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2. What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask

Author

Kohane, Isaac S, Aronow, Bruce J, Avillach, Paul, Beaulieu-Jones, Brett K, Bellazzi, Riccardo, Bradford, Robert L, Brat, Gabriel A, Cannataro, Mario, Cimino, James J, García-Barrio, Noelia, Gehlenborg, Nils, Ghassemi, Marzyeh, Gutiérrez-Sacristán, Alba, Hanauer, David A, Holmes, John H, Hong, Chuan, Klann, Jeffrey G, Loh, Ne Hooi Will, Luo, Yuan, Mandl, Kenneth D, Daniar, Mohamad, Moore, Jason H, Murphy, Shawn N, Neuraz, Antoine, Ngiam, Kee Yuan, Omenn, Gilbert S, Palmer, Nathan, Patel, Lav P, Pedrera-Jiménez, Miguel, Sliz, Piotr, South, Andrew M, Tan, Amelia Li Min, Taylor, Deanne M, Taylor, Bradley W, Torti, Carlo, Vallejos, Andrew K, Wagholikar, Kavishwar B, Weber, Griffin M, and Cai, Tianxi

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

Coincident with the tsunami of COVID-19–related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

Published
2021
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4. The Sociological Imagination within Teaching Sociology: 1973-2020

Author

Palmer, Nathan

Abstract

The sociological imagination is widely considered essential to sociology and sociological scholarship-of-teaching-and-learning research. Still, sociologists have struggled to agree on precisely what it is and how to measure its development effectively. A content analysis of every article published in Teaching Sociology was conducted examining where the sociological imagination appeared in the journal, where authors claimed to develop students' sociological imagination, and the methodological sophistication of the evidence they provided to substantiate those claims. Analysis confirms the importance of the sociological imagination, appearing in a fourth of all published articles and nearly half of the articles published between 2010 and 2020. Just over a fourth of claims-making authors provided no evidence to validate their claims, and the frequency of making unsubstantiated claims persists even as the methodological rigor of the journal overall increased. Among the studies that provided evidence, however, the methodological sophistication appears to be increasing.

Published
2023
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5. Comprehensive interrogation of the ADAR2 deaminase domain for engineering enhanced RNA editing activity and specificity

Author

Katrekar, Dhruva, Xiang, Yichen, Palmer, Nathan, Saha, Anushka, Meluzzi, Dario, and Mali, Prashant

Subjects
Biotechnology, Genetics, Adenosine Deaminase, Humans, Nucleotide Motifs, Protein Domains, Protein Engineering, RNA Editing, RNA-Binding Proteins, Transcriptome, ADARs, RNA editing, split proteins, deep mutational scan, Human, biochemistry, chemical biology, computational biology, human, systems biology, Biochemistry and Cell Biology
Abstract

Adenosine deaminases acting on RNA (ADARs) can be repurposed to enable programmable RNA editing, however their exogenous delivery leads to transcriptome-wide off-targeting, and additionally, enzymatic activity on certain RNA motifs, especially those flanked by a 5' guanosine is very low thus limiting their utility as a transcriptome engineering toolset. Towards addressing these issues, we first performed a novel deep mutational scan of the ADAR2 deaminase domain, directly measuring the impact of every amino acid substitution across 261 residues, on RNA editing. This enabled us to create a domain-wide mutagenesis map while also revealing a novel hyperactive variant with improved enzymatic activity at 5'-GAN-3' motifs. As overexpression of ADAR enzymes, especially hyperactive variants, can lead to significant transcriptome-wide off-targeting, we next engineered a split-ADAR2 deaminase which resulted in >100-fold more specific RNA editing as compared to full-length deaminase overexpression. Taken together, we anticipate this systematic engineering of the ADAR2 deaminase domain will enable broader utility of the ADAR toolset for RNA biotechnology applications.

Published
2022

6. International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Author

Weber, Griffin M, Hong, Chuan, Xia, Zongqi, Palmer, Nathan P, Avillach, Paul, L’Yi, Sehi, Keller, Mark S, Murphy, Shawn N, Gutiérrez-Sacristán, Alba, Bonzel, Clara-Lea, Serret-Larmande, Arnaud, Neuraz, Antoine, Omenn, Gilbert S, Visweswaran, Shyam, Klann, Jeffrey G, South, Andrew M, Loh, Ne Hooi Will, Cannataro, Mario, Beaulieu-Jones, Brett K, Bellazzi, Riccardo, Agapito, Giuseppe, Alessiani, Mario, Aronow, Bruce J, Bell, Douglas S, Benoit, Vincent, Bourgeois, Florence T, Chiovato, Luca, Cho, Kelly, Dagliati, Arianna, DuVall, Scott L, Barrio, Noelia García, Hanauer, David A, Ho, Yuk-Lam, Holmes, John H, Issitt, Richard W, Liu, Molei, Luo, Yuan, Lynch, Kristine E, Maidlow, Sarah E, Malovini, Alberto, Mandl, Kenneth D, Mao, Chengsheng, Matheny, Michael E, Moore, Jason H, Morris, Jeffrey S, Morris, Michele, Mowery, Danielle L, Ngiam, Kee Yuan, Patel, Lav P, Pedrera-Jimenez, Miguel, Ramoni, Rachel B, Schriver, Emily R, Schubert, Petra, Balazote, Pablo Serrano, Spiridou, Anastasia, Tan, Amelia LM, Tan, Byorn WL, Tibollo, Valentina, Torti, Carlo, Trecarichi, Enrico M, Wang, Xuan, Kohane, Isaac S, Cai, Tianxi, and Brat, Gabriel A

