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402 results on '"Swamidass, S. Joshua"'

1. UNIFYING A DIVIDED CHURCH: Fifty years later, the Lausanne Covenant gives us a chance to remember who we are-and who we're not

Author

Swamidass, S. Joshua

Subjects
Lausanne Covenant (Nonfiction work) -- Criticism and interpretation, Theologians -- Public opinion, Church -- Unity, Evangelists -- Public opinion, Evangelicalism -- Analysis, Christian union -- Analysis
Abstract

IN IN THE 2000 MOVIE MEMENTO, PROTAGONIST LEONARD Shelby has a specific brain injury that prevents him from forming new long-term memories. He can remember information for 30 seconds to [...]

Published
2024

3. Fair-Net: A Network Architecture For Reducing Performance Disparity Between Identifiable Sub-Populations

Author

Datta, Arghya and Swamidass, S. Joshua

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

In real world datasets, particular groups are under-represented, much rarer than others, and machine learning classifiers will often preform worse on under-represented populations. This problem is aggravated across many domains where datasets are class imbalanced, with a minority class far rarer than the majority class. Naive approaches to handle under-representation and class imbalance include training sub-population specific classifiers that handle class imbalance or training a global classifier that overlooks sub-population disparities and aims to achieve high overall accuracy by handling class imbalance. In this study, we find that these approaches are vulnerable in class imbalanced datasets with minority sub-populations. We introduced Fair-Net, a branched multitask neural network architecture that improves both classification accuracy and probability calibration across identifiable sub-populations in class imbalanced datasets. Fair-Nets is a straightforward extension to the output layer and error function of a network, so can be incorporated in far more complex architectures. Empirical studies with three real world benchmark datasets demonstrate that Fair-Net improves classification and calibration performance, substantially reducing performance disparity between gender and racial sub-populations.

Published
2021
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8. Deep learning long-range information in undirected graphs with wave networks

Author

Matlock, Matthew K., Datta, Arghya, Dang, Na Le, Jiang, Kevin, and Swamidass, S. Joshua

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Graph algorithms are key tools in many fields of science and technology. Some of these algorithms depend on propagating information between distant nodes in a graph. Recently, there have been a number of deep learning architectures proposed to learn on undirected graphs. However, most of these architectures aggregate information in the local neighborhood of a node, and therefore they may not be capable of efficiently propagating long-range information. To solve this problem we examine a recently proposed architecture, wave, which propagates information back and forth across an undirected graph in waves of nonlinear computation. We compare wave to graph convolution, an architecture based on local aggregation, and find that wave learns three different graph-based tasks with greater efficiency and accuracy. These three tasks include (1) labeling a path connecting two nodes in a graph, (2) solving a maze presented as an image, and (3) computing voltages in a circuit. These tasks range from trivial to very difficult, but wave can extrapolate from small training examples to much larger testing examples. These results show that wave may be able to efficiently solve a wide range of problems that require long-range information propagation across undirected graphs. An implementation of the wave network, and example code for the maze problem are included in the tflon deep learning toolkit (https://bitbucket.org/mkmatlock/tflon).

Published
2018

14. A survey of current trends in computational drug repositioning

Author

Li, Jiao, Zheng, Si, Chen, Bin, Butte, Atul J, Swamidass, S Joshua, and Lu, Zhiyong

Subjects
Networking and Information Technology R&D (NITRD), Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, Generic health relevance, Good Health and Well Being, Computational Biology, Data Mining, Drug Combinations, Drug Repositioning, Genomics, Humans, Machine Learning, Molecular Structure, Phenotype, Surveys and Questionnaires, computational drug repositioning, integrative strategies, genome, phenome, chemical structure, drug combination, prediction validation, Biochemistry and Cell Biology, Computation Theory and Mathematics, Other Information and Computing Sciences, Bioinformatics
Abstract

Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.

