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arXiv 2021年

作者： Nwosu, Lucy Li, Xiangfang Qian, Lijun Kim, Seungchan Dong, Xishuang Center of Computational Systems Biology Department of Electrical and Computer Engineering Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States Center of Excellence in Research and Education for Big Military Data Intelligence CREDIT Center Center of Computational Systems Biology Department of Electrical and Computer Engineering Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States Center of Computational Systems Biology Department of Electrical and Computer Engineering Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200 countries and territories, which has resulted in a great public health concern across the international community. Analysis of X-ray imaging data can play a critical role in timely and accurate screening and fighting against COVID-19. Supervised deep learning has been successfully applied to recognize COVID-19 pathology from X-ray imaging datasets. However, it requires a substantial amount of annotated X-ray images to train models, which is often not applicable to data analysis for emerging events such as COVID-19 outbreak, especially in the early stage of the outbreak. To address this challenge, this paper proposes a two-path semi-supervised deep learning model, ssResNet, based on Residual Neural Network (ResNet) for COVID-19 image classification, where two paths refer to a supervised path and an unsupervised path, respectively. Moreover, we design a weighted supervised loss that assigns higher weight for the minority classes in the training process to resolve the data imbalance. Experimental results on a large-scale of X-ray image dataset COVIDx demonstrate that the proposed model can achieve promising performance even when trained on very few labeled training images. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

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A hybrid FEM-PINN method for time-dependent partial differential equations

arXiv

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arXiv 2024年

作者： Feng, Xiaodong Shangguan, Haojiong Tang, Tao Wan, Xiaoliang Zhou, Tao Division of Science and Technology BNU-HKBU United International College Zhuhai519087 China School of Electrical and Computer Engineering Guangzhou Nanfang College Guangzhou510970 China Department of Mathematics Center for Computation and Technology Louisiana State University Baton Rouge70803 United States Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

In this work, we present a hybrid numerical method for solving evolution partial differential equations (PDEs) by merging the time finite element method with deep neural networks. In contrast to the conventional deep learning-based formulation where the neural network is defined on a spatiotemporal domain, our methodology utilizes finite element basis functions in the time direction where the space-dependent coefficients are defined as the output of a neural network. We then apply the Galerkin or collocation projection in the time direction to obtain a system of PDEs for the space-dependent coefficients which is approximated in the framework of PINN. The advantages of such a hybrid formulation are twofold: statistical errors are avoided for the integral in the time direction, and the neural network’s output can be regarded as a set of reduced spatial basis functions. To further alleviate the difficulties from high dimensionality and low regularity, we have developed an adaptive sampling strategy that refines the training set. More specifically, we use an explicit density model to approximate the distribution induced by the PDE residual and then augment the training set with new time-dependent random samples given by the learned density model. The effectiveness and efficiency of our proposed method have been demonstrated through a series of numerical experiments. © 2024, CC BY.

关键词： Partial differential equations

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MIST-CF: Chemical formula inference from tandem mass spectra

arXiv

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arXiv 2023年

作者： Goldman, Samuel Xin, Jiayi Provenzano, Joules Coley, Connor W. Computational and Systems Biology Massachusetts Institute of Technology CambridgeMA02139 United States Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends on time-intensive, proprietary, and expert-parameterized fragmentation tree construction and scoring. In this work we extend our previous spectrum Transformer methodology into an energy based modeling framework, MIST-CF: Metabolite Inference with Spectrum Transformers for Chemical Formulae, for learning to rank chemical formula and adduct assignments given an unannotated MS/MS spectrum. Importantly, MIST-CF learns in a data dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction. We train and evaluate our model on a large open-access database, showing an absolute improvement of 10% top 1 accuracy over other neural network architectures. We further validate our approach on the CASMI2022 challenge dataset, achieving nearly equivalent performance to the winning entry within the positive mode category without any manual curation or post-processing of our results. These results demonstrate an exciting strategy to more powerfully leverage MS2 fragment peaks for predicting MS1 precursor chemical formula with data driven learning. © 2023, CC BY-NC-SA.