Subjects
Health Services and Systems, Health Sciences, Infectious Diseases, Coronaviruses, Clinical Research, Emerging Infectious Diseases, 4.2 Evaluation of markers and technologies, Good Health and Well Being, Consortium for Clinical Characterization of COVID-19 by EHR, Health services and systems
Abstract

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

Published
2022

11. A matrix-free approach to geostatistical filtering

Author

Pereira, Mike, Desassis, Nicolas, Magneron, Cédric, and Palmer, Nathan

Subjects
Statistics - Methodology, Mathematics - Statistics Theory, Statistics - Applications
Abstract

In this paper, we present a novel approach to geostatistical filtering which tackles two challenges encountered when applying this method to complex spatial datasets: modeling the non-stationarity of the data while still being able to work with large datasets. The approach is based on a finite element approximation of Gaussian random fields expressed as an expansion of the eigenfunctions of a Laplace--Beltrami operator defined to account for local anisotropies. The numerical approximation of the resulting random fields using a finite element approach is then leveraged to solve the scalability issue through a matrix-free approach. Finally, two cases of application of this approach, on simulated and real seismic data are presented., Comment: 25 pages, 8 figures

Published
2020

12. Peptide-tiling screens of cancer drivers reveal oncogenic protein domains and associated peptide inhibitors

Author

Ford, Kyle M, Panwala, Rebecca, Chen, Dai-Hua, Portell, Andrew, Palmer, Nathan, and Mali, Prashant

Subjects
Biotechnology, Breast Cancer, Cancer, Development of treatments and therapeutic interventions, Aetiology, 2.1 Biological and endogenous factors, 5.1 Pharmaceuticals, Cell Proliferation, Humans, Neoplasms, Oncogenes, Peptides, Protein Domains, cancer drivers, inhibitors, peptides, protein-protein interactions, Biochemistry and Cell Biology
Abstract

Gene fragments derived from structural domains mediating physical interactions can modulate biological functions. Utilizing this, we developed lentiviral overexpression libraries of peptides comprehensively tiling high-confidence cancer driver genes. Toward inhibiting cancer growth, we assayed ~66,000 peptides, tiling 65 cancer drivers and 579 mutant alleles. Pooled fitness screens in two breast cancer cell lines revealed peptides, which selectively reduced cellular proliferation, implicating oncogenic protein domains important for cell fitness. Coupling of cell-penetrating motifs to these peptides enabled drug-like function, with peptides derived from EGFR and RAF1 inhibiting cell growth at IC50s of 27-63 μM. We anticipate that this peptide-tiling (PepTile) approach will enable rapid de novo mapping of bioactive protein domains and associated interfering peptides.

Published
2021

13. Long-lasting analgesia via targeted in situ repression of NaV1.7 in mice.

Author

Moreno, Ana M, Alemán, Fernando, Catroli, Glaucilene F, Hunt, Matthew, Hu, Michael, Dailamy, Amir, Pla, Andrew, Woller, Sarah A, Palmer, Nathan, Parekh, Udit, McDonald, Daniella, Roberts, Amanda J, Goodwill, Vanessa, Dryden, Ian, Hevner, Robert F, Delay, Lauriane, Gonçalves Dos Santos, Gilson, Yaksh, Tony L, and Mali, Prashant

Subjects
Ganglia, Spinal, Animals, Mice, Neuralgia, Hyperalgesia, Analgesia, Chronic Pain, Peripheral Neuropathy, Pain Research, Substance Misuse, Genetics, Neurosciences, Biotechnology, Drug Abuse (NIDA only), Neurodegenerative, Dental/Oral and Craniofacial Disease, Aetiology, 2.1 Biological and endogenous factors, Neurological, Biological Sciences, Medical and Health Sciences
Abstract

