1
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Harris J, Zaki MJ. Neural Models for Generating Natural Language Summaries from Temporal Personal Health Data. J Healthc Inform Res 2024; 8:370-399. [PMID: 38681757 PMCID: PMC11052757 DOI: 10.1007/s41666-023-00158-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 11/23/2023] [Accepted: 12/21/2023] [Indexed: 05/01/2024]
Abstract
With an increased interest in the production of personal health technologies designed to track user data (e.g., nutrient intake, step counts), there is now more opportunity than ever to surface meaningful behavioral insights to everyday users in the form of natural language. This knowledge can increase their behavioral awareness and allow them to take action to meet their health goals. It can also bridge the gap between the vast collection of personal health data and the summary generation required to describe an individual's behavioral tendencies. Previous work has focused on rule-based time-series data summarization methods designed to generate natural language summaries of interesting patterns found within temporal personal health data. We examine recurrent, convolutional, and Transformer-based encoder-decoder models to automatically generate natural language summaries from numeric temporal personal health data. We showcase the effectiveness of our models on real user health data logged in MyFitnessPal (Weber and Achananuparp 2016) and show that we can automatically generate high-quality natural language summaries. Our work serves as a first step towards the ambitious goal of automatically generating novel and meaningful temporal summaries from personal health data.
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Affiliation(s)
- Jonathan Harris
- Computer Science, Rensselaer Polytechnic Institute, Troy, NY USA
| | - Mohammed J. Zaki
- Computer Science, Rensselaer Polytechnic Institute, Troy, NY USA
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2
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Qi M, Santos H, Pinheiro P, McGuinness DL, Bennett KP. Demographic and socioeconomic determinants of access to care: A subgroup disparity analysis using new equity-focused measurements. PLoS One 2023; 18:e0290692. [PMID: 37972008 PMCID: PMC10653411 DOI: 10.1371/journal.pone.0290692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/15/2023] [Indexed: 11/19/2023] Open
Abstract
Disparities in healthcare access and utilization associated with demographic and socioeconomic status hinder advancement of health equity. Thus, we designed a novel equity-focused approach to quantify variations of healthcare access/utilization from the expectation in national target populations. We additionally applied survey-weighted logistic regression models, to identify factors associated with usage of a particular type of health care. To facilitate generation of analysis datasets, we built an National Health and Nutrition Examination Survey (NHANES) knowledge graph to help automate source-level dynamic analyses across different survey years and subjects' characteristics. We performed a cross-sectional subgroup disparity analysis of 2013-2018 NHANES on U.S. adults for receipt of diabetes treatments and vaccines against Hepatitis A (HAV), Hepatitis B (HBV), and Human Papilloma (HPV). Results show that in populations with hemoglobin A1c level ≥6%, patients with non-private insurance were less likely to receive newer and more beneficial antidiabetic medications; being Asian further exacerbated these disparities. For widely used drugs such as insulin, Asians experienced insignificant disparities in odds of prescription compared to White patients but received highly inadequate treatments with regard to their distribution in U.S. diabetic population. Vaccination rates were associated with some demographic/socioeconomic factors but not the others at different degrees for different diseases. For instance, while equity scores increase with rising education levels for HBV, they decrease with rising wealth levels for HPV. Among women vaccinated against HPV, minorities and poor communities usually received Cervarix while non-Hispanic White and higher-income groups received the more comprehensive Gardasil vaccine. Our study identified and quantified the impact of determinants of healthcare utilization for antidiabetic medications and vaccinations. Our new methods for semantics-aware disparity analysis of NHANES data could be readily generalized to other public health goals to support more rapid identification of disparities and development of policies, thus advancing health equity.
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Affiliation(s)
- Miao Qi
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York, United States of America
| | - Henrique Santos
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York, United States of America
| | - Paulo Pinheiro
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York, United States of America
- Parcela Semântica Lda, Madeira, Portugal
| | - Deborah L. McGuinness
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York, United States of America
| | - Kristin P. Bennett
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York, United States of America
- Department of Mathematical Sciences, Rensselaer Polytechnic Institute, Troy, New York, United States of America
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3
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Bose A, Burch M, Chowdhury A, Paschou P, Drineas P. Structure-informed clustering for population stratification in association studies. BMC Bioinformatics 2023; 24:411. [PMID: 37907836 PMCID: PMC10619291 DOI: 10.1186/s12859-023-05511-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 10/02/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants. RESULTS To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans. CONCLUSIONS CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.
