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Three Dimensions of Reproducibility in Natural Language Processing.

LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources and Evaluation

Cohen KB, Xia J, Zweigenbaum P, Callahan TJ, Hargraves O, Goss F, Ide N, Névéol A, Grouin C, Hunter LE.
PMID: 29911205
LREC Int Conf Lang Resour Eval. 2018 May;2018:156-165.

Despite considerable recent attention to problems with reproducibility of scientific research, there is a striking lack of agreement about the definition of the term. That is a problem, because the lack of a consensus definition makes it difficult to...

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Cohen KB, Xia J, Zweigenbaum P, et al. Three Dimensions of Reproducibility in Natural Language Processing. LREC Int Conf Lang Resour Eval. 2018;2018:156-165
Cohen, K. B., Xia, J., Zweigenbaum, P., Callahan, T. J., Hargraves, O., Goss, F., Ide, N., Névéol, A., Grouin, C., & Hunter, L. E. (2018). Three Dimensions of Reproducibility in Natural Language Processing. LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources and Evaluation, 2018156-165.
Cohen, K Bretonnel, et al. "Three Dimensions of Reproducibility in Natural Language Processing." LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources and Evaluation vol. 2018 (2018): 156-165.
Cohen KB, Xia J, Zweigenbaum P, Callahan TJ, Hargraves O, Goss F, Ide N, Névéol A, Grouin C, Hunter LE. Three Dimensions of Reproducibility in Natural Language Processing. LREC Int Conf Lang Resour Eval. 2018 May;2018:156-165. PMID: 29911205; PMCID: PMC5998676.
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KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response.

Patterns (New York, N.Y.)

Reese JT, Unni D, Callahan TJ, Cappelletti L, Ravanmehr V, Carbon S, Shefchek KA, Good BM, Balhoff JP, Fontana T, Blau H, Matentzoglu N, Harris NL, Munoz-Torres MC, Haendel MA, Robinson PN, Joachimiak MP, Mungall CJ.
PMID: 33196056
Patterns (N Y). 2021 Jan 08;2(1):100155. doi: 10.1016/j.patter.2020.100155. Epub 2020 Nov 09.

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult...

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Reese JT, Unni D, Callahan TJ, et al. KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response. Patterns (N Y). 2020;2(1):100155doi: 10.1016/j.patter.2020.100155.
Reese, J. T., Unni, D., Callahan, T. J., Cappelletti, L., Ravanmehr, V., Carbon, S., Shefchek, K. A., Good, B. M., Balhoff, J. P., Fontana, T., Blau, H., Matentzoglu, N., Harris, N. L., Munoz-Torres, M. C., Haendel, M. A., Robinson, P. N., Joachimiak, M. P., & Mungall, C. J. (2021). KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response. Patterns (New York, N.Y.), 2(1), 100155. https://doi.org/10.1016/j.patter.2020.100155
Reese, Justin T, et al. "KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response." Patterns (New York, N.Y.) vol. 2,1 (2021): 100155. doi: https://doi.org/10.1016/j.patter.2020.100155
Reese JT, Unni D, Callahan TJ, Cappelletti L, Ravanmehr V, Carbon S, Shefchek KA, Good BM, Balhoff JP, Fontana T, Blau H, Matentzoglu N, Harris NL, Munoz-Torres MC, Haendel MA, Robinson PN, Joachimiak MP, Mungall CJ. KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response. Patterns (N Y). 2021 Jan 08;2(1):100155. doi: 10.1016/j.patter.2020.100155. Epub 2020 Nov 09. PMID: 33196056; PMCID: PMC7649624.
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A Data Element-Function Conceptual Model for Data Quality Checks.

EGEMS (Washington, DC)

Rogers JR, Callahan TJ, Kang T, Bauck A, Khare R, Brown JS, Kahn MG, Weng C.
PMID: 31065558
EGEMS (Wash DC). 2019 Apr 23;7(1):17. doi: 10.5334/egems.289.

INTRODUCTION: In aggregate, existing data quality (DQ) checks are currently represented in heterogeneous formats, making it difficult to compare, categorize, and index checks. This study contributes a data element-function conceptual model to facilitate the categorization and indexing of DQ...