Current treatments for chronic pain rely largely on opioids despite their substantial side effects and risk of addiction. Genetic studies have identified in humans key targets pivotal to nociceptive processing. In particular, a hereditary loss-of-function mutation in NaV1.7, a sodium channel protein associated with signaling in nociceptive sensory afferents, leads to insensitivity to pain without other neurodevelopmental alterations. However, the high sequence and structural similarity between NaV subtypes has frustrated efforts to develop selective inhibitors. Here, we investigated targeted epigenetic repression of NaV1.7 in primary afferents via epigenome engineering approaches based on clustered regularly interspaced short palindromic repeats (CRISPR)-dCas9 and zinc finger proteins at the spinal level as a potential treatment for chronic pain. Toward this end, we first optimized the efficiency of NaV1.7 repression in vitro in Neuro2A cells and then, by the lumbar intrathecal route, delivered both epigenome engineering platforms via adeno-associated viruses (AAVs) to assess their effects in three mouse models of pain: carrageenan-induced inflammatory pain, paclitaxel-induced neuropathic pain, and BzATP-induced pain. Our results show effective repression of NaV1.7 in lumbar dorsal root ganglia, reduced thermal hyperalgesia in the inflammatory state, decreased tactile allodynia in the neuropathic state, and no changes in normal motor function in mice. We anticipate that this long-lasting analgesia via targeted in vivo epigenetic repression of NaV1.7 methodology we dub pain LATER, might have therapeutic potential in management of persistent pain states.

Published
2021

14. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium

Author

Aaron, James R., Adam, Atif, Agapito, Giuseppe, Albayrak, Adem, Albi, Giuseppe, Alessiani, Mario, Alloni, Anna, Amendola, Danilo F., Angoulvant, François, Anthony, Li LLJ., Aronow, Bruce J., Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Panickan, Vidul Ayakulangara, Azevedo, Paula S., Badenes, Rafael, Balshi, James, Batugo, Ashley, Beaulieu-Jones, Brendin R., Beaulieu-Jones, Brett K., Bell, Douglas S., Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Boeker, Martin, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence T., Bradford, Robert L., Brat, Gabriel A., Bréant, Stéphane, Brown, Nicholas W., Bruno, Raffaele, Bryant, William A., Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Cattelan, Anna Maria, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista Y., Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James J., Colicchio, Tiago K., Cormont, Sylvie, Cossin, Sébastien, Craig, Jean B., Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Dionne, Audrey, Duan, Rui, Dubiel, Julien, DuVall, Scott L., Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert W., Ganslandt, Thomas, García-Barrio, Noelia, Garmire, Lana X., Gehlenborg, Nils, Getzen, Emily J., Geva, Alon, Goh, Rachel SJ., González, Tomás González, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Guzzi, Pietro H., Han, Larry, Hanauer, David A., Haverkamp, Christian, Hazard, Derek Y., He, Bing, Henderson, Darren W., Hilka, Martin, Ho, Yuk-Lam, Holmes, John H., Honerlaw, Jacqueline P., Hong, Chuan, Huling, Kenneth M., Hutch, Meghan R., Issitt, Richard W., Jannot, Anne Sophie, Jouhet, Vianney, Kainth, Mundeep K., Kate, Kernan F., Kavuluru, Ramakanth, Keller, Mark S., Kennedy, Chris J., Kernan, Kate F., Key, Daniel A., Kirchoff, Katie, Klann, Jeffrey G., Kohane, Isaac S., Krantz, Ian D., Kraska, Detlef, Krishnamurthy, Ashok K., L'Yi, Sehi, Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Will Loh, Ne Hooi, Long, Qi, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine E., Mahmood, Sadiqa, Maidlow, Sarah E., Makoudjou, Adeline, Makwana, Simran, Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Maram, Anupama, Maripuri, Monika, Martel, Patricia, Martins, Marcelo R., Marwaha, Jayson S., Masino, Aaron J., Mazzitelli, Maria, Mazzotti, Diego R., Mensch, Arthur, Milano, Marianna, Minicucci, Marcos F., Moal, Bertrand, Ahooyi, Taha Mohseni, Moore, Jason H., Moraleda, Cinta, Morris, Jeffrey S., Morris, Michele, Moshal, Karyn L., Mousavi, Sajad, Mowery, Danielle L., Murad, Douglas A., Murphy, Shawn N., Naughton, Thomas P., Breda Neto, Carlos Tadeu, Neuraz, Antoine, Newburger, Jane, Ngiam, Kee Yuan, Njoroge, Wanjiku FM., Norman, James B., Obeid, Jihad, Okoshi, Marina P., Olson, Karen L., Omenn, Gilbert S., Orlova, Nina, Ostasiewski, Brian D., Palmer, Nathan P., Paris, Nicolas, Patel, Lav P., Pedrera-Jiménez, Miguel, Pfaff, Ashley C., Pfaff, Emily R., Pillion, Danielle, Pizzimenti, Sara, Priya, Tanu, Prokosch, Hans U., Prudente, Robson A., Prunotto, Andrea, Quirós-González, Víctor, Ramoni, Rachel B., Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Romero-Garcia, Nekane, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina C.C., Sanz Vidorreta, Fernando J., Savino, Maria, Schriver, Emily R., Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil J., Serrano-Balazote, Pablo, Serre, Patricia, Serret-Larmande, Arnaud, Shah, Mohsin A., Hossein Abad, Zahra Shakeri, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, South, Andrew M., Sperotto, Francesca, Spiridou, Anastasia, Strasser, Zachary H., Tan, Amelia LM., Tan, Bryce W.Q., Tan, Byorn W.L., Tanni, Suzana E., Taylor, Deanne M., Terriza-Torres, Ana I., Tibollo, Valentina, Tippmann, Patric, Toh, Emma MS., Torti, Carlo, Trecarichi, Enrico M., Vallejos, Andrew K., Varoquaux, Gael, Vella, Margaret E., Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar B., Waitman, Lemuel R., Wang, Xuan, Wassermann, Demian, Weber, Griffin M., Wolkewitz, Martin, Wong, Scott, Xia, Zongqi, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zachariasse, Joany M., Zahner, Janet J., Zambelli, Alberto, Zhang, Harrison G., Zöller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Li, Xiudi, Rofeberg, Valerie N., Elias, Matthew D., Laird-Gion, Jessica, and Newburger, Jane W.