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Affiliation(s)
- Aritra Bose
- Computational Genomics, IBM T.J Watson Research Center, Yorktown Heights, NY, USA
| | - Myson Burch
- Computational Genomics, IBM T.J Watson Research Center, Yorktown Heights, NY, USA
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Agniva Chowdhury
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Peristera Paschou
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Petros Drineas
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
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4
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Kim S, Schroeder CM, Jackson NE. Open Macromolecular Genome: Generative Design of Synthetically Accessible Polymers. ACS Polym Au 2023; 3:318-330. [PMID: 37576712 PMCID: PMC10416319 DOI: 10.1021/acspolymersau.3c00003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/13/2023] [Accepted: 03/14/2023] [Indexed: 03/31/2023]
Abstract
A grand challenge in polymer science lies in the predictive design of new polymeric materials with targeted functionality. However, de novo design of functional polymers is challenging due to the vast chemical space and an incomplete understanding of structure-property relations. Recent advances in deep generative modeling have facilitated the efficient exploration of molecular design space, but data sparsity in polymer science is a major obstacle hindering progress. In this work, we introduce a vast polymer database known as the Open Macromolecular Genome (OMG), which contains synthesizable polymer chemistries compatible with known polymerization reactions and commercially available reactants selected for synthetic feasibility. The OMG is used in concert with a synthetically aware generative model known as Molecule Chef to identify property-optimized constitutional repeating units, constituent reactants, and reaction pathways of polymers, thereby advancing polymer design into the realm of synthetic relevance. As a proof-of-principle demonstration, we show that polymers with targeted octanol-water solubilities are readily generated together with monomer reactant building blocks and associated polymerization reactions. Suggested reactants are further integrated with Reaxys polymerization data to provide hypothetical reaction conditions (e.g., temperature, catalysts, and solvents). Broadly, the OMG is a polymer design approach capable of enabling data-intensive generative models for synthetic polymer design. Overall, this work represents a significant advance, enabling the property targeted design of synthetic polymers subject to practical synthetic constraints.
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Affiliation(s)
- Seonghwan Kim
- Department
of Materials Science and Engineering, University
of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Charles M. Schroeder
- Department
of Chemistry, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, United States
- Department
of Materials Science and Engineering, University
of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department
of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Nicholas E. Jackson
- Department
of Chemistry, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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5
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Athena F, West MP, Hah J, Graham S, Vogel EM. Trade-off between Gradual Set and On/Off Ratio in HfO x-Based Analog Memory with a Thin SiO x Barrier Layer. ACS Appl Electron Mater 2023; 5:3048-3058. [PMID: 37396057 PMCID: PMC10308818 DOI: 10.1021/acsaelm.3c00131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 04/27/2023] [Indexed: 07/04/2023]
Abstract
HfOx-based synapses are widely accepted as a viable candidate for both in-memory and neuromorphic computing. Resistance change in oxide-based synapses is caused by the motion of oxygen vacancies. HfOx-based synapses typically demonstrate an abrupt nonlinear resistance change under positive bias application (set), limiting their viability as analog memory. In this work, a thin barrier layer of AlOx or SiOx is added to the bottom electrode/oxide interface to slow the migration of oxygen vacancies. Electrical results show that the resistance change in HfOx/SiOx devices is more controlled than the HfOx devices during the set. While the on/off ratio for the HfOx/SiOx devices is still large (∼10), it is shown to be smaller than that of HfOx/AlOx and HfOx devices. Finite element modeling suggests that the slower oxygen vacancy migration in HfOx/SiOx devices during reset results in a narrower rupture region in the conductive filament. The narrower rupture region causes a lower high resistance state and, thus, a smaller on/off ratio for the HfOx/SiOx devices. Overall, the results show that slowing the motion of oxygen vacancies in the barrier layer devices improves the resistance change during the set but lowers the on/off ratio.