关键词： Mass spectrometry

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High-field/high-frequency electron spin resonances of Fe-doped β−Ga2O3 by terahertz generalized ellipsometry: Monoclinic symmetry effects

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Physical Review B 2024年第21期109卷 214106-214106页

作者： Steffen Richter Sean Knight Oscar Bulancea-Lindvall Sai Mu Philipp Kühne Megan Stokey Alexander Ruder Viktor Rindert Viktor Ivády Igor A. Abrikosov Chris G. Van de Walle Mathias Schubert Vanya Darakchieva Competence Center for III-Nitride Technology C3NiT-Janzén Solid State Physics and NanoLund Competence Center for III-Nitride Technology C3NiT-Janzén Department of Physics Chemistry and Biology (IFM) Department of Physics Chemistry and Biology (IFM) Materials Department Department of Physics and Astronomy Department of Electrical and Computer Engineering Department of Physics of Complex Systems MTA–ELTE Lendület “Momentum” NewQubit Research Group Pázmány Péter Sétány 1/A 1117 Budapest Hungary

We demonstrate detection and measurement of electron paramagnetic spin resonances (EPR) of iron defects in β−Ga2O3 utilizing generalized ellipsometry at frequencies between 110 and 170 GHz. The experiments are performed on an Fe-doped single crystal in a free-beam configuration in reflection at 45∘ and magnetic fields between 3 and 7 T. In contrast with low-field, low-frequency EPR measurements, we observe all five transitions of the s=5/2 high-spin state Fe3+ simultaneously. We confirm that ferric Fe3+ is predominantly found at octahedrally coordinated Ga sites. We obtain the full set of fourth-order monoclinic zero-field splitting parameters for both octahedrally and tetrahedrally coordinated sites by employing measurements at multiple sample azimuth rotations. The capability of high-field EPR allows us to demonstrate that simplified second-order orthorhombic spin Hamiltonians are insufficient, and fourth-order terms as well as consideration of the monoclinic symmetry are needed. These findings are supported by computational approaches based on density-functional theory for second-order and on ligand-field theory for fourth-order parameters of the spin Hamiltonian. Terahertz ellipsometry is a way to measure spin resonances in a cavity-free setup. Its possibility of varying the probe frequency arbitrarily without otherwise changing the experimental setup offers unique means of truly disentangling different components of highly anisotropic spin Hamiltonians.

关键词： Defects Dopants Energy materials Paramagnets Wide band gap systems Spin Electron paramagnetic resonance Terahertz spectroscopy Terahertz techniques

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Out-of-Equilibrium Fluctuations Drive Correlations Between Enzyme and Metabolic Product Levels

Out-of-Equilibrium Fluctuations Drive Correlations Between E...

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IEEE Conference on Decision and Control

作者： A. Borri P. Palumbo A. Singh Istituto di Analisi dei Sistemi e Informatica “A. Ruberti” Italian National Research Council (IASI-CNR) Roma Italy Department of Biotechnologies and Biosciences University of Milano-Bicocca Milan Italy Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology University of Delaware Newark DE USA

Enzyme-driven catalysis of a substrate into a product forms the fundamental backbone of cellular metabolic pathways. In the deterministic formulation of such a reaction scheme, the equilibrium level of the metabolic product is independent of the steady-state enzyme, so that any perturbation in enzyme levels causes a transient change in metabolic product levels that perfectly adapts to the original enzyme-independent steady state. In this work, we consider a stochastic formulation of the problem, where enzyme levels constantly fluctuate due to the inherently noisy gene expression process as well as to the extrinsic noise in substrate availability. Our results show that such out-of-equilibrium fluctuations can result in positive (or negative) enzyme-product and substrate-product correlations, whose behavior qualitatively and quantitatively changes in different scenarios characterized by perturbations of nominal parameters and variable noise levels.

关键词：

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Assessing hemispheric blood flow differences in patients undergoing extracorporeal membrane oxygenation using diffuse correlation spectroscopy

Assessing hemispheric blood flow differences in patients und...