Published
2023
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15. Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study

Author

Aaron, James R., Agapito, Giuseppe, Albayrak, Adem, Albi, Giuseppe, Alessiani, Mario, Alloni, Anna, Amendola, Danilo F., François Angoulvant, Anthony, Li L.L.J., Aronow, Bruce J., Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Azevedo, Paula S., Balshi, James, Beaulieu-Jones, Brett K., Bell, Douglas S., Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence T., Bradford, Robert L., Brat, Gabriel A., Bréant, Stéphane, Brown, Nicholas W., Bruno, Raffaele, Bryant, William A., Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista Y., Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James J., Colicchio, Tiago K., Cormont, Sylvie, Cossin, Sébastien, Craig, Jean B., Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Dionne, Audrey, Duan, Rui, Dubiel, Julien, DuVall, Scott L., Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert W., Ganslandt, Thomas, Barrio, Noelia García, Garmire, Lana X., Gehlenborg, Nils, Getzen, Emily J., Geva, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Han, Larry, Hanauer, David A., Haverkamp, Christian, Hazard, Derek Y., He, Bing, Henderson, Darren W., Hilka, Martin, Ho, Yuk-Lam, Holmes, John H., Hong, Chuan, Huling, Kenneth M., Hutch, Meghan R., Issitt, Richard W., Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Keller, Mark S., Kennedy, Chris J., Key, Daniel A., Kirchoff, Katie, Klann, Jeffrey G., Kohane, Isaac S., Krantz, Ian D., Kraska, Detlef, Krishnamurthy, Ashok K., L'Yi, Sehi, Le, Trang T., Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Will Loh, Ne Hooi, Long, Qi, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine E., Mahmood, Sadiqa, Maidlow, Sarah E., Makoudjou, Adeline, Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Maram, Anupama, Martel, Patricia, Martins, Marcelo R., Marwaha, Jayson S., Masino, Aaron J., Mazzitelli, Maria, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos F., Moal, Bertrand, Ahooyi, Taha Mohseni, Moore, Jason H., Moraleda, Cinta, Morris, Jeffrey S., Morris, Michele, Moshal, Karyn L., Mousavi, Sajad, Mowery, Danielle L., Murad, Douglas A., Murphy, Shawn N., Naughton, Thomas P., Breda Neto, Carlos Tadeu, Neuraz, Antoine, Newburger, Jane, Ngiam, Kee Yuan, Njoroge, Wanjiku F.M., Norman, James B., Obeid, Jihad, Okoshi, Marina P., Olson, Karen L., Omenn, Gilbert S., Orlova, Nina, Ostasiewski, Brian D., Palmer, Nathan P., Paris, Nicolas, Patel, Lav P., Pedrera-Jiménez, Miguel, Pfaff, Emily R., Pfaff, Ashley C., Pillion, Danielle, Pizzimenti, Sara, Prokosch, Hans U., Prudente, Robson A., Prunotto, Andrea, Quirós-González, Víctor, Ramoni, Rachel B., Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina C.C., Sanz Vidorreta, Fernando J., Savino, Maria, Schriver, Emily R., Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil J., Serrano-Balazote, Pablo, Serre, Patricia, Serret-Larmande, Arnaud, Shah, Mohsin, Hossein Abad, Zahra Shakeri, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, South, Andrew M., Spiridou, Anastasia, Strasser, Zachary H., Tan, Amelia L.M., Tan, Bryce W.Q., Tan, Byorn W.L., Tanni, Suzana E., Taylor, Deanne M., Terriza-Torres, Ana I., Tibollo, Valentina, Tippmann, Patric, Toh, Emma M.S., Torti, Carlo, Trecarichi, Enrico M., Tseng, Yi-Ju, Vallejos, Andrew K., Varoquaux, Gael, Vella, Margaret E., Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar B., Waitman, Lemuel R., Wang, Xuan, Wassermann, Demian, Weber, Griffin M., Wolkewitz, Martin, Wong, Scott, Xia, Zongqi, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zambelli, Alberto, Zhang, Harrison G., Zo¨ller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Mesa, Rebecca, and Verdy, Guillame

Published
2023
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16. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