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Affiliation(s)
- Fabia
F. Athena
- School
of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Matthew P. West
- School
of Materials Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
| | - Jinho Hah
- School
of Materials Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
| | - Samuel Graham
- Department
of Mechanical Engineering, University of
Maryland, College Park, Maryland 20742, United States
- George
W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Eric M. Vogel
- School
of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- School
of Materials Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
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6
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Biner DW, Grosch JS, Ortoleva PJ. B-cell epitope discovery: The first protein flexibility-based algorithm-Zika virus conserved epitope demonstration. PLoS One 2023; 18:e0262321. [PMID: 36920995 PMCID: PMC10016673 DOI: 10.1371/journal.pone.0262321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 12/22/2021] [Indexed: 03/16/2023] Open
Abstract
Antibody-antigen interaction-at antigenic local environments called B-cell epitopes-is a prominent mechanism for neutralization of infection. Effective mimicry, and display, of B-cell epitopes is key to vaccine design. Here, a physical approach is evaluated for the discovery of epitopes which evolve slowly over closely related pathogens (conserved epitopes). The approach is 1) protein flexibility-based and 2) demonstrated with clinically relevant enveloped viruses, simulated via molecular dynamics. The approach is validated against 1) seven structurally characterized enveloped virus epitopes which evolved the least (out of thirty-nine enveloped virus-antibody structures), 2) two structurally characterized non-enveloped virus epitopes which evolved slowly (out of eight non-enveloped virus-antibody structures), and 3) eight preexisting epitope and peptide discovery algorithms. Rationale for a new benchmarking scheme is presented. A data-driven epitope clustering algorithm is introduced. The prediction of five Zika virus epitopes (for future exploration on recombinant vaccine technologies) is demonstrated. For the first time, protein flexibility is shown to outperform solvent accessible surface area as an epitope discovery metric.
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Affiliation(s)
- Daniel W. Biner
- Department of Chemistry, Indiana University, Bloomington, Indiana, United States of America
| | - Jason S. Grosch
- Department of Chemistry, Indiana University, Bloomington, Indiana, United States of America
| | - Peter J. Ortoleva
- Department of Chemistry, Indiana University, Bloomington, Indiana, United States of America
- * E-mail:
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7
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Wada K, Goto M, Tanaka H, Mizukami M, Suzuki Y, Lee KH, Yamashita H. Discovery of C 20-Diterpenoid Alkaloid Kobusine Derivatives Exhibiting Sub-G1 Inducing Activity. ACS Omega 2022; 7:28173-28181. [PMID: 35990488 PMCID: PMC9386823 DOI: 10.1021/acsomega.2c02363] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
Although many diterpenoid alkaloids have been evaluated recently for antiproliferative activity against human cancer cell lines, little data have been offered relating to the antiproliferative effects of hetisine-type C20-diterpenoid alkaloids, such as kobusine (1), likewise as their derivatives. A total of 43 novel diterpenoid alkaloid derivatives (2-10, 2b, 3a, 3b, 6a-16a, 7b, 9b, 10b, 13, 15-26, 15b, 18a, 23a, 27a) were prepared by C-11 and -15 esterification of 1. Antiproliferative effects of the natural parent compound (1) and all synthesized kobusine derivatives against human cancer cell lines, including a triple-negative breast cancer (TNBC) cell line as well as a P-glycoprotein overexpressing multidrug-resistant subline, were assessed. The structure-based design strategy resulted in the lead derivative 11,15-dibenzoylkobusine (3; average IC50 7.3 μM). Several newly synthesized kobusine derivatives (particularly, 5-8, 10, 13, 15-26) exhibited substantial suppressive effects against all tested human cancer cell lines. In contrast, kobusine (1), 11,15-O-diacetylkobusine (2), 11-acylkobusine derivatives (3a, 6a, 9a, 11a, 12a, 15a, 27a), and 15-acylkobusine derivatives (2b, 3b, 7b, 9b, 10b, 15b) showed no effect. The most active kobusine derivatives primarily had two specific substitution patterns, C-11,15 and C-11. Notably, 11,15-diacylkobusine derivatives (3, 6-10, 13, 15, 16, 18, 23) were more potent compared with 11- and 15-acylkobusine derivatives (3a, 3b, 6a-10a, 7b, 9b, 10b, 13a, 15a, 15b, 16a, 18a, 23a). Derivatives 13 and 25 induced MDA-MB-231 cells to the sub-G1 phase within 12 h. 11,15-Diacylation of kobusine (1) appears to be crucial for inducing antiproliferative activity in this alkaloid class and could introduce a new avenue to overcome TNBC using natural product derivatives.
