutron star-black hole, and binary neutron star. Informed by explainability studies of the original model, the updated model architecture is also significantly simplified, including replacing input images with intuitive summary values, making it more interpretable. For significant O4 event alerts issued between May 2023 and December 2024, GWSkyNet-Multi II produces a prediction that is consistent with the updated LVK classification for 93% of events. The updated model can be used by the community to help make time-critical follow-up decisions. © 2025, CC BY.

关键词： Neutrons

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Artificial intelligence advances for de novo molecular structure modeling in cryo-EM

arXiv

引用

arXiv 2021年

作者： Si, Dong Nakamura, Andrew Tang, Runbang Guan, Haowen Hou, Jie Firozi, Ammaar Cao, Renzhi Hippe, Kyle Zhao, Minglei Division of Computing and Software Systems University of Washington Bothell BothellWA98011 United States Molecular Engineering and Sciences Institute University of Washington Seattle SeattleWA98105 United States Applied and Computational Math Sciences University of Washington Seattle SeattleWA98195 United States Department of Computer Science Saint Louis University Saint. LouisMO63103 United States Department of Computer Science Pacific Lutheran University TacomaWA98447 United States Department of Biochemistry and Molecular Biology University of Chicago ChicagoIL60637 United States

Cryo-electron microscopy (cryo-EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been drastically improved to generate high-resolution three-dimensional (3D) maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo-EM model building approach is template-based homology modeling. Manual de novo modeling is very time-consuming when no template model is found in the database. In recent years, de novo cryo-EM modeling using machine learning (ML) and deep learning (DL) has ranked among the top-performing methods in macromolecular structure modeling. Deep-learning-based de novo cryo-EM modeling is an important application of artificial intelligence, with impressive results and great potential for the next generation of molecular biomedicine. Accordingly, we systematically review the representative ML/DL-based de novo cryo-EM modeling methods. And their significances are discussed from both practical and methodological viewpoints. We also briefly describe the background of cryo-EM data processing workflow. Overall, this review provides an introductory guide to modern research on artificial intelligence (AI) for de novo molecular structure modeling and future directions in this emerging field. © 2021, CC BY-NC-ND.

关键词： Electron microscopes

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Noise entangled GAN for low-dose CT simulation

arXiv

引用

arXiv 2021年

作者： Niu, Chuang Wang, Ge Yan, Pingkun Hahn, Juergen Lai, Youfang Jia, Xun Krishna, Arjun Mueller, Klaus Badal, Andreu Myers, Kyle J. Zeng, Rongping Department of Biomedical Engineering Center for Biotechnology & Interdisciplinary Studies Rensselaer Polytechnic Institute TroyNY United States Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States Computer Science Department Stony Brook University Stony BrookNY United States Division of Imaging Diagnostics and Software Reliability OSEL CDRH U.S. Food and Drug Administration Silver SpringMD United States

We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image. First, we present two schemes to generate a clean CT image and a noise image from the high-dose CT image. Then, given these generated images, an NE-GAN is proposed to simulate different levels of low-dose CT images, where the level of generated noise can be continuously controlled by a noise factor. NE-GAN consists of a generator and a set of discriminators, and the number of discriminators is determined by the number of noise levels during training. Compared with the traditional methods based on the projection data that are usually unavailable in real applications, NE-GAN can directly learn from the real and/or simulated CT images and may create low-dose CT images quickly without the need of raw data or other proprietary CT scanner information. The experimental results show that the proposed method has the potential to simulate the realistic low-dose CT images. © 2021, CC BY.

关键词： computerized tomography

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CovidCare: Transferring knowledge from existing EMR to emerging epidemic for interpretable prognosis

arXiv

引用

arXiv 2020年

作者： Ma, Liantao Ma, Xinyu Gao, Junyi Zhang, Chaohe Yu, Zhihao Jiao, Xianfeng Ruan, Wenjie Wang, Yasha Tang, Wen Wang, Jiangtao 1Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China 2National Engineering Research Center of Software Engineering Peking University Beijing China 3School of Electronics Engineering and Computer Science Peking University Beijing China 4School of Computing and Communications Lancaster University UK 5Division of Nephrology Peking University Third Hospital Beijing China

Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide. Many patients suffer from systemic life-threatening problems and need to be carefully monitored in ICUs. Thus the intelligent prognosis is in an urgent need to assist physicians to take an early intervention, prevent the adverse outcome, and optimize the medical resource allocation. However, in the early stage of the epidemic outbreak, the data available for analysis is limited due to the lack of effective diagnostic mechanisms, rarity of the cases, and privacy concerns. In this paper, we propose a deep-learning-based approach, CovidCare, which leverages the existing electronic medical records to enhance the prognosis for inpatients with emerging infectious diseases. It learns to embed the COVID-19-related medical features based on massive existing EMR data via transfer learning. The transferred parameters are further trained to imitate the teacher model’s representation behavior based on knowledge distillation, which embeds the health status more comprehensively in the source dataset. We conduct the length of stay prediction experiments for patients on a real-world COVID-19 dataset. The experiment results indicate that our proposed model consistently outperforms the comparative baseline methods. CovidCare also reveals that, 1) hs-cTnI, hs-CRP and Platelet Counts are the most fatal biomarkers, whose abnormal values usually indicate emergency adverse outcome. 2) Normal values of γ-GT, AP and eGFR indicate the overall improvement of health. The medical findings extracted by CovidCare are empirically confirmed by human experts and medical literatures. Copyright © 2020, The Authors. All rights reserved.

关键词： Epidemiology

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Endurable SSD-Based Read Cache for Improving the Performance of Selective Restore from Deduplication Systems

引用

Journal of computer science & Technology 2018年第1期33卷 58-78页

作者： Jian Liu Yun-Peng Chai Xiao Qin Yao-Hong Liu Division of Computer Science and Engineering Louisiana State University Baton Rouge LA 70803 U.S.A. Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Shelby Center for Engineering Technology Department of Computer Science and Software Engineering Samuel Ginn College of Engineering Auburn University Auburn AL 368~9-53~7 U.S.A.

Deduplication has been commonly used in both enterprise storage systems and cloud storage. To overcome the performance challenge for the selective restore operations of deduplication systems, solid-state-drive-based （i.e., SSD-based） re^d cache cm, be deployed for speeding up by caching popular restore contents dynamically. Unfortunately, frequent data updates induced by classical cache schemes （e.g., LRU and LFU） significantly shorten SSDs＇ lifetime while slowing down I/O processes in SSDs. To address this problem, we propose a new solution -- LOP-Cache to greatly improve tile write durability of SSDs as well as I/O performance by enlarging the proportion of long-term popular （LOP） data among data written into SSD-based cache. LOP-Cache keeps LOP data in the SSD cache for a long time period to decrease the number of cache replacements. Furthermore, it prevents unpopular or unnecessary data in deduplication containers from being written into the SSD cache. We implemented LOP-Cache in a prototype deduplication system to evaluate its pertbrmance. Our experimental results indicate that LOP-Cache shortens the latency of selective restore by an average of 37.3% at the cost of a small SSD-based cache with only 5.56% capacity of the deduplicated data. Importantly, LOP-Cache improves SSDs＇ lifetime by a factor of 9.77. The evidence shows that LOP-Cache offers a cost-efficient SSD-based read cache solution to boost performance of selective restore for deduplication systems.

关键词： data deduplication solid state drive （SSD） flash cache endurance

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