Published
2016

16. Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples

Author

Barnell, Erica K., Ronning, Peter, Campbell, Katie M., Krysiak, Kilannin, Ainscough, Benjamin J., Sheta, Lana M., Pema, Shahil P., Schmidt, Alina D., Richters, Megan, Cotto, Kelsy C., Danos, Arpad M., Ramirez, Cody, Skidmore, Zachary L., Spies, Nicholas C., Hundal, Jasreet, Sediqzad, Malik S., Kunisaki, Jason, Gomez, Felicia, Trani, Lee, Matlock, Matthew, Wagner, Alex H., Swamidass, S. Joshua, Griffith, Malachi, and Griffith, Obi L.

Published
2019
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19. Large scale study of multiple-molecule queries

Author

Nasr, Ramzi J, Swamidass, S Joshua, and Baldi, Pierre F

Abstract

Background: In ligand-based screening, as well as in other chemoinformatics applications, one seeks to effectively search large repositories of molecules in order to retrieve molecules that are similar typically to a single molecule lead. However, in some case, multiple molecules from the same family are available to seed the query and search for other members of the same family. Multiple-molecule query methods have been less studied than single-molecule query methods. Furthermore, the previous studies have relied on proprietary data and sometimes have not used proper cross-validation methods to assess the results. In contrast, here we develop and compare multiple-molecule query methods using several large publicly available data sets and background. We also create a framework based on a strict cross-validation protocol to allow unbiased benchmarking for direct comparison in future studies across several performance metrics. Results: Fourteen different multiple-molecule query methods were defined and benchmarked using: (1) 41 publicly available data sets of related molecules with similar biological activity; and (2) publicly available background data sets consisting of up to 175,000 molecules randomly extracted from the ChemDB database and other sources. Eight of the fourteen methods were parameter free, and six of them fit one or two free parameters to the data using a careful cross-validation protocol. All the methods were assessed and compared for their ability to retrieve members of the same family against the background data set by using several performance metrics including the Area Under the Accumulation Curve (AUAC), Area Under the Curve (AUC), F1-measure, and BEDROC metrics. Consistent with the previous literature, the best parameter-free methods are the MAX-SIM and MIN-RANK methods, which score a molecule to a family by the maximum similarity, or minimum ranking, obtained across the family. One new parameterized method introduced in this study and two previously defined methods, the Exponential Tanimoto Discriminant (ETD), the Tanimoto Power Discriminant (TPD), and the Binary Kernel Discriminant (BKD), outperform most other methods but are more complex, requiring one or two parameters to be fit to the data. Conclusion: Fourteen methods for multiple-molecule querying of chemical databases, including novel methods, (ETD) and (TPD), are validated using publicly available data sets, standard cross-validation protocols, and established metrics. The best results are obtained with ETD, TPD, BKD, MAX-SIM, and MIN-RANK. These results can be replicated and compared with the results of future studies using data freely downloadable from http://cdb.ics.uci.edu/.

Published
2009

22. Precision Medicine in Pancreatic Disease—Knowledge Gaps and Research Opportunities: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop

Author

Lowe, Mark E., Andersen, Dana K., Caprioli, Richard M., Choudhary, Jyoti, Cruz-Monserrate, Zobeida, Dasyam, Anil K., Forsmark, Christopher E., Gorelick, Fred S., Gray, Joe W., Haupt, Mark, Kelly, Kimberly A., Olive, Kenneth P., Plevritis, Sylvia K., Rappaport, Noa, Roth, Holger R., Steen, Hanno, Swamidass, S. Joshua, Tirkes, Temel, Uc, Aliye, Veselkov, Kirill, Whitcomb, David C., and Habtezion, Aida

Published
2019
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25. Novel advances in biotransformation and bioactivation research – 2020 year in review