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Affiliation(s)
- Koji Wada
- Department
of Medicinal Chemistry, Faculty of Pharmaceutical Sciences, Hokkaido University of Science, 4-1, Maeda 7-jo 15-choume, Teine-ku, Sapporo 006-8585, Japan
| | - Masuo Goto
- Division
of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of
Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599-7568, United States
| | - Hisano Tanaka
- Department
of Medicinal Chemistry, Faculty of Pharmaceutical Sciences, Hokkaido University of Science, 4-1, Maeda 7-jo 15-choume, Teine-ku, Sapporo 006-8585, Japan
| | - Megumi Mizukami
- Department
of Medicinal Chemistry, Faculty of Pharmaceutical Sciences, Hokkaido University of Science, 4-1, Maeda 7-jo 15-choume, Teine-ku, Sapporo 006-8585, Japan
| | - Yuji Suzuki
- Department
of Medicinal Chemistry, Faculty of Pharmaceutical Sciences, Hokkaido University of Science, 4-1, Maeda 7-jo 15-choume, Teine-ku, Sapporo 006-8585, Japan
| | - Kuo-Hsiung Lee
- Division
of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of
Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599-7568, United States
| | - Hiroshi Yamashita
- Department
of Medicinal Chemistry, Faculty of Pharmaceutical Sciences, Hokkaido University of Science, 4-1, Maeda 7-jo 15-choume, Teine-ku, Sapporo 006-8585, Japan
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8
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Gao H, Ni J, Zhang Y, Qian K, Chang S, Hasegawa-Johnson M. Domain Generalization for Language-Independent Automatic Speech Recognition. Front Artif Intell 2022; 5:806274. [PMID: 35647534 PMCID: PMC9133481 DOI: 10.3389/frai.2022.806274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 03/22/2022] [Indexed: 12/04/2022] Open
Abstract
A language-independent automatic speech recognizer (ASR) is one that can be used for phonetic transcription in languages other than the languages in which it was trained. Language-independent ASR is difficult to train, because different languages implement phones differently: even when phonemes in two different languages are written using the same symbols in the international phonetic alphabet, they are differentiated by different distributions of language-dependent redundant articulatory features. This article demonstrates that the goal of language-independence may be approximated in different ways, depending on the size of the training set, the presence vs. absence of familial relationships between the training and test languages, and the method used to implement phone recognition or classification. When the training set contains many languages, and when every language in the test set is related (shares the same language family with) a language in the training set, then language-independent ASR may be trained using an empirical risk minimization strategy (e.g., using connectionist temporal classification without extra regularizers). When the training set is limited to a small number of languages from one language family, however, and the test languages are not from the same language family, then the best performance is achieved by using domain-invariant representation learning strategies. Two different representation learning strategies are tested in this article: invariant risk minimization, and regret minimization. We find that invariant risk minimization is better at the task of phone token classification (given known segment boundary times), while regret minimization is better at the task of phone token recognition.
