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Tunable structure priors for Bayesian rule learning for knowledge integrated biomarker discovery

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World Journal of Clinical Oncology 2018年第5期9卷 98-109页

作者： Jeya Balaji Balasubramanian Vanathi Gopalakrishnan Intelligent Systems Program School of Computing and Information University of Pittsburgh Department of Biomedical Informatics School of Medicine University of Pittsburgh

AIM To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in *** Bayesian rule learning(BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks(BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRL_p. The structure prior has a λ hyperparameter that allows the user to tune the degree of incorporation of the prior knowledge in the model learning process. We studied the effect of λ on model learning using a simulated dataset and a real-world lung cancer prognostic biomarker dataset, by measuring the degree of incorporation of our specified prior knowledge. We also monitored its effect on the model predictive performance. Finally, we compared BRL_p to other stateof-the-art classifiers commonly used in *** We evaluated the degree of incorporation of prior knowledge into BRL_p, with simulated data by measuring the Graph Edit Distance between the true datagenerating model and the model learned by BRL_p. We specified the true model using informative structurepriors. We observed that by increasing the value of λ we were able to increase the influence of the specified structure priors on model learning. A large value of λ of BRL_p caused it to return the true model. This also led to a gain in predictive performance measured by area under the receiver operator characteristic curve(AUC). We then obtained a publicly available real-world lung cancer prognostic biomarker dataset and specified a known biomarker from literature [the epidermal growth factor receptor(EGFR) gene]. We again observed that larger values of λ led to an increased

关键词： Supervised machine learning Rule-based models Bayesian methods Background knowledge Informative priors Biomarker discovery

Mechanisms Underlying Reshuffling of Visual Responses by Optogenetic Stimulation in Mice and Monkeys

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SSRN

SSRN 2022年

作者： Sanzeni, Alessandro Palmigiano, Agostina Nguyen, Tuan Huu Luo, Junxiang Nassi, Jonathan J. Reynolds, John H. Histed, Mark H. Miller, Ken D. Brunel, Nicolas Department of Computing Sciences Bocconi University Milan Italy Center for Theoretical Neuroscience Mortimer B Zuckerman Mind Brain Behavior Institute Columbia University New YorkNY United States Department of Neurobiology Duke University DurhamNC United States Department of Physics Columbia University New YorkNY United States Systems Neurobiology Laboratory The Salk Institute for Biological Studies La Jolla CA United States National Institute of Mental Health Intramural Program NIH BethesdaMD United States Department of Neuroscience Swartz Program in Theoretical Neuroscience Kavli Institute for Brain Science College of Physicians and Surgeons Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York CityNY United States Department of Physics Duke University DurhamNC United States

The ability to optogenetically perturb neural circuits opens an unprecedented window into mechanisms governing circuit function. We analyzed and theoretically modeled neuronal responses to visual and optogenetic inputs in mouse and monkey V1. In both species, optogenetic stimulation of excitatory neurons strongly modulated the activity of single neurons, yet had weak or no effects on the distribution of firing rates across the population. Thus, the optogenetic inputs reshuffled firing rates across the network. Key statistics of mouse and monkey responses lay on a continuum, with mice/monkeys occupying the low/high rate regions, respectively. We show that neuronal reshuffling emerges generically in randomly connected excitatory/inhibitory networks, provided the coupling strength (combination of recurrent coupling and external input) is sufficient that powerful inhibitory feedback cancels the mean optogenetic input. A more realistic model, distinguishing tuned visual vs. untuned optogenetic input in a structured network, reduces the coupling strength needed to explain reshuffling. © 2022, The Authors. All rights reserved.

关键词： Electrophysiology

Malignant peritoneal mesothelioma interactome with 417 novel protein-protein interactions

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BJC reports 2024年第1期2卷 42页

作者： Kalyani B Karunakaran Madhavi K Ganapathiraju Supercomputer Education and Research Centre Indian Institute of Science Bengaluru 560012 India. kalyanithepebble@***. Department of Biomedical Informatics School of Medicine and Intelligent Systems Program School of Computing and Information University of Pittsburgh 5607 Baum Blvd 5th Floor Pittsburgh PA 15206 USA. madhavi@pitt.edu. Carnegie Mellon University in Qatar Doha Qatar. madhavi@pitt.edu.

BACKGROUND:Malignant peritoneal mesothelioma (MPeM) is an aggressive cancer affecting the abdominal peritoneal lining and intra-abdominal organs, with a median survival of ~2.5 years. METHODS:We constructed the protein interactome of 59 MPeM-associated genes with previously known protein-protein interactions (PPIs) as well as novel PPIs predicted using our previously developed HiPPIP computational model and analysed it for transcriptomic and functional associations and for repurposable drugs. RESULTS:The MPeM interactome had over 400 computationally predicted PPIs and 4700 known PPIs. Transcriptomic evidence validated 75.6% of the genes in the interactome and 65% of the novel interactors. Some genes had tissue-specific expression in extramedullary hematopoietic sites and the expression of some genes could be correlated with unfavourable prognoses in various cancers. 39 out of 152 drugs that target the proteins in the interactome were identified as potentially repurposable for MPeM, with 29 having evidence from prior clinical trials, animal models or cell lines for effectiveness against peritoneal and pleural mesothelioma and primary peritoneal cancer. Functional modules related to chromosomal segregation, transcriptional dysregulation, IL-6 production and hematopoiesis were identified from the interactome. The MPeM interactome overlapped significantly with the malignant pleural mesothelioma interactome, revealing shared molecular pathways. CONCLUSIONS:Our findings demonstrate the utility of the interactome in uncovering biological associations and in generating clinically translatable results.