Author

Khojasteh, S Cyrus, Argikar, Upendra A, Driscoll, James P, Heck, Carley JS, King, Lloyd, Jackson, Klarissa D, Jian, Wenying, Kalgutkar, Amit S, Miller, Grover P, Kramlinger, Valerie, Rietjens, Ivonne MCM, Teitelbaum, Aaron M, Wang, Kai, Wei, Cong, Johnson, Benjamin M, Shu, Yue-Zhong, Zhuo, Xiaoliang, Meanwell, Nicholas A, Cerny, Matthew A, Obach, R Scott, Sharma, Raman, Spracklin, Douglas K, Walker, Gregory S, Goracci, Laura, Desantis, Jenny, Valeri, Aurora, Castellani, Beatrice, Eleuteri, Michela, Cruciani, Gabriele, Lall, Manjinder S, Bassyouni, Asser, Bradow, James, Brown, Maria, Bundesmann, Mark, Chen, Jinshan, Ciszewski, Gregory, Hagen, Anne E, Hyek, Dennis, Jenkinson, Stephen, Liu, Bo, Pan, Senliang, Reilly, Usa, Sach, Neal, Smaltz, Daniel J, Starr, Jeremy, Wagenaar, Melissa, de Bruyn Kops, Christina, Sicho, Martin, Mazzolari, Angelica, Kirchmair, Johannes, Korprasertthaworn, Porntipa, Chau, Nuy, Nair, Pramod C, Rowland, Andrew, Miners, John O, Wrobleski, Stephen T, Moslin, Ryan, Lin, Shuqun, Zhang, Yanlei, Spergel, Steven, Kempson, James, Tokarski, John S, Strnad, Joann, Zupa-Fernandez, Adriana, Cheng, Lihong, Shuster, David, Gillooly, Kathleen, Yang, Xiaoxia, Heimrich, Elizabeth, McIntyre, Kim W, Chaudhry, Charu, Khan, Javed, Ruzanov, Max, Tredup, Jeffrey, Mulligan, Dawn, Xie, Dianlin, Sun, Huadong, Huang, Christine, D'Arienzo, Celia, Aranibar, Nelly, Chiney, Manoj, Chimalakonda, Anjaneya, Pitts, William J, Lombardo, Louis, Carter, Percy H, Burke, James R, Weinstein, David S, Li, Jing, Liu, Ju, Enders, Jennifer, Arciprete, Michael, Tran, Chris, Aluri, Krishna, Guan, Li-Hua, O'Shea, Jonathan, Bisbe, Anna, Charisse, Klaus, Zlatev, Ivan, Najarian, Diana, Xu, Yuanxin, Kim, Jaeah, El Zahar, Noha M, Bartlett, Michael G, Katyayan, Kishore, Yi, Ping, Monk, Scott, Cassidy, Kenneth, Takahashi, Ryan H, Grandner, Jessica M, Bobba, Sudheer, Liu, Yanzhou, Beroza, Paul, Zhang, Donglu, Ma, Shuguang, Post, Noah, Yu, Rosie, Greenlee, Sarah, Gaus, Hans, Hurh, Eunju, Matson, John, Wang, Yanfeng, Tajima, Yuya, Toyoda, Takeshi, Hirayama, Yuichiro, Matsushita, Kohei, Yamada, Takanori, Ogawa, Kumiko, Watanabe, Kenji, Takamura-Enya, Takeji, Totsuka, Yukari, Wakabayashi, Keiji, Miyoshi, Noriyuki, Zhang, Jiayin, Chan, Chi-Kong, Ham, Yat-Hing, Chan, Wan, Schleiff, Mary Alexandra, Flynn, Noah R, Payakachat, Sasin, Schleiff, Benjamin Mark, Pinson, Anna O, Province, Dennis W, Swamidass, S Joshua, Boysen, Gunnar, Nardone-White, Dasean T, Bissada, Jennifer E, Abouda, Arsany A, Zhang, Zhuming, Connolly, Peter J, Lim, Heng Keang, Pande, Vineet, Meerpoel, Lieven, Teleha, Christopher, Branch, Jonathan R, Ondrus, Janine, Hickson, Ian, Bush, Tammy, Luistro, Leopoldo, Packman, Kathryn, Bischoff, James R, Ibrahim, Salam, Parrett, Christopher, Chong, Yolanda, Gottardis, Marco M, Bignan, Gilles, Mulder, Teresa, Bobba, Sudder, Johnson, Kevin M, Wang, Wei, Zhang, Chenghong, Cai, Jingwei, Choo, Edna F, Crawford, James J, Landry, Matthew L, Chen, Huifen, Kenny, Jane R, Lee, Wendy, Young, Wendy B, Geib, Timon, Thulasingam, Madhuranayaki, Haeggstrom, Jesper Z, Sleno, Lekha, Monroe, James J, Tanis, Keith Q, Podtelezhnikov, Alexei A, Nguyen, Truyen, Machotka, Sam V, Lynch, Donna, Evers, Raymond, Palamanda, Jairam, Miller, Randy R, Pippert, Todd, Cabalu, Tamara D, Johnson, Timothy E, Aslamkhan, Amy G, Kang, Wen, Tamburino, Alex M, Mitra, Kaushik, Agrawal, Nancy GB, and Sistare, Frank D