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Affiliation(s)
- Heting Gao
- Department of Electrical and Computer Engineering (ECE), Beckman Institute, University of Illinois, Urbana, IL, United States
| | - Junrui Ni
- Department of Electrical and Computer Engineering (ECE), Beckman Institute, University of Illinois, Urbana, IL, United States
| | - Yang Zhang
- MIT-IBM Watson AI Lab, Cambridge, MA, United States
| | - Kaizhi Qian
- MIT-IBM Watson AI Lab, Cambridge, MA, United States
| | - Shiyu Chang
- MIT-IBM Watson AI Lab, Cambridge, MA, United States
- Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - Mark Hasegawa-Johnson
- Department of Electrical and Computer Engineering (ECE), Beckman Institute, University of Illinois, Urbana, IL, United States
- *Correspondence: Mark Hasegawa-Johnson
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9
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Graff DE, Shakhnovich EI, Coley CW. Accelerating high-throughput virtual screening through molecular pool-based active learning. Chem Sci 2021; 12:7866-7881. [PMID: 34168840 PMCID: PMC8188596 DOI: 10.1039/d0sc06805e] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 04/26/2021] [Indexed: 12/13/2022] Open
Abstract
Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in excess of 108 molecules), so too do the resources necessary to conduct exhaustive virtual screening campaigns on these libraries. However, Bayesian optimization techniques, previously employed in other scientific discovery problems, can aid in their exploration: a surrogate structure-property relationship model trained on the predicted affinities of a subset of the library can be applied to the remaining library members, allowing the least promising compounds to be excluded from evaluation. In this study, we explore the application of these techniques to computational docking datasets and assess the impact of surrogate model architecture, acquisition function, and acquisition batch size on optimization performance. We observe significant reductions in computational costs; for example, using a directed-message passing neural network we can identify 94.8% or 89.3% of the top-50 000 ligands in a 100M member library after testing only 2.4% of candidate ligands using an upper confidence bound or greedy acquisition strategy, respectively. Such model-guided searches mitigate the increasing computational costs of screening increasingly large virtual libraries and can accelerate high-throughput virtual screening campaigns with applications beyond docking.
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Affiliation(s)
- David E Graff
- Department of Chemistry and Chemical Biology, Harvard University Cambridge MA USA
| | - Eugene I Shakhnovich
- Department of Chemistry and Chemical Biology, Harvard University Cambridge MA USA
| | - Connor W Coley
- Department of Chemical Engineering, MIT Cambridge MA USA
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10
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Perfetto L, Pastrello C, del-Toro N, Duesbury M, Iannuccelli M, Kotlyar M, Licata L, Meldal B, Panneerselvam K, Panni S, Rahimzadeh N, Ricard-Blum S, Salwinski L, Shrivastava A, Cesareni G, Pellegrini M, Orchard S, Jurisica I, Hermjakob H, Porras P. The IMEx coronavirus interactome: an evolving map of Coronaviridae-host molecular interactions. Database (Oxford) 2020; 2020:baaa096. [PMID: 33206959 PMCID: PMC7673336 DOI: 10.1093/database/baaa096] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 10/19/2020] [Accepted: 10/23/2020] [Indexed: 12/14/2022]
Abstract
The current coronavirus disease of 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus (SARS-CoV)-2, has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions can provide fine-grained resolution of the mechanisms behind the virus biology and the human organism response. We present a curated dataset of physical molecular interactions focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family that has been manually extracted by International Molecular Exchange (IMEx) Consortium curators. Currently, the dataset comprises over 4400 binarized interactions extracted from 151 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website (https://www.ebi.ac.uk/intact) and will be continuously updated as research on COVID-19 progresses.