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Mixing-specific data augmentation techniques for improved blind violin/piano source separation

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arXiv 2020年

作者： Chiu, Ching-Yu Hsiao, Wen-Yi Yeh, Yin-Cheng Yang, Yi-Hsuan Su, Alvin Wen-Yu Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan Dept. CSIE National Cheng Kung University Taiwan

Blind music source separation has been a popular and active subject of research in both the music information retrieval and signal processing communities. To counter the lack of available multi-track data for supervised model training, a data augmentation method that creates artificial mixtures by combining tracks from different songs has been shown useful in recent works. Following this light, we examine further in this paper extended data augmentation methods that consider more sophisticated mixing settings employed in the modern music production routine, the relationship between the tracks to be combined, and factors of silence. As a case study, we consider the separation of violin and piano tracks in a violin piano ensemble, evaluating the performance in terms of common metrics, namely SDR, SIR, and SAR. In addition to examining the effectiveness of these new data augmentation methods, we also study the influence of the amount of training data. Our evaluation shows that the proposed mixing-specific data augmentation methods can help improve the performance of a deep learning-based model for source separation, especially in the case of small training data. Copyright © 2020, The Authors. All rights reserved.

关键词： Mixing

PM2.5-GNN: A domain knowledge enhanced graph neural network for PM2.5 forecasting

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arXiv 2020年

作者： Wang, Shuo Li, Yanran Zhang, Jiang Meng, Qingye Meng, Lingwei Gao, Fei School of Systems Science Beijing Normal University Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong ColorfulClouds Pacific Technology Co. Ltd. Beijing China Atmospheric and Oceanic Sciences Program Princeton University PrincetonNJ United States

When predicting PM2.5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period. In this paper, we identify a set of critical domain knowledge for PM2.5 forecasting and develop a novel graph based model, PM2.5-GNN, being capable of capturing long-term dependencies. On a real-world dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in PM2.5 process. The proposed PM2.5-GNN has also been deployed online to provide free forecasting service. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

Biolink Model: A Universal Schema for Knowledge Graphs in Clinical, Biomedical, and Translational Science

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arXiv 2022年

作者： Unni, Deepak R. Moxon, Sierra A.T. Bada, Michael Brush, Matthew Bruskiewich, Richard Clemons, Paul Dancik, Vlado Dumontier, Michel Fecho, Karamarie Glusman, Gustavo Hadlock, Jennifer J. Harris, Nomi L. Joshi, Arpita Putman, Tim Qin, Guangrong Ramsey, Stephen A. Shefchek, Kent A. Solbrig, Harold Soman, Karthik Thessen, Anne T. Haendel, Melissa A. Bizon, Chris Mungall, Christopher J. Genome Biology Unit European Molecular Biology Laboratory Heidelberg Germany Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA United States Center for Health AI University of Colorado Anschutz Medical Campus AuroraCO United States Star Informatics SookeBC Canada Chemical Biology and Therapeutics Science Program Broad Institute CambridgeMA United States Institute of Data Science Maastricht University Maastricht Netherlands Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel HillNC United States Institute for Systems Biology SeattleWA United States Department of Biomedical Sciences Oregon State University CorvallisOR United States Johns Hopkins University BaltimoreMD United States Department of Neurology University of California San Francisco San FranciscoCA United States

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness between core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these ‘knowledge graphs’ (KGs) has remained difficult. Data set heterogeneity and complexity;the proliferation of ad hoc data formats;poor compliance with guidelines on findability, accessibility, interoperability, and reusability;and, in particular, the lack of a universally-accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates), representing biomedical entities such as gene, disease, chemical, anatomical structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science. © 2022, CC0.