Author

Brat, Gabriel A, Weber, Griffin M, Gehlenborg, Nils, Avillach, Paul, Palmer, Nathan P, Chiovato, Luca, Cimino, James, Waitman, Lemuel R, Omenn, Gilbert S, Malovini, Alberto, Moore, Jason H, Beaulieu-Jones, Brett K, Tibollo, Valentina, Murphy, Shawn N, Yi, Sehi L’, Keller, Mark S, Bellazzi, Riccardo, Hanauer, David A, Serret-Larmande, Arnaud, Gutierrez-Sacristan, Alba, Holmes, John J, Bell, Douglas S, Mandl, Kenneth D, Follett, Robert W, Klann, Jeffrey G, Murad, Douglas A, Scudeller, Luigia, Bucalo, Mauro, Kirchoff, Katie, Craig, Jean, Obeid, Jihad, Jouhet, Vianney, Griffier, Romain, Cossin, Sebastien, Moal, Bertrand, Patel, Lav P, Bellasi, Antonio, Prokosch, Hans U, Kraska, Detlef, Sliz, Piotr, Tan, Amelia LM, Ngiam, Kee Yuan, Zambelli, Alberto, Mowery, Danielle L, Schiver, Emily, Devkota, Batsal, Bradford, Robert L, Daniar, Mohamad, Daniel, Christel, Benoit, Vincent, Bey, Romain, Paris, Nicolas, Serre, Patricia, Orlova, Nina, Dubiel, Julien, Hilka, Martin, Jannot, Anne Sophie, Breant, Stephane, Leblanc, Judith, Griffon, Nicolas, Burgun, Anita, Bernaux, Melodie, Sandrin, Arnaud, Salamanca, Elisa, Cormont, Sylvie, Ganslandt, Thomas, Gradinger, Tobias, Champ, Julien, Boeker, Martin, Martel, Patricia, Esteve, Loic, Gramfort, Alexandre, Grisel, Olivier, Leprovost, Damien, Moreau, Thomas, Varoquaux, Gael, Vie, Jill-Jênn, Wassermann, Demian, Mensch, Arthur, Caucheteux, Charlotte, Haverkamp, Christian, Lemaitre, Guillaume, Bosari, Silvano, Krantz, Ian D, South, Andrew, Cai, Tianxi, and Kohane, Isaac S

Subjects
Health Services and Systems, Health Sciences, Infectious Diseases, Coronaviruses, Emerging Infectious Diseases, Generic health relevance, Good Health and Well Being, Databases, Outcomes research, Viral infection, Health services and systems
Abstract

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Published
2020

17. Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data

Author

Beam, Andrew L., Kompa, Benjamin, Schmaltz, Allen, Fried, Inbar, Weber, Griffin, Palmer, Nathan P., Shi, Xu, Cai, Tianxi, and Kohane, Isaac S.

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of embeddings for medical concepts learned using an extremely large collection of multimodal medical data. Leaning on recent theoretical insights, we demonstrate how an insurance claims database of 60 million members, a collection of 20 million clinical notes, and 1.7 million full text biomedical journal articles can be combined to embed concepts into a common space, resulting in the largest ever set of embeddings for 108,477 medical concepts. To evaluate our approach, we present a new benchmark methodology based on statistical power specifically designed to test embeddings of medical concepts. Our approach, called cui2vec, attains state-of-the-art performance relative to previous methods in most instances. Finally, we provide a downloadable set of pre-trained embeddings for other researchers to use, as well as an online tool for interactive exploration of the cui2vec embeddings

Published
2018

18. A Fast Divide-and-Conquer Sparse Cox Regression

Author

Wang, Yan, Palmer, Nathan, Di, Qian, Schwartz, Joel, Kohane, Isaac, and Cai, Tianxi

Subjects
Statistics - Computation, Statistics - Applications
Abstract

We propose a computationally and statistically efficient divide-and-conquer (DAC) algorithm to fit sparse Cox regression to massive datasets where the sample size $n_0$ is exceedingly large and the covariate dimension $p$ is not small but $n_0\gg p$. The proposed algorithm achieves computational efficiency through a one-step linear approximation followed by a least square approximation to the partial likelihood (PL). These sequences of linearization enable us to maximize the PL with only a small subset and perform penalized estimation via a fast approximation to the PL. The algorithm is applicable for the analysis of both time-independent and time-dependent survival data. Simulations suggest that the proposed DAC algorithm substantially outperforms the full sample-based estimators and the existing DAC algorithm with respect to the computational speed, while it achieves similar statistical efficiency as the full sample-based estimators. The proposed algorithm was applied to an extraordinarily large time-independent survival dataset and an extraordinarily large time-dependent survival dataset for the prediction of heart failure-specific readmission within 30 days among Medicare heart failure patients.

