Gastroenterology Fellowship and Postdoctoral Training in Omics and Statistics—Part I: Why Is It Needed?
Abstract A multitude of federally and industry-funded efforts are underway to generate and collect human, animal, microbial, and other sources of data on an unprecedented scale; the results are commonly referred to as “big data.” Often vaguely defined, big data refers to large and complex datasets c...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Alizadeh, Madeline [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 |
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| Übergeordnetes Werk: |
Enthalten in: Digestive diseases and sciences - Dordrecht : Springer Science + Business Media B.V., 1934, 69(2023), 1 vom: 02. Nov., Seite 18-21 |
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| Übergeordnetes Werk: |
volume:69 ; year:2023 ; number:1 ; day:02 ; month:11 ; pages:18-21 |
| Links: |
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| DOI / URN: |
10.1007/s10620-023-08136-4 |
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| Katalog-ID: |
SPR054380731 |
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| 520 | |a Abstract A multitude of federally and industry-funded efforts are underway to generate and collect human, animal, microbial, and other sources of data on an unprecedented scale; the results are commonly referred to as “big data.” Often vaguely defined, big data refers to large and complex datasets consisting of myriad datatypes that can be integrated to address complex questions. Big data offers a wealth of information that can be accessed only by those who pose the right questions and have sufficient technical knowhow and analytical skills. The intersection comprised of the gut-brain axis, the intestinal microbiome and multi-ome, and several other interconnected organ systems poses particular challenges and opportunities for those engaged in gastrointestinal and liver research. Unfortunately, there is currently a shortage of clinicians, scientists, and physician-scientists with the training needed to use and analyze big data at the scale necessary for widespread implementation of precision medicine. Here, we review the importance of training in the use of big data, the perils of insufficient training, and potential solutions that exist or can be developed to address the dearth of individuals in GI and hepatology research with the necessary level of big data expertise. | ||
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| 700 | 1 | |a Ravel, Jacques |4 aut | |
| 700 | 1 | |a Raufman, Jean-Pierre |4 aut | |
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10.1007/s10620-023-08136-4 doi (DE-627)SPR054380731 (SPR)s10620-023-08136-4-e DE-627 ger DE-627 rakwb eng Alizadeh, Madeline verfasserin (orcid)0000-0001-5650-3393 aut Gastroenterology Fellowship and Postdoctoral Training in Omics and Statistics—Part I: Why Is It Needed? 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 Abstract A multitude of federally and industry-funded efforts are underway to generate and collect human, animal, microbial, and other sources of data on an unprecedented scale; the results are commonly referred to as “big data.” Often vaguely defined, big data refers to large and complex datasets consisting of myriad datatypes that can be integrated to address complex questions. Big data offers a wealth of information that can be accessed only by those who pose the right questions and have sufficient technical knowhow and analytical skills. The intersection comprised of the gut-brain axis, the intestinal microbiome and multi-ome, and several other interconnected organ systems poses particular challenges and opportunities for those engaged in gastrointestinal and liver research. Unfortunately, there is currently a shortage of clinicians, scientists, and physician-scientists with the training needed to use and analyze big data at the scale necessary for widespread implementation of precision medicine. Here, we review the importance of training in the use of big data, the perils of insufficient training, and potential solutions that exist or can be developed to address the dearth of individuals in GI and hepatology research with the necessary level of big data expertise. Medical informatics (dpeaa)DE-He213 Medical education (dpeaa)DE-He213 Big data (dpeaa)DE-He213 Gastroenterology (dpeaa)DE-He213 Hepatology (dpeaa)DE-He213 Sampaio Moura, Natalia aut Schledwitz, Alyssa aut Patil, Seema A. aut Ravel, Jacques aut Raufman, Jean-Pierre aut Enthalten in Digestive diseases and sciences Dordrecht : Springer Science + Business Media B.V., 1934 69(2023), 1 vom: 02. Nov., Seite 18-21 (DE-627)320525384 (DE-600)2015102-0 1573-2568 nnns volume:69 year:2023 number:1 day:02 month:11 pages:18-21 https://dx.doi.org/10.1007/s10620-023-08136-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 69 2023 1 02 11 18-21 |