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Affiliation(s)
- L Perfetto
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - C Pastrello
- Krembil Research Institute, Data Science Discovery Centre for Chronic Diseases, University Health Network, 5KD-407, 60 Leonard Avenue, Toronto, ON, M5T 0S8, Canada
| | - N del-Toro
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - M Duesbury
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
- UCLA-DOE Institute, UCLA, Los Angeles, CA 90095, USA
| | - M Iannuccelli
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, 00133, Italy
| | - M Kotlyar
- Krembil Research Institute, Data Science Discovery Centre for Chronic Diseases, University Health Network, 5KD-407, 60 Leonard Avenue, Toronto, ON, M5T 0S8, Canada
| | - L Licata
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, 00133, Italy
| | - B Meldal
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - K Panneerselvam
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - S Panni
- Department of Biology, Ecology and Earth Sciences, Università della Calabria, Rende, 87036, Italy
| | - N Rahimzadeh
- UCLA-DOE Institute, UCLA, Los Angeles, CA 90095, USA
- Providence John Wayne Cancer Institute, Department of Translational Molecular, Santa Monica, CA 90404, USA
| | - S Ricard-Blum
- Univ Lyon, University Claude Bernard Lyon 1, INSA Lyon, CPE, Institute of Molecular and Supramolecular Chemistry and Biochemistry (ICBMS), UMR 5246, F-69622 Villeurbanne, 69622, France
| | - L Salwinski
- UCLA-DOE Institute, UCLA, Los Angeles, CA 90095, USA
| | - A Shrivastava
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - G Cesareni
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, 00133, Italy
| | - M Pellegrini
- Department of Molecular, Cell and Developmental Biology, UCLA, Los Angeles, CA 90095, USA
| | - S Orchard
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - I Jurisica
- Krembil Research Institute, Data Science Discovery Centre for Chronic Diseases, University Health Network, 5KD-407, 60 Leonard Avenue, Toronto, ON, M5T 0S8, Canada
- Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, ON, M5T 0S8, Canada
| | - H Hermjakob
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - P Porras
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
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Badal VD, Wright D, Katsis Y, Kim HC, Swafford AD, Knight R, Hsu CN. Challenges in the construction of knowledge bases for human microbiome-disease associations. Microbiome 2019; 7:129. [PMID: 31488215 PMCID: PMC6728997 DOI: 10.1186/s40168-019-0742-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Accepted: 08/20/2019] [Indexed: 05/05/2023]
Abstract
The last few years have seen tremendous growth in human microbiome research, with a particular focus on the links to both mental and physical health and disease. Medical and experimental settings provide initial sources of information about these links, but individual studies produce disconnected pieces of knowledge bounded in context by the perspective of expert researchers reading full-text publications. Building a knowledge base (KB) consolidating these disconnected pieces is an essential first step to democratize and accelerate the process of accessing the collective discoveries of human disease connections to the human microbiome. In this article, we survey the existing tools and development efforts that have been produced to capture portions of the information needed to construct a KB of all known human microbiome-disease associations and highlight the need for additional innovations in natural language processing (NLP), text mining, taxonomic representations, and field-wide vocabulary standardization in human microbiome research. Addressing these challenges will enable the construction of KBs that help identify new insights amenable to experimental validation and potentially clinical decision support.
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Affiliation(s)
- Varsha Dave Badal
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
| | - Dustin Wright
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
- Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
| | - Yannis Katsis
- Scalable Knowledge Intelligence, IBM Research-Almaden, 650 Harry Road, San Jose, CA 95120 USA
| | - Ho-Cheol Kim
- Scalable Knowledge Intelligence, IBM Research-Almaden, 650 Harry Road, San Jose, CA 95120 USA
| | - Austin D. Swafford
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
| | - Rob Knight
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
- Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
- UCSD Health Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
| | - Chun-Nan Hsu
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
- Department of Neurosciences and Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
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Vat LE, Ryan D, Etchegary H. Recruiting patients as partners in health research: a qualitative descriptive study. Res Involv Engagem 2017; 3:15. [PMID: 29062540 PMCID: PMC5611573 DOI: 10.1186/s40900-017-0067-x] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 08/09/2017] [Indexed: 05/06/2023]
Abstract
PLAIN ENGLISH SUMMARY Increasingly, funders and researchers want to partner with patients in health research, but it can be challenging for researchers to find patient partners. More than taking part in research as participants, patient partners help design, carry out and manage research projects. The goal of this study was to describe ways that patient partners have been recruited by researchers and patient engagement leads (individuals within organizations responsible for promoting and supporting patients as research partners). We talked with researchers and patient engagement leads in Canada and the United Kingdom, as well as a patient representative. We found three ways that could help researchers and patients find each other. One way is a case-by-case basis, where patients are often sought with experience of a health condition that is the focus of the research. The other ways involved directories where projects were posted and could be found by patients and researchers, or a third party matched patients with research projects. We found four recruitment strategies:Social marketingCommunity outreachHealth systemPartnering with other organizations (e.g., advocacy groups) There are many influences on finding, selecting and retaining patient partners: patient characteristics, the local setting, the opportunity, work climate, education and support. We hope study results will provide a useful starting point for research teams in recruiting their patient partners. ABSTRACT Background Patient engagement in clinical trials and other health research continues to gain momentum. While the benefits of patient engagement in research are emerging, relatively little is known about recruiting patients as research partners. The purpose of this study was to describe recruitment strategies and models of recruiting patients as partners in health research. Methods Qualitative descriptive study. Thirteen patient engagement leads and health researchers from Canada and the United Kingdom, as well as one patient representative from a national patient organization (7 female) completed semi-structured interviews. Results Recruitment infrastructures available to respondents varied, but could be categorized into three models including the traditional, third-party and directory models. Four categories of recruitment strategies were identified, representing multiple ways of recruiting patient partners: social marketing recruitment, community outreach recruitment, health system recruitment, and partnering recruitment. Conclusions Multiple recruitment strategies were identified for engaging patient partners in research, and some common factors influenced recruitment. Study findings contribute to the evidence base in patient engagement and provide guidance for research teams to help identify potential recruitment methods for their patient partners.