Published
2018

19. Immune-orthogonal orthologues of AAV capsids and of Cas9 circumvent the immune response to the administration of gene therapy

Author

Moreno, Ana M, Palmer, Nathan, Alemán, Fernando, Chen, Genghao, Pla, Andrew, Jiang, Ning, Leong Chew, Wei, Law, Mansun, and Mali, Prashant

Subjects
Engineering, Biomedical Engineering, Genetics, Biotechnology, Gene Therapy, 1.1 Normal biological development and functioning, Development of treatments and therapeutic interventions, 5.2 Cellular and gene therapies, Underpinning research, Inflammatory and immune system, Adaptive Immunity, Animals, CRISPR-Cas Systems, Capsid, Dependovirus, Gene Editing, Genetic Therapy, Genetic Vectors, Male, Mice, Mice, Inbred BALB C, Mice, Inbred C57BL, Protein Engineering, Biomedical engineering
Abstract

Protein-based therapeutics can activate the adaptive immune system, leading to the production of neutralizing antibodies and the clearance of the treated cells mediated by cytotoxic T cells. Here, we show that the sequential use of immune-orthogonal orthologues of CRISPR-associated protein 9 (Cas9) and adeno-associated viruses (AAVs) evades adaptive immune responses and enables effective gene editing using repeated dosing. We compared total sequence similarities and predicted binding strengths to class-I and class-II major histocompatibility complex (MHC) proteins for 284 DNA-targeting and 84 RNA-targeting CRISPR effectors and 167 AAV VP1-capsid-protein orthologues. We predict the absence of cross-reactive immune responses for 79% of the DNA-targeting Cas orthologues-which we validated for three Cas9 orthologues in mice-yet we anticipate broad immune cross-reactivity among the AAV serotypes. We also show that efficacious in vivo gene editing is uncompromised when using multiple dosing with orthologues of AAVs and Cas9 in mice that were previously immunized against the AAV vector and the Cas9 cargo. Multiple dosing with protein orthologues may allow for sequential regimens of protein therapeutics that circumvent pre-existing immunity or induced immunity.

Published
2019

20. SurvMaximin: Robust federated approach to transporting survival risk prediction models

Author

Wang, Xuan, Zhang, Harrison G., Xiong, Xin, Hong, Chuan, Weber, Griffin M., Brat, Gabriel A., Bonzel, Clara-Lea, Luo, Yuan, Duan, Rui, Palmer, Nathan P., Hutch, Meghan R., Gutiérrez-Sacristán, Alba, Bellazzi, Riccardo, Chiovato, Luca, Cho, Kelly, Dagliati, Arianna, Estiri, Hossein, García-Barrio, Noelia, Griffier, Romain, Hanauer, David A., Ho, Yuk-Lam, Holmes, John H., Keller, Mark S., Klann MEng, Jeffrey G., L'Yi, Sehi, Lozano-Zahonero, Sara, Maidlow, Sarah E., Makoudjou, Adeline, Malovini, Alberto, Moal, Bertrand, Moore, Jason H., Morris, Michele, Mowery, Danielle L., Murphy, Shawn N, Neuraz, Antoine, Yuan Ngiam, Kee, Omenn, Gilbert S., Patel, Lav P., Pedrera-Jiménez, Miguel, Prunotto, Andrea, Jebathilagam Samayamuthu, Malarkodi, Sanz Vidorreta, Fernando J, Schriver, Emily R., Schubert, Petra, Serrano-Balazote, Pablo, South, Andrew M., Tan, Amelia L.M., Tan, Byorn W.L., Tibollo, Valentina, Tippmann, Patric, Visweswaran, Shyam, Xia, Zongqi, Yuan, William, Zöller, Daniela, Kohane, Isaac S., Avillach, Paul, Guo, Zijian, and Cai, Tianxi

Published
2022
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21. Atomistic simulation studies of storage and ageing behaviour of plutonium dioxide and mixed oxide fuel

Author

Palmer, Nathan Alan

Subjects
540, QC Physics, TK Electrical engineering. Electronics Nuclear engineering
Abstract

Many of the fundamental aspects of the ageing of plutonium (Pu) in storage conditions are of theoretical interest and practical importance. The UK has a large Pu stockpile in interim storage at Sellafield, Cumbria. The aim of this research project was to employ atomistic simulations to study the properties and ageing behaviour of plutonium dioxide (PuO\(_2\)). A range of properties of PuO\(_2\) were predicted using bulk static lattice, surface and molecular dynamics simulations. These revealed that the oxygen terminated (111) is the most stable surface and that oxygen Frenkel pairs and Schottky defects are energetically favoured. Modelling of helium incorporation showed that helium trapping is effective in octahedral interstitial sites and Schottky defects, in particular on the (111) surface, with helium segregation predicted. In addition, molecular dynamics predicted thermal properties and 1 keV cascades in PuO\(_2\) varying the temperature, showing it be a thermally and radiation resilient material. Given its technological importance, mixed oxide (MOX) fuel was also modelled; with bulk lattice properties predicted varying the Pu content, showing it obeys Vegard's law. Intrinsic defects and helium incorporation was modelled in 9% Pu doped MOX fuel, of relevance to nuclear reactors.