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Optics and the Brain, BRAIN 2022

作者： Dar, Irfaan A. Johnson, Thomas W. Xu, Yama Y. Santiago, Esmeralda Selioutski, Olga Marinescu, Mark A. Maddox, Ross K. Wu, Tong Tong Khan, Imad R. Choe, Regine Department of Biomedical Engineering University of Rochester RochesterNY United States Department of Neurology University of Rochester Medical Center RochesterNY United States School of Arts and Sciences University of Rochester RochesterNY United States Department of Medicine University of Rochester RochesterNY United States Department of Neuroscience University of Rochester Medical Center RochesterNY United States Department of Biostatistics and Computational Biology University of Rochester RochesterNY United States Department of Electrical and Computer Engineering University of Rochester RochesterNY United States

We monitored patients under extracorporeal membrane oxygenation using diffuse correlation spectroscopy and found hemispheric blood flow differences between non-comatose and comatose patients. © 2022 The Author(s).

ISBN: (纸本)9781957171036

关键词： Blood

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Context-aware query design combines knowledge and data for efficient reading and reasoning 20

Context-aware query design combines knowledge and data for e...

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20th Workshop on Biomedical Language Processing, BioNLP 2021

作者： Holtzapple, Emilee Cochran, Brent Miskov-Zivanov, Natasa Dept. of Computational and Systems Biology University of Pittsburgh PittsburghPA United States Dept. of Developmental Molecular and Chemical Biology Tufts University School of Medicine BostonMA United States Dept. of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Dept. of Bioengineering University of Pittsburgh PittsburghPA United States

ISBN: (纸本)9781954085404

The amount of biomedical literature has vastly increased over the past few decades. As a result, the sheer quantity of accessible information is overwhelming, and complicates manual information retrieval. Automated methods seek to speed up information retrieval from biomedical literature. However, such automated methods are still too time-intensive to survey all existing biomedical literature. We present a methodology for automatically generating literature queries that select relevant papers based on biological data. By using differentially expressed genes to inform our literature searches, we focus information extraction on mechanistic signaling details that are crucial for the disease or context of interest. © 2021 Association for computational Linguistics

关键词： Information retrieval

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LinearSankoff: Linear-time Simultaneous Folding and Alignment of RNA Homologs

arXiv

引用

arXiv 2023年

作者： Li, Sizhen Dai, Ning Zhang, He Malik, Apoorv Mathews, David H. Huang, Liang School of Electrical Engineering & Computer Science Oregon State University CorvallisOR97330 United States Baidu Research USA SunnyvaleCA94089 United States Dept. of Biochemistry & Biophysics University of Rochester Medical Center RochesterNY14642 United States Center for RNA Biology University of Rochester Medical Center RochesterNY14642 United States Dept. of Biostatistics & Computational Biology University of Rochester Medical Center RochesterNY14642 United States

The classical Sankoff algorithm for the simultaneous folding and alignment of homologous RNA sequences is highly influential, but it suffers from two major limitations in efficiency and modeling power. First, it takes O(n6) for two sequences where n is the average sequence length. Most implementations and variations reduce the runtime to O(n3) by restricting the alignment search space, but this is still too slow for long sequences such as full-length viral genomes. On the other hand, the Sankoff algorithm and all its existing implementations use a rather simplistic alignment model, which can result in poor alignment accuracy. To address these problems, we propose LinearSankoff, which seamlessly integrates the original Sankoff algorithm with a powerful Hidden Markov Model-based alignment module. This extension substantially improves alignment quality, which in turn benefits secondary structure prediction quality, confirmed over a diverse set of RNA families. LinearSankoff also applies beam search heuristics and the A* algorithm to achieve that runtime scales linearly with sequence length. LinearSankoff is the first linear-time algorithm for simultaneous folding and alignment, and the first such algorithm to scale to coronavirus genomes (n 30, 000nt). It only takes 10 minutes for a pair of SARS-CoV-2 and SARS-related genomes, and outperforms previous work at identifying crucial conserved structures between the two genomes. Copyright © 2023, The Authors. All rights reserved.

关键词： RNA

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Modern deep learning in bioinformatics

引用

Journal of Molecular Cell biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

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MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTERING 12

MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTER...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Maximum principle

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