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10.1007/s10620-023-08136-4 doi (DE-627)SPR054380731 (SPR)s10620-023-08136-4-e DE-627 ger DE-627 rakwb eng Alizadeh, Madeline verfasserin (orcid)0000-0001-5650-3393 aut Gastroenterology Fellowship and Postdoctoral Training in Omics and Statistics—Part I: Why Is It Needed? 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 Abstract A multitude of federally and industry-funded efforts are underway to generate and collect human, animal, microbial, and other sources of data on an unprecedented scale; the results are commonly referred to as “big data.” Often vaguely defined, big data refers to large and complex datasets consisting of myriad datatypes that can be integrated to address complex questions. Big data offers a wealth of information that can be accessed only by those who pose the right questions and have sufficient technical knowhow and analytical skills. The intersection comprised of the gut-brain axis, the intestinal microbiome and multi-ome, and several other interconnected organ systems poses particular challenges and opportunities for those engaged in gastrointestinal and liver research. Unfortunately, there is currently a shortage of clinicians, scientists, and physician-scientists with the training needed to use and analyze big data at the scale necessary for widespread implementation of precision medicine. Here, we review the importance of training in the use of big data, the perils of insufficient training, and potential solutions that exist or can be developed to address the dearth of individuals in GI and hepatology research with the necessary level of big data expertise. Medical informatics (dpeaa)DE-He213 Medical education (dpeaa)DE-He213 Big data (dpeaa)DE-He213 Gastroenterology (dpeaa)DE-He213 Hepatology (dpeaa)DE-He213 Sampaio Moura, Natalia aut Schledwitz, Alyssa aut Patil, Seema A. aut Ravel, Jacques aut Raufman, Jean-Pierre aut Enthalten in Digestive diseases and sciences Dordrecht : Springer Science + Business Media B.V., 1934 69(2023), 1 vom: 02. Nov., Seite 18-21 (DE-627)320525384 (DE-600)2015102-0 1573-2568 nnns volume:69 year:2023 number:1 day:02 month:11 pages:18-21 https://dx.doi.org/10.1007/s10620-023-08136-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 69 2023 1 02 11 18-21 |
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10.1007/s10620-023-08136-4 doi (DE-627)SPR054380731 (SPR)s10620-023-08136-4-e DE-627 ger DE-627 rakwb eng Alizadeh, Madeline verfasserin (orcid)0000-0001-5650-3393 aut Gastroenterology Fellowship and Postdoctoral Training in Omics and Statistics—Part I: Why Is It Needed? 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 Abstract A multitude of federally and industry-funded efforts are underway to generate and collect human, animal, microbial, and other sources of data on an unprecedented scale; the results are commonly referred to as “big data.” Often vaguely defined, big data refers to large and complex datasets consisting of myriad datatypes that can be integrated to address complex questions. Big data offers a wealth of information that can be accessed only by those who pose the right questions and have sufficient technical knowhow and analytical skills. The intersection comprised of the gut-brain axis, the intestinal microbiome and multi-ome, and several other interconnected organ systems poses particular challenges and opportunities for those engaged in gastrointestinal and liver research. Unfortunately, there is currently a shortage of clinicians, scientists, and physician-scientists with the training needed to use and analyze big data at the scale necessary for widespread implementation of precision medicine. Here, we review the importance of training in the use of big data, the perils of insufficient training, and potential solutions that exist or can be developed to address the dearth of individuals in GI and hepatology research with the necessary level of big data expertise. Medical informatics (dpeaa)DE-He213 Medical education (dpeaa)DE-He213 Big data (dpeaa)DE-He213 Gastroenterology (dpeaa)DE-He213 Hepatology (dpeaa)DE-He213 Sampaio Moura, Natalia aut Schledwitz, Alyssa aut Patil, Seema A. aut Ravel, Jacques aut Raufman, Jean-Pierre aut Enthalten in Digestive diseases and sciences Dordrecht : Springer Science + Business Media B.V., 1934 69(2023), 1 vom: 02. Nov., Seite 18-21 (DE-627)320525384 (DE-600)2015102-0 1573-2568 nnns volume:69 year:2023 number:1 day:02 month:11 pages:18-21 https://dx.doi.org/10.1007/s10620-023-08136-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 69 2023 1 02 11 18-21 |