Published
2019

22. Synergistic interaction between wheat streak mosaic virus and Triticum mosaic virus modulates wheat transcriptome to favor disease severity.

Author

Nunna, Haritha, Palmer, Nathan A., Sarath, Gautam, Wegulo, Stephen N., and Tatineni, Satyanarayana

Subjects
TRANSCRIPTION factors, MOSAIC diseases, VIRUS diseases, PLANT diseases, GENE expression profiling, GENE regulatory networks
Abstract

Wheat streak mosaic virus (WSMV; Tritimovirus tritici) and Triticum mosaic virus (TriMV; Poacevirus tritici), the type members of the genera Tritimovirus and Poacevirus , respectively, in the family Potyviridae , are economically important wheat viruses in the Great Plains region of the USA. Co-infection of wheat by WSMV and TriMV results in disease synergism. Wheat transcriptome from singly (WSMV or TriMV) and doubly (WSMV+TriMV) infected upper uninoculated leaves were analyzed by RNA-Seq at 9, 12, and 21 days postinoculation. A total of 31,754 differentially expressed wheat genes were identified among all comparisons. Weighted gene co-expression network analysis resulted in 11 co-expression modules that broadly indicated gene expression profiles attributable to control, single, and double infections. Gene ontology, protein domain and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis revealed that genes specifically related to photosynthesis, growth, stress, senescence, and defense were differentially enriched. Analyses of transcription factor families indicated that genes encoding MADS-Box and ARFs were strongly enriched in control plants, moderately repressed in TriMV-infected plants, and more strongly repressed in WSMV- and doubly-infected plants, whereas genes encoding WRKYs and NACs were more enriched in WSMV or doubly infected plants. Synergistic interactions between WSMV and TriMV drastically enhanced disease phenotype compared to individual virus infections. The progression of disease phenotype was correlated to transcriptomic changes, indicating the strong disruption to plant metabolism and likely channeling of energy and metabolites for viral replication. There also appeared to be a connection between viral replication and plastid health, with stronger downregulation of genes needed for chloroplast functions and integrity and increased synergism between TriMV and WSMV. This study provides an overview of transcriptomic changes distinctly influenced by TriMV and WSMV either singly or in combination and provides a good correlation between specific transcription factors and genes associated with metabolism to observed phenotypic changes in plant growth and disease synergism. [ABSTRACT FROM AUTHOR]

Published
2025
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29. Doubly robust matching estimators for high dimensional confounding adjustment

Author

Antonelli, Joseph, Cefalu, Matthew, Palmer, Nathan, and Agniel, Denis

Subjects
Statistics - Methodology
Abstract

Valid estimation of treatment effects from observational data requires proper control of confounding. If the number of covariates is large relative to the number of observations, then controlling for all available covariates is infeasible. In cases where a sparsity condition holds, variable selection or penalization can reduce the dimension of the covariate space in a manner that allows for valid estimation of treatment effects. In this article, we propose matching on both the estimated propensity score and the estimated prognostic scores when the number of covariates is large relative to the number of observations. We derive asymptotic results for the matching estimator and show that it is doubly robust, in the sense that only one of the two score models need be correct to obtain a consistent estimator. We show via simulation its effectiveness in controlling for confounding and highlight its potential to address nonlinear confounding. Finally, we apply the proposed procedure to analyze the effect of gender on prescription opioid use using insurance claims data.

Published
2016

36. Identification, characterization, and gene expression analysis of nucleotide binding site (NB)-type resistance gene homologues in switchgrass

Author

Frazier, Taylor P, Palmer, Nathan A, Xie, Fuliang, Tobias, Christian M, Donze-Reiner, Teresa J, Bombarely, Aureliano, Childs, Kevin L, Shu, Shengqiang, Jenkins, Jerry W, Schmutz, Jeremy, Zhang, Baohong, Sarath, Gautam, and Zhao, Bingyu

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Alleles, Amino Acid Sequence, Computational Biology, Databases, Nucleic Acid, Disease Resistance, Gene Expression Profiling, Genes, Plant, Genetic Association Studies, Genetic Predisposition to Disease, Genome, Plant, Genomics, Panicum, Phylogeny, Polymorphism, Single Nucleotide, Position-Specific Scoring Matrices, Protein Interaction Domains and Motifs, Reproducibility of Results, Biofuel, Disease resistance, Gene expression, NB-LRR, Panicum virgatum, RNA-seq, SNP, Information and Computing Sciences, Medical and Health Sciences, Bioinformatics, Biological sciences, Biomedical and clinical sciences
Abstract