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Rogers JR, Callahan TJ, Kang T, et al. A Data Element-Function Conceptual Model for Data Quality Checks. EGEMS (Wash DC). 2019;7(1):17doi: 10.5334/egems.289.
Rogers, J. R., Callahan, T. J., Kang, T., Bauck, A., Khare, R., Brown, J. S., Kahn, M. G., & Weng, C. (2019). A Data Element-Function Conceptual Model for Data Quality Checks. EGEMS (Washington, DC), 7(1), 17. https://doi.org/10.5334/egems.289
Rogers, James R, et al. "A Data Element-Function Conceptual Model for Data Quality Checks." EGEMS (Washington, DC) vol. 7,1 (2019): 17. doi: https://doi.org/10.5334/egems.289
Rogers JR, Callahan TJ, Kang T, Bauck A, Khare R, Brown JS, Kahn MG, Weng C. A Data Element-Function Conceptual Model for Data Quality Checks. EGEMS (Wash DC). 2019 Apr 23;7(1):17. doi: 10.5334/egems.289. PMID: 31065558; PMCID: PMC6484368.
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Rapid identification of thousands of copperhead snake (Agkistrodon contortrix) microsatellite loci from modest amounts of 454 shotgun genome sequence.

Molecular ecology resources

Castoe TA, Poole AW, Gu W, Jason de Koning AP, Daza JM, Smith EN, Pollock DD.
PMID: 21565030
Mol Ecol Resour. 2010 Mar;10(2):341-7. doi: 10.1111/j.1755-0998.2009.02750.x. Epub 2009 Jul 30.

Optimal integration of next-generation sequencing into mainstream research requires re-evaluation of how problems can be reasonably overcome and what questions can be asked. One potential application is the rapid acquisition of genomic information to identify microsatellite loci for evolutionary,...

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Castoe TA, Poole AW, Gu W, et al. Rapid identification of thousands of copperhead snake (Agkistrodon contortrix) microsatellite loci from modest amounts of 454 shotgun genome sequence. Mol Ecol Resour. 2009;10(2):341-7doi: 10.1111/j.1755-0998.2009.02750.x.
Castoe, T. A., Poole, A. W., Gu, W., Jason de Koning, A. P., Daza, J. M., Smith, E. N., & Pollock, D. D. (2010). Rapid identification of thousands of copperhead snake (Agkistrodon contortrix) microsatellite loci from modest amounts of 454 shotgun genome sequence. Molecular ecology resources, 10(2), 341-7. https://doi.org/10.1111/j.1755-0998.2009.02750.x
Castoe, Todd A, et al. "Rapid identification of thousands of copperhead snake (Agkistrodon contortrix) microsatellite loci from modest amounts of 454 shotgun genome sequence." Molecular ecology resources vol. 10,2 (2010): 341-7. doi: https://doi.org/10.1111/j.1755-0998.2009.02750.x
Castoe TA, Poole AW, Gu W, Jason de Koning AP, Daza JM, Smith EN, Pollock DD. Rapid identification of thousands of copperhead snake (Agkistrodon contortrix) microsatellite loci from modest amounts of 454 shotgun genome sequence. Mol Ecol Resour. 2010 Mar;10(2):341-7. doi: 10.1111/j.1755-0998.2009.02750.x. Epub 2009 Jul 30. PMID: 21565030; PMCID: PMC5172459.
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HIGH-PRECISION BIOLOGICAL EVENT EXTRACTION: EFFECTS OF SYSTEM AND OF DATA.

Computational intelligence

Cohen KB, Verspoor K, Johnson HL, Roeder C, Ogren PV, Baumgartner WA, White E, Tipney H, Hunter L.
PMID: 25937701
Comput Intell. 2011 Nov;27(4):681-701. doi: 10.1111/j.1467-8640.2011.00405.x.

We approached the problems of event detection, argument identification, and negation and speculation detection in the BioNLP'09 information extraction challenge through concept recognition and analysis. Our methodology involved using the OpenDMAP semantic parser with manually written rules. The original...