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Affiliation(s)
- Lidewij Eva Vat
- Training and Capacity Lead, NL SUPPORT Unit, Memorial University of Newfoundland, Faculty of Medicine, Room 4M104, St. John’s, NF A1B 3V6 Canada
| | - Devonne Ryan
- Research Assistant/ PhD Candidate, Memorial University of Newfoundland, Clinical Epidemiology Unit, Faculty of Medicine, Suite 4M120, St. John’s, NF A1B 3V6 Canada
| | - Holly Etchegary
- Assistant professor/ Patient Engagement lead NL SUPPORT Unit, Memorial University of Newfoundland, Clinical Epidemiology Unit, Faculty of Medicine, Craig L. Dobbin Centre for Genetics, Room 4M220, St. John’s, NF A1B 3V6 Canada
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Ali MN, Schoop LM, Garg C, Lippmann JM, Lara E, Lotsch B, Parkin SSP. Butterfly magnetoresistance, quasi-2D Dirac Fermi surface and topological phase transition in ZrSiS. Sci Adv 2016; 2:e1601742. [PMID: 28028541 PMCID: PMC5161428 DOI: 10.1126/sciadv.1601742] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 11/15/2016] [Indexed: 05/14/2023]
Abstract
Magnetoresistance (MR), the change of a material's electrical resistance in response to an applied magnetic field, is a technologically important property that has been the topic of intense study for more than a quarter century. We report the observation of an unusual "butterfly"-shaped titanic angular magnetoresistance (AMR) in the nonmagnetic Dirac material, ZrSiS, which we find to be the most conducting sulfide known, with a 2-K resistivity as low as 48(4) nΩ⋅cm. The MR in ZrSiS is large and positive, reaching nearly 1.8 × 105 percent at 9 T and 2 K at a 45° angle between the applied current (I || a) and the applied field (90° is H || c). Approaching 90°, a "dip" is seen in the AMR, which, by analyzing Shubnikov de Haas oscillations at different angles, we find to coincide with a very sharp topological phase transition unlike any seen in other known Dirac/Weyl materials. We find that ZrSiS has a combination of two-dimensional (2D) and 3D Dirac pockets comprising its Fermi surface and that the combination of high-mobility carriers and multiple pockets in ZrSiS allows for large property changes to occur as a function of angle between applied fields. This makes it a promising platform to study the physics stemming from the coexistence of 2D and 3D Dirac electrons as well as opens the door to creating devices focused on switching between different parts of the Fermi surface and different topological states.
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Affiliation(s)
- Mazhar N. Ali
- IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95120, USA
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany
- Corresponding author.
| | - Leslie M. Schoop
- Max Planck Institute for Solid State Research, Heisenbergstasse 1, 70569 Stuttgart, Germany
| | - Chirag Garg
- IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95120, USA
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany
| | - Judith M. Lippmann
- Max Planck Institute for Solid State Research, Heisenbergstasse 1, 70569 Stuttgart, Germany
| | - Erik Lara
- IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95120, USA
| | - Bettina Lotsch
- Max Planck Institute for Solid State Research, Heisenbergstasse 1, 70569 Stuttgart, Germany
- Department of Chemistry, Ludwig-Maximilians-Universität München, Butenandtstrasse 5-13, 81377 München, Germany
| | - Stuart S. P. Parkin
- IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95120, USA
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany
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