BackgroundSwitchgrass (Panicum virgatum L.) is a warm-season perennial grass that can be used as a second generation bioenergy crop. However, foliar fungal pathogens, like switchgrass rust, have the potential to significantly reduce switchgrass biomass yield. Despite its importance as a prominent bioenergy crop, a genome-wide comprehensive analysis of NB-LRR disease resistance genes has yet to be performed in switchgrass.ResultsIn this study, we used a homology-based computational approach to identify 1011 potential NB-LRR resistance gene homologs (RGHs) in the switchgrass genome (v 1.1). In addition, we identified 40 RGHs that potentially contain unique domains including major sperm protein domain, jacalin-like binding domain, calmodulin-like binding, and thioredoxin. RNA-sequencing analysis of leaf tissue from 'Alamo', a rust-resistant switchgrass cultivar, and 'Dacotah', a rust-susceptible switchgrass cultivar, identified 2634 high quality variants in the RGHs between the two cultivars. RNA-sequencing data from field-grown cultivar 'Summer' plants indicated that the expression of some of these RGHs was developmentally regulated.ConclusionsOur results provide useful insight into the molecular structure, distribution, and expression patterns of members of the NB-LRR gene family in switchgrass. These results also provide a foundation for future work aimed at elucidating the molecular mechanisms underlying disease resistance in this important bioenergy crop.

Published
2016

37. Graduating USG officials reflect on their incumbency - The Record

Author

Palmer, Nathan

Subjects
News, opinion and commentary, Sports and fitness
Abstract

Byline: Nathan Palmer content'class='sno-story-body-content sno-no-cap'> The student body has spoken! The United Student Government held their annual elections on March 18 - 21 in the Campbell Student Union. The results [...]

Published
2024

38. A multidimensional precision medicine approach identifies an autism subtype characterized by dyslipidemia

Author

Luo, Yuan, Eran, Alal, Palmer, Nathan, Avillach, Paul, Levy-Moonshine, Ami, Szolovits, Peter, and Kohane, Isaac S.

Subjects
Identification and classification, Usage, Complications and side effects, Genetic aspects, Risk factors, Precision medicine -- Usage, Autism -- Identification and classification -- Risk factors -- Genetic aspects, Dyslipidemias -- Genetic aspects -- Complications and side effects
Abstract

Author(s): Yuan Luo [sup.1] [sup.2] [sup.3] [sup.4] , Alal Eran [sup.5] [sup.6] [sup.7] , Nathan Palmer [sup.6] , Paul Avillach [sup.6] , Ami Levy-Moonshine [sup.8] , Peter Szolovits [sup.9] , [...], The promise of precision medicine lies in data diversity. More than the sheer size of biomedical data, it is the layering of multiple data modalities, offering complementary perspectives, that is thought to enable the identification of patient subgroups with shared pathophysiology. In the present study, we use autism to test this notion. By combining healthcare claims, electronic health records, familial whole-exome sequences and neurodevelopmental gene expression patterns, we identified a subgroup of patients with dyslipidemia-associated autism. Integration of large datasets of familial whole-exome sequencing, neurodevelopmental gene expression, electronic health records and healthcare claims led to the identification of a subtype of autism spectrum disorder that is associated with lipid dysregulation.

Published
2020
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45. Accelerating diagnosis of Parkinson’s disease through risk prediction

Author

Yuan, William, Beaulieu-Jones, Brett, Krolewski, Richard, Palmer, Nathan, Veyrat-Follet, Christine, Frau, Francesca, Cohen, Caroline, Bozzi, Sylvie, Cogswell, Meaghan, Kumar, Dinesh, Coulouvrat, Catherine, Leroy, Bruno, Fischer, Tanya Z., Sardi, S. Pablo, Chandross, Karen J., Rubin, Lee L., Wills, Anne-Marie, Kohane, Isaac, and Lipnick, Scott L.

Published
2021
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46. Engineering Progressively Deimmunized and Redosable CRISPR-Cas Gene Therapies

Author

Palmer, Nathan David

Subjects
Biology
Abstract

The advent of genomic editing technology offers the tantalizing promise of precisely treating a variety of disease states at the genetic level. CRISPR-Cas has democratized this capability in laboratory settings across the world. However, developing these exciting tools into therapies appropriate for clinical use involves several challenges including the potential recognition of foreign components such as the Cas effector proteins by the adaptive immune system which may limit the effectiveness of gene therapy. Here I approach this problem by first characterizing the immune response to AAV-delivered CRISPR-Cas in a mouse model targeting the PCSK9 protein, known to play a major role in atherosclerotic disease. Next, I propose and test a method to enable redosing by leveraging immune orthogonal components of AAV-CRISPR-Cas therapeutics, systematically defining the capability of this approach to avoid immune-mediated inhibition of therapeutic efficacy. Finally, in an effort towards building a single deimmunized Cas9 protein usable across diverse contexts while circumventing pre- existing immunity, I develop a scalable and portable long-range multiplexed protein engineering platform to progressively de-immunize target proteins by abolishing the most immunogenic MHC-restricted epitopes without disrupting protein function. By applying this technique to Cas9, I identify a Cas9 variant with 7 simultaneously deimmunized epitopes that retains near wild-type functionality for both direct editing and gene activation/repression. Taken together, this work represents a meaningful step towards unlocking the potential to precisely edit genomes and gene expression in the clinical setting.

Published
2022

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