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Cohen KB, Verspoor K, Johnson HL, et al. HIGH-PRECISION BIOLOGICAL EVENT EXTRACTION: EFFECTS OF SYSTEM AND OF DATA. Comput Intell. 2011;27(4):681-701doi: 10.1111/j.1467-8640.2011.00405.x.
Cohen, K. B., Verspoor, K., Johnson, H. L., Roeder, C., Ogren, P. V., Baumgartner, W. A., White, E., Tipney, H., & Hunter, L. (2011). HIGH-PRECISION BIOLOGICAL EVENT EXTRACTION: EFFECTS OF SYSTEM AND OF DATA. Computational intelligence, 27(4), 681-701. https://doi.org/10.1111/j.1467-8640.2011.00405.x
Cohen, K Bretonnel, et al. "HIGH-PRECISION BIOLOGICAL EVENT EXTRACTION: EFFECTS OF SYSTEM AND OF DATA." Computational intelligence vol. 27,4 (2011): 681-701. doi: https://doi.org/10.1111/j.1467-8640.2011.00405.x
Cohen KB, Verspoor K, Johnson HL, Roeder C, Ogren PV, Baumgartner WA, White E, Tipney H, Hunter L. HIGH-PRECISION BIOLOGICAL EVENT EXTRACTION: EFFECTS OF SYSTEM AND OF DATA. Comput Intell. 2011 Nov;27(4):681-701. doi: 10.1111/j.1467-8640.2011.00405.x. PMID: 25937701; PMCID: PMC4414063.
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Knowledge-Based Biomedical Data Science.

Annual review of biomedical data science

Callahan TJ, Tripodi IJ, Pielke-Lombardo H, Hunter LE.
PMID: 33954284
Annu Rev Biomed Data Sci. 2020 Jul;3:23-41. doi: 10.1146/annurev-biodatasci-010820-091627. Epub 2020 Apr 07.

Knowledge-based biomedical data science involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we...

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Callahan TJ, Tripodi IJ, Pielke-Lombardo H, et al. Knowledge-Based Biomedical Data Science. Annu Rev Biomed Data Sci. 2020;3:23-41doi: 10.1146/annurev-biodatasci-010820-091627.
Callahan, T. J., Tripodi, I. J., Pielke-Lombardo, H., & Hunter, L. E. (2020). Knowledge-Based Biomedical Data Science. Annual review of biomedical data science, 323-41. https://doi.org/10.1146/annurev-biodatasci-010820-091627
Callahan, Tiffany J, et al. "Knowledge-Based Biomedical Data Science." Annual review of biomedical data science vol. 3 (2020): 23-41. doi: https://doi.org/10.1146/annurev-biodatasci-010820-091627
Callahan TJ, Tripodi IJ, Pielke-Lombardo H, Hunter LE. Knowledge-Based Biomedical Data Science. Annu Rev Biomed Data Sci. 2020 Jul;3:23-41. doi: 10.1146/annurev-biodatasci-010820-091627. Epub 2020 Apr 07. PMID: 33954284; PMCID: PMC8095730.
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KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response.

bioRxiv : the preprint server for biology

Reese J, Unni D, Callahan TJ, Cappelletti L, Ravanmehr V, Carbon S, Fontana T, Blau H, Matentzoglu N, Harris NL, Munoz-Torres MC, Robinson PN, Joachimiak MP, Mungall CJ.
PMID: 32839776
bioRxiv. 2020 Aug 18; doi: 10.1101/2020.08.17.254839.

Integrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (COVID-19) is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this...

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Reese J, Unni D, Callahan TJ, et al. KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response. bioRxiv. 2020;doi: 10.1101/2020.08.17.254839.
Reese, J., Unni, D., Callahan, T. J., Cappelletti, L., Ravanmehr, V., Carbon, S., Fontana, T., Blau, H., Matentzoglu, N., Harris, N. L., Munoz-Torres, M. C., Robinson, P. N., Joachimiak, M. P., & Mungall, C. J. (2020). KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response. bioRxiv : the preprint server for biology, . https://doi.org/10.1101/2020.08.17.254839
Reese, Justin, et al. "KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response." bioRxiv : the preprint server for biology vol. (2020). doi: https://doi.org/10.1101/2020.08.17.254839
Reese J, Unni D, Callahan TJ, Cappelletti L, Ravanmehr V, Carbon S, Fontana T, Blau H, Matentzoglu N, Harris NL, Munoz-Torres MC, Robinson PN, Joachimiak MP, Mungall CJ. KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response. bioRxiv. 2020 Aug 18; doi: 10.1101/2020.08.17.254839. PMID: 32839776; PMCID: PMC7444288.
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ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine.