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10.1007/s10620-023-08136-4 doi (DE-627)SPR054380731 (SPR)s10620-023-08136-4-e DE-627 ger DE-627 rakwb eng Alizadeh, Madeline verfasserin (orcid)0000-0001-5650-3393 aut Gastroenterology Fellowship and Postdoctoral Training in Omics and Statistics—Part I: Why Is It Needed? 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 Abstract A multitude of federally and industry-funded efforts are underway to generate and collect human, animal, microbial, and other sources of data on an unprecedented scale; the results are commonly referred to as “big data.” Often vaguely defined, big data refers to large and complex datasets consisting of myriad datatypes that can be integrated to address complex questions. Big data offers a wealth of information that can be accessed only by those who pose the right questions and have sufficient technical knowhow and analytical skills. The intersection comprised of the gut-brain axis, the intestinal microbiome and multi-ome, and several other interconnected organ systems poses particular challenges and opportunities for those engaged in gastrointestinal and liver research. Unfortunately, there is currently a shortage of clinicians, scientists, and physician-scientists with the training needed to use and analyze big data at the scale necessary for widespread implementation of precision medicine. Here, we review the importance of training in the use of big data, the perils of insufficient training, and potential solutions that exist or can be developed to address the dearth of individuals in GI and hepatology research with the necessary level of big data expertise. Medical informatics (dpeaa)DE-He213 Medical education (dpeaa)DE-He213 Big data (dpeaa)DE-He213 Gastroenterology (dpeaa)DE-He213 Hepatology (dpeaa)DE-He213 Sampaio Moura, Natalia aut Schledwitz, Alyssa aut Patil, Seema A. aut Ravel, Jacques aut Raufman, Jean-Pierre aut Enthalten in Digestive diseases and sciences Dordrecht : Springer Science + Business Media B.V., 1934 69(2023), 1 vom: 02. Nov., Seite 18-21 (DE-627)320525384 (DE-600)2015102-0 1573-2568 nnns volume:69 year:2023 number:1 day:02 month:11 pages:18-21 https://dx.doi.org/10.1007/s10620-023-08136-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 69 2023 1 02 11 18-21 |
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10.1007/s10620-023-08136-4 doi (DE-627)SPR054380731 (SPR)s10620-023-08136-4-e DE-627 ger DE-627 rakwb eng Alizadeh, Madeline verfasserin (orcid)0000-0001-5650-3393 aut Gastroenterology Fellowship and Postdoctoral Training in Omics and Statistics—Part I: Why Is It Needed? 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 Abstract A multitude of federally and industry-funded efforts are underway to generate and collect human, animal, microbial, and other sources of data on an unprecedented scale; the results are commonly referred to as “big data.” Often vaguely defined, big data refers to large and complex datasets consisting of myriad datatypes that can be integrated to address complex questions. Big data offers a wealth of information that can be accessed only by those who pose the right questions and have sufficient technical knowhow and analytical skills. The intersection comprised of the gut-brain axis, the intestinal microbiome and multi-ome, and several other interconnected organ systems poses particular challenges and opportunities for those engaged in gastrointestinal and liver research. Unfortunately, there is currently a shortage of clinicians, scientists, and physician-scientists with the training needed to use and analyze big data at the scale necessary for widespread implementation of precision medicine. Here, we review the importance of training in the use of big data, the perils of insufficient training, and potential solutions that exist or can be developed to address the dearth of individuals in GI and hepatology research with the necessary level of big data expertise. Medical informatics (dpeaa)DE-He213 Medical education (dpeaa)DE-He213 Big data (dpeaa)DE-He213 Gastroenterology (dpeaa)DE-He213 Hepatology (dpeaa)DE-He213 Sampaio Moura, Natalia aut Schledwitz, Alyssa aut Patil, Seema A. aut Ravel, Jacques aut Raufman, Jean-Pierre aut Enthalten in Digestive diseases and sciences Dordrecht : Springer Science + Business Media B.V., 1934 69(2023), 1 vom: 02. Nov., Seite 18-21 (DE-627)320525384 (DE-600)2015102-0 1573-2568 nnns volume:69 year:2023 number:1 day:02 month:11 pages:18-21 https://dx.doi.org/10.1007/s10620-023-08136-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 69 2023 1 02 11 18-21 |
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Alizadeh, Madeline @@aut@@ Sampaio Moura, Natalia @@aut@@ Schledwitz, Alyssa @@aut@@ Patil, Seema A. @@aut@@ Ravel, Jacques @@aut@@ Raufman, Jean-Pierre @@aut@@ |
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Gastroenterology Fellowship and Postdoctoral Training in Omics and Statistics—Part I: Why Is It Needed? Medical informatics (dpeaa)DE-He213 Medical education (dpeaa)DE-He213 Big data (dpeaa)DE-He213 Gastroenterology (dpeaa)DE-He213 Hepatology (dpeaa)DE-He213 |