Subjects
METABOLISM, Toxicology, 030226 pharmacology & pharmacy, 03 medical and health sciences, nonp450 enzymes, 0302 clinical medicine, p450 enzymes, Biotransformation, INTRINSIC CLEARANCE, Political science, MASS-BALANCE, Humans, Pharmacology (medical), Pharmacology & Pharmacy, P450 Enzymes, General Pharmacology, Toxicology and Pharmaceutics, ANTISENSE OLIGONUCLEOTIDE, Toxicologie, VLAG, ARISTOLOCHIC ACID, Drug metabolism, Science & Technology, WIMEK, bioactivation, IDENTIFICATION, Year in review, IN-VITRO, NONHEPATOTOXIC DRUGS, Drug metabolizing enzymes, Drug development, 030220 oncology & carcinogenesis, COVALENT BINDING DATA, Engineering ethics, biotransformation, LIVER-MICROSOMES, Life Sciences & Biomedicine
Abstract

This annual review is the sixth of its kind since 2016 (see references). Our objective is to explore and share articles which we deem influential and significant in the field of biotransformation and bioactivation. These fields are constantly evolving with new molecular structures and discoveries of corresponding pathways for metabolism that impact relevant drug development with respect to efficacy and safety. Based on the selected articles, we created three sections: (1) drug design, (2) metabolites and drug metabolizing enzymes, and (3) bioactivation and safety (Table 1). Unlike in years past, more biotransformation experts have joined and contributed to this effort while striving to maintain a balance of authors from academic and industry settings.[Table: see text]. ispartof: DRUG METABOLISM REVIEWS vol:53 issue:3 pages:384-433 ispartof: location:England status: published

Published
2021

26. Statistical Distribution of Chemical Fingerprints

Author

Swamidass, S. Joshua, Baldi, Pierre, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Bloch, Isabelle, editor, Petrosino, Alfredo, editor, and Tettamanzi, Andrea G. B., editor

Published
2006
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27. The asthma gut microbiota influences lung inflammation in gnotobiotic mice

Author

Wilson, Naomi G., primary, Hernandez-Leyva, Ariel, additional, Rosen, Anne L., additional, Jaeger, Natalia, additional, McDonough, Ryan T., additional, Santiago-Borges, Jesus, additional, Lint, Michael A., additional, Rosen, Thomas R., additional, Tomera, Christopher P., additional, Bacharier, Leonard B., additional, Swamidass, S. Joshua, additional, and Kau, Andrew L., additional

Published
2022
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39. A U-Turn on Adam and Eve

Author

Swamidass, S. Joshua

Abstract

BioLogos updates their scientific position on Adam and Eve. This is an important step forward and a key milestone in the conversation.

Published
2021
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41. Deep Learning Coordinate-Free Quantum Chemistry

Author

Matlock, Matthew K., primary, Hoffman, Max, additional, Dang, Na Le, additional, Folmsbee, Dakota L., additional, Langkamp, Luke A., additional, Hutchison, Geoffrey R., additional, Kumar, Neeraj, additional, Sarullo, Kathryn, additional, and Swamidass, S. Joshua, additional

Published
2021
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