Applied sciences (Basel, Switzerland)

Stevens L, Kao D, Hall J, Görg C, Abdo K, Linstead E.
PMID: 33664984
Appl Sci (Basel). 2020 May;10(9). doi: 10.3390/app10093309. Epub 2020 May 09.

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool...

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Stevens L, Kao D, Hall J, et al. ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine. Appl Sci (Basel). 2020;10(9)doi: 10.3390/app10093309.
Stevens, L., Kao, D., Hall, J., Görg, C., Abdo, K., & Linstead, E. (2020). ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine. Applied sciences (Basel, Switzerland), 10(9), . https://doi.org/10.3390/app10093309
Stevens, Laura, et al. "ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine." Applied sciences (Basel, Switzerland) vol. 10,9 (2020). doi: https://doi.org/10.3390/app10093309
Stevens L, Kao D, Hall J, Görg C, Abdo K, Linstead E. ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine. Appl Sci (Basel). 2020 May;10(9). doi: 10.3390/app10093309. Epub 2020 May 09. PMID: 33664984; PMCID: PMC7928533.
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Adaptive molecular convergence: Molecular evolution versus molecular phylogenetics.

Communicative & integrative biology

Castoe TA, de Koning AP, Pollock DD.
PMID: 20539788
Commun Integr Biol. 2010 Jan;3(1):67-9. doi: 10.4161/cib.3.1.10174.

Definitive identification of convergent evolution, the acquisition of the same biological trait in unrelated lineages, provides one of the most compelling sources of evidence for natural selection. Although numerous examples of convergent morphological evolution are well known (such as...

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Castoe TA, de Koning AP, Pollock DD. Adaptive molecular convergence: Molecular evolution versus molecular phylogenetics. Commun Integr Biol. 2010;3(1):67-9doi: 10.4161/cib.3.1.10174.
Castoe, T. A., de Koning, A. P., & Pollock, D. D. (2010). Adaptive molecular convergence: Molecular evolution versus molecular phylogenetics. Communicative & integrative biology, 3(1), 67-9. https://doi.org/10.4161/cib.3.1.10174
Castoe, Todd A, et al. "Adaptive molecular convergence: Molecular evolution versus molecular phylogenetics." Communicative & integrative biology vol. 3,1 (2010): 67-9. doi: https://doi.org/10.4161/cib.3.1.10174
Castoe TA, de Koning AP, Pollock DD. Adaptive molecular convergence: Molecular evolution versus molecular phylogenetics. Commun Integr Biol. 2010 Jan;3(1):67-9. doi: 10.4161/cib.3.1.10174. PMID: 20539788; PMCID: PMC2881246.
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Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct.

Journal of biomedical semantics

Funk CS, Kahanda I, Ben-Hur A, Verspoor KM.
PMID: 26005564
J Biomed Semantics. 2015 Mar 18;6:9. doi: 10.1186/s13326-015-0006-4. eCollection 2015.

Most computational methods that predict protein function do not take advantage of the large amount of information contained in the biomedical literature. In this work we evaluate both ontology term co-mention and bag-of-words features mined from the biomedical literature...

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Funk CS, Kahanda I, Ben-Hur A, et al. Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct. J Biomed Semantics. 2015;6:9doi: 10.1186/s13326-015-0006-4.
Funk, C. S., Kahanda, I., Ben-Hur, A., & Verspoor, K. M. (2015). Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct. Journal of biomedical semantics, 69. https://doi.org/10.1186/s13326-015-0006-4
Funk, Christopher S, et al. "Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct." Journal of biomedical semantics vol. 6 (2015): 9. doi: https://doi.org/10.1186/s13326-015-0006-4
Funk CS, Kahanda I, Ben-Hur A, Verspoor KM. Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct. J Biomed Semantics. 2015 Mar 18;6:9. doi: 10.1186/s13326-015-0006-4. eCollection 2015. PMID: 26005564; PMCID: PMC4441003.
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Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery.