关键词： Reusability

How the world’s collective attention is being paid to a pandemic: COVID-19 related 1-gram time series for 24 languages on Twitter

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arXiv 2020年

作者： Alshaabi, Thayer Arnold, Michael V. Minot, Joshua R. Adams, Jane Lydia Dewhurst, David Rushing Reagan, Andrew J. Muhamad, Roby Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 MassMutual Data Science AmherstMA01002 International Class Program Faculty of Social and Political Sciences University of Indonesia Jakarta Indonesia Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States

In confronting the global spread of the coronavirus disease COVID-19 pandemic we must have coordinated medical, operational, and political responses. In all efforts, data is crucial. Fundamentally, and in the possible absence of a vaccine for 12 to 18 months, we need universal, well-documented testing for both the presence of the disease as well as confirmed recovery through serological tests for antibodies, and we need to track major socioeconomic indices. But we also need auxiliary data of all kinds, including data related to how populations are talking about the unfolding pandemic through news and stories. To in part help on the social media side, we curate a set of 1000 day-scale time series of 1-grams across 24 languages on Twitter that are most ‘important’ for March 2020 with respect to March 2019. We determine importance through our allotaxonometric instrument, rank-turbulence divergence. We make some basic observations about some of the time series, including a comparison to numbers of confirmed deaths due to COVID-19 over time. We broadly observe across all languages a peak for the language-specific word for ‘virus’ in January followed by a decline through February and a recent surge through March. The world’s collective attention dropped away while the virus spread out from China. We host the time series on Gitlab, updating them on a daily basis while relevant. Our main intent is for other researchers to use these time series to enhance whatever analyses that may be of use during the pandemic as well as for retrospective investigations. Copyright © 2020, The Authors. All rights reserved.

关键词： Time series

Shifting and expanding ranges of a sub-Arctic caribou herd and associated changes in vegetation

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Ecological Applications 2025年第4期35卷

作者： Kathleen M. Orndahl Torsten W. Bentzen Logan T. Berner Libby P. W. Ehlers Mark Hebblewhite Jim D. Herriges Kyle Joly Matthew J. Macander Eric C. Palm Michael J. Suitor Scott J. Goetz School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff Arizona USA Alaska Department of Fish & Game Fairbanks Alaska USA W.A. Franke College of Forestry and Conservation Wildlife Biology Program University of Montana Missoula Montana USA Bureau of Land Management Fairbanks Alaska USA National Park Service Yukon-Charley Rivers National Preserve Fairbanks Alaska USA ABR Inc.—Environmental Research & Services Fairbanks Alaska USA Department of Environment Fish and Wildlife Whitehorse Yukon Canada

Rapid climate warming has contributed to significant changes in Arctic and boreal vegetation over the past half century. Changes in vegetation can impact wildlife by altering habitat and forage availability, which can affect behavior and range use. However, animals can also influence vegetation through foraging and trampling and therefore play an important role in determining ecosystem responses to climate change. As wildlife populations grow, density-dependent processes can prompt range expansion or shifts. One mechanism for this is density-dependent forage reduction, which can contribute to nutritional stress and population declines, and can also alter vegetation change trajectories. We assessed the range characteristics of a migratory caribou ( Rangifer tarandus ) herd in east-central Alaska and west-central Yukon Territory as it grew (1992–2017) then declined (2017–2020). Furthermore, we analyzed the correlation between caribou relative spatial density and vegetation change over this period using remotely sensed models of plant functional type cover. Over this period, caribou population density increased in all seasonal ranges. This was most acute in the calving range where density increased 8-fold, from 1.5 to 12.0 animals km −2 . Concurrent with increasing density, we documented range shifts and expansion across summer, post-calving and winter ranges. In particular, summer range size doubled (12,000 km 2 increase) and overlap with core range (areas with repeated year-round use) was halved. Meanwhile, lichen cover, a key forage item, declined more in areas with high caribou density (2.4% absolute, 22% relative decline in cover) compared to areas where caribou were mostly absent (0.3% absolute, 1.9% relative decline). Conversely, deciduous shrub cover increased more in high caribou density areas. However, increases were dominated by less palatable shrubs whereas more palatable shrubs (i.e., willow [ Salix spp.]) were stable or declined slightly. These changes in

关键词： caribou climate change density dependence forage Fortymile Caribou Herd herbivory range expansion Rangifer tarandus remote sensing shrub expansion vegetation change

OnsagerNet: Learning stable and interpretable dynamics using a generalized Onsager principle

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arXiv 2020年

作者： Yu, Haijun Tian, Xinyuan Weinan, E. Li, Qianxiao NCMIS LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics The Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics National University of Singapore Singapore119077 Singapore Institute of High Performance Computing A*STAR Singapore138632 Singapore

We propose a systematic method for learning stable and physically interpretable dynamical models using sampled trajectory data from physical processes based on a generalized Onsager principle. The learned dynamics are autonomous ordinary differential equations parameterized by neural networks that retain clear physical structure information, such as free energy, diffusion, conservative motion and external forces. For high dimensional problems with a low dimensional slow manifold, an autoencoder with metric preserving regularization is introduced to find the low dimensional generalized coordinates on which we learn the generalized Onsager dynamics. Our method exhibits clear advantages over existing methods on benchmark problems for learning ordinary differential equations. We further apply this method to study Rayleigh-Bénard convection and learn Lorenz-like low dimensional autonomous reduced order models that capture both qualitative and quantitative properties of the underlying dynamics. This forms a general approach to building reduced order models for forced dissipative *** Codes 76E30, 34D20, 68T05/07, 82C35 Copyright © 2020, The Authors. All rights reserved.

关键词： Ordinary differential equations