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gastroenterology fellowship and postdoctoral training in omics and statistics—part i: why is it needed? |
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Gastroenterology Fellowship and Postdoctoral Training in Omics and Statistics—Part I: Why Is It Needed? |
| abstract |
Abstract A multitude of federally and industry-funded efforts are underway to generate and collect human, animal, microbial, and other sources of data on an unprecedented scale; the results are commonly referred to as “big data.” Often vaguely defined, big data refers to large and complex datasets consisting of myriad datatypes that can be integrated to address complex questions. Big data offers a wealth of information that can be accessed only by those who pose the right questions and have sufficient technical knowhow and analytical skills. The intersection comprised of the gut-brain axis, the intestinal microbiome and multi-ome, and several other interconnected organ systems poses particular challenges and opportunities for those engaged in gastrointestinal and liver research. Unfortunately, there is currently a shortage of clinicians, scientists, and physician-scientists with the training needed to use and analyze big data at the scale necessary for widespread implementation of precision medicine. Here, we review the importance of training in the use of big data, the perils of insufficient training, and potential solutions that exist or can be developed to address the dearth of individuals in GI and hepatology research with the necessary level of big data expertise. © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 |
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Abstract A multitude of federally and industry-funded efforts are underway to generate and collect human, animal, microbial, and other sources of data on an unprecedented scale; the results are commonly referred to as “big data.” Often vaguely defined, big data refers to large and complex datasets consisting of myriad datatypes that can be integrated to address complex questions. Big data offers a wealth of information that can be accessed only by those who pose the right questions and have sufficient technical knowhow and analytical skills. The intersection comprised of the gut-brain axis, the intestinal microbiome and multi-ome, and several other interconnected organ systems poses particular challenges and opportunities for those engaged in gastrointestinal and liver research. Unfortunately, there is currently a shortage of clinicians, scientists, and physician-scientists with the training needed to use and analyze big data at the scale necessary for widespread implementation of precision medicine. Here, we review the importance of training in the use of big data, the perils of insufficient training, and potential solutions that exist or can be developed to address the dearth of individuals in GI and hepatology research with the necessary level of big data expertise. © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 |
| abstract_unstemmed |
Abstract A multitude of federally and industry-funded efforts are underway to generate and collect human, animal, microbial, and other sources of data on an unprecedented scale; the results are commonly referred to as “big data.” Often vaguely defined, big data refers to large and complex datasets consisting of myriad datatypes that can be integrated to address complex questions. Big data offers a wealth of information that can be accessed only by those who pose the right questions and have sufficient technical knowhow and analytical skills. The intersection comprised of the gut-brain axis, the intestinal microbiome and multi-ome, and several other interconnected organ systems poses particular challenges and opportunities for those engaged in gastrointestinal and liver research. Unfortunately, there is currently a shortage of clinicians, scientists, and physician-scientists with the training needed to use and analyze big data at the scale necessary for widespread implementation of precision medicine. Here, we review the importance of training in the use of big data, the perils of insufficient training, and potential solutions that exist or can be developed to address the dearth of individuals in GI and hepatology research with the necessary level of big data expertise. © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 |
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Gastroenterology Fellowship and Postdoctoral Training in Omics and Statistics—Part I: Why Is It Needed? |
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Sampaio Moura, Natalia Schledwitz, Alyssa Patil, Seema A. Ravel, Jacques Raufman, Jean-Pierre |
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| score |
7.399988 |