NPJ digital medicine

Zhang XA, Yates A, Vasilevsky N, Gourdine JP, Callahan TJ, Carmody LC, Danis D, Joachimiak MP, Ravanmehr V, Pfaff ER, Champion J, Robasky K, Xu H, Fecho K, Walton NA, Zhu RL, Ramsdill J, Mungall CJ, Köhler S, Haendel MA, McDonald CJ, Vreeman DJ, Peden DB, Bennett TD, Feinstein JA, Martin B, Stefanski AL, Hunter LE, Chute CG, Robinson PN.
PMID: 31119199
NPJ Digit Med. 2019;2. doi: 10.1038/s41746-019-0110-4. Epub 2019 May 02.

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational...

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Zhang XA, Yates A, Vasilevsky N, et al. Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery. NPJ Digit Med. 2019;2doi: 10.1038/s41746-019-0110-4.
Zhang, X. A., Yates, A., Vasilevsky, N., Gourdine, J. P., Callahan, T. J., Carmody, L. C., Danis, D., Joachimiak, M. P., Ravanmehr, V., Pfaff, E. R., Champion, J., Robasky, K., Xu, H., Fecho, K., Walton, N. A., Zhu, R. L., Ramsdill, J., Mungall, C. J., Köhler, S., Haendel, M. A., McDonald, C. J., Vreeman, D. J., Peden, D. B., Bennett, T. D., Feinstein, J. A., Martin, B., Stefanski, A. L., Hunter, L. E., Chute, C. G., & Robinson, P. N. (2019). Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery. NPJ digital medicine, 2. https://doi.org/10.1038/s41746-019-0110-4
Zhang, Xingmin Aaron, et al. "Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery." NPJ digital medicine vol. 2 (2019). doi: https://doi.org/10.1038/s41746-019-0110-4
Zhang XA, Yates A, Vasilevsky N, Gourdine JP, Callahan TJ, Carmody LC, Danis D, Joachimiak MP, Ravanmehr V, Pfaff ER, Champion J, Robasky K, Xu H, Fecho K, Walton NA, Zhu RL, Ramsdill J, Mungall CJ, Köhler S, Haendel MA, McDonald CJ, Vreeman DJ, Peden DB, Bennett TD, Feinstein JA, Martin B, Stefanski AL, Hunter LE, Chute CG, Robinson PN. Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery. NPJ Digit Med. 2019;2. doi: 10.1038/s41746-019-0110-4. Epub 2019 May 02. PMID: 31119199; PMCID: PMC6527418.
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Draft Genome Sequence of Mycobacterium chelonae Type Strain ATCC 35752.

Genome announcements

Hasan NA, Davidson RM, de Moura VC, Garcia BJ, Reynolds PR, Epperson LE, Farias-Hesson E, DeGroote MA, Jackson M, Strong M.
PMID: 26021923
Genome Announc. 2015 May 28;3(3). doi: 10.1128/genomeA.00536-15.

Mycobacterium chelonae is a rapidly growing opportunistic nontuberculous mycobacterial (NTM) species that causes infections in humans and other hosts. Here, we report the draft genome sequence of Mycobacterium chelonae type strain ATCC 35752, consisting of 4.89 Mbp, 63.96% G+C...

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Hasan NA, Davidson RM, de Moura VC, et al. Draft Genome Sequence of Mycobacterium chelonae Type Strain ATCC 35752. Genome Announc. 2015;3(3)doi: 10.1128/genomeA.00536-15.
Hasan, N. A., Davidson, R. M., de Moura, V. C., Garcia, B. J., Reynolds, P. R., Epperson, L. E., Farias-Hesson, E., DeGroote, M. A., Jackson, M., & Strong, M. (2015). Draft Genome Sequence of Mycobacterium chelonae Type Strain ATCC 35752. Genome announcements, 3(3), . https://doi.org/10.1128/genomeA.00536-15
Hasan, Nabeeh A, et al. "Draft Genome Sequence of Mycobacterium chelonae Type Strain ATCC 35752." Genome announcements vol. 3,3 (2015). doi: https://doi.org/10.1128/genomeA.00536-15
Hasan NA, Davidson RM, de Moura VC, Garcia BJ, Reynolds PR, Epperson LE, Farias-Hesson E, DeGroote MA, Jackson M, Strong M. Draft Genome Sequence of Mycobacterium chelonae Type Strain ATCC 35752. Genome Announc. 2015 May 28;3(3). doi: 10.1128/genomeA.00536-15. PMID: 26021923; PMCID: PMC4447908.
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