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1. Incidence of diabetes after SARS-CoV-2 infection in England and the implications of COVID-19 vaccination: a retrospective cohort study of 16 million people

Author

Al Arab, Marwa, Almaghrabi, Fatima, Andrews, Colm, Badrick, Ellena, Baz, Sarah, Beckford, Chelsea, Berman, Samantha, Bolton, Tom, Booth, Charlotte, Bowyer, Ruth, Boyd, Andy, Bridger-Staatz, Charis, Brophy, Sinead, Campbell, Archie, Campbell, Kirsteen C, Carnemolla, Alisia, Carpentieri, Jd, Cezard, Genevieve, Chaturvedi, Nishi, Cheetham, Nathan, Costello, Ruth, Cowling, Thomas, Crane, Matthew, Cuitun Coronado, Jose Ignacio, Curtis, Helen, Denaxas, Spiros, Denholm, Rachel, Di Gessa, Giorgio, Dobson, Richard, Douglas, Ian, Evans, Katharine M, Fang, Chao, Ferreira, Vanessa, Finnigan, Lucy, Fisher, Louis, Flaig, Robin, Folarin, Amos, Forbes, Harriet, Foster, Diane, Fox, Laura, Freydin, Maxim, Garcia, Paz, Gibson, Andy, Glen, Fiona, Goldacre, Ben, Goncalves Soares, Ana, Greaves, Felix, Green, Amelia, Green, Mark, Green, Michael, Griffith, Gareth, Hamill Howes, Lee, Hamilton, Olivia, Herbet, Annie, Herrett, Emily, Hopcroft, Lisa, Horne, Elsie, Hou, Bo, Hughes, Alun, Hulme, William, Huntley, Lizzie, Ip, Samantha, Jacques, Wels, Jezzard, Peter, Jones, Louise, Kanagaratnam, Arun, Karthikeyan Suseeladevi, Arun, Katikireddi, Vittal, Kellas, John, Kennedy, Jonathan I, Kibble, Milla, Knight, Rochelle, Knueppel, Anika, Kopasker, Daniel, Kromydas, Theocharis, Kwong, Alex, Langan, Sinead, Lemanska, Agnieszka, Lukaschuk, Elena, Mackenna, Brain, Macleod, John, Maddock, Jane, Mahalingasivam, Viyaasan, Mansfield, Kathryn, McArdle, Fintan, McCartney, Daniel, McEachan, Rosie, McElroy, Eoin, McLachlan, Stela, Mitchell, Ruth, Moltrecht, Bettina, Morley, Jess, Nab, Linda, Neubauer, Stefan, Nigrelli, Lidia, North, Teri, Northstone, Kate, Oakley, Jacqui, Palmer, Tom, Park, Chloe, Parker, Michael, Parsons, Sam, Patalay, Praveetha, Patel, Kishan, Perez-Reche, Francisco, Piechnik, Stefan, Piehlmaier, Dominik, Ploubidis, George, Rafeti, Elena, Raman, Betty, Ranjan, Yatharth, Rapala, Alicja, Rhead, Rebecca, Roberts, Amy, Sampri, Alexia, Sanders, Zeena-Britt, Santorelli, Gillian, Saunders, Laura C, Shah, Anoop, Shah, Syed Ahmar, Sharp, Steve, Shaw, Richard, Sheard, Laura, Sheikh, Aziz, Silverwood, Richard, Smeeth, Liam, Smith, Stephen, Stafford, Jean, Steptoe, Andrew, Sterne, Jonathan, Steves, Claire, Stewart, Callum, Taylor, Kurt, Tazare, John, Teece, Lucy, Thomas, Richard, Thompson, Ellen, Tilling, Kate, Timpson, Nicholas, Tomlinson, Laurie, Toms, Renin, Tunnicliffe, Elizabeth, Turner, Emma L, Walker, Alex, Walker, Venexia, Walter, Scott, Wang, Kevin, Wei, Yinghui, Whitehorn, Rebecca, Wielgoszewska, Bozena, Wild, James M, Willan, Kathryn, Willans, Robert, Williams, Dylan, Wong, Andrew, Wood, Angela, Woodward, Hannah, Wright, John, Yang, Tiffany, Zaninotto, Paola, Zheng, Bang, Zhu, Jingmin, Eastwood, Sophie, Horne, Elsie M F, Massey, Jon, Hopcroft, Lisa E M, Cuitun Coronado, Jose, Davy, Simon, Dillingham, Iain, Morton, Caroline, and Sterne, Jonathan A C

Published
2024
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2. Undervaccination and severe COVID-19 outcomes: meta-analysis of national cohort studies in England, Northern Ireland, Scotland, and Wales

Author

Kerr, Steven, Bedston, Stuart, Cezard, Genevieve, Sampri, Alexia, Murphy, Siobhan, Bradley, Declan T, Morrison, Kirsty, Akbari, Ashley, Whiteley, William, Sullivan, Christopher, Patterson, Lynsey, Khunti, Kamlesh, Denaxas, Spiros, Bolton, Thomas, Khan, Samaira, Keys, Alan, Weatherill, David, Mooney, Karen, Davies, Jan, Ritchie, Lewis, McMenamin, Jim, Kee, Frank, Wood, Angela, Lyons, Ronan A, Sudlow, Cathie, Robertson, Chris, and Sheikh, Aziz

Published
2024
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3. Ethnic differences in the indirect effects of the COVID-19 pandemic on clinical monitoring and hospitalisations for non-COVID conditions in England: a population-based, observational cohort study using the OpenSAFELY platform

Author

Chaturvedi, Nishi, Park, Chloe, Carnemolla, Alisia, Williams, Dylan, Knueppel, Anika, Boyd, Andy, Turner, Emma L., Evans, Katharine M., Thomas, Richard, Berman, Samantha, McLachlan, Stela, Crane, Matthew, Whitehorn, Rebecca, Oakley, Jacqui, Foster, Diane, Woodward, Hannah, Campbell, Kirsteen C., Timpson, Nicholas, Kwong, Alex, Soares, Ana Goncalves, Griffith, Gareth, Toms, Renin, Jones, Louise, Annie, Herbert, Mitchell, Ruth, Palmer, Tom, Sterne, Jonathan, Walker, Venexia, Huntley, Lizzie, Fox, Laura, Denholm, Rachel, Knight, Rochelle, Northstone, Kate, Kanagaratnam, Arun, Horne, Elsie, Forbes, Harriet, North, Teri, Taylor, Kurt, Arab, Marwa A.L., Walker, Scott, Coronado, Jose I.C., Karthikeyan, Arun S., Ploubidis, George, Moltrecht, Bettina, Booth, Charlotte, Parsons, Sam, Wielgoszewska, Bozena, Bridger-Staatz, Charis, Steves, Claire, Thompson, Ellen, Garcia, Paz, Cheetham, Nathan, Bowyer, Ruth, Freydin, Maxim, Roberts, Amy, Goldacre, Ben, Walker, Alex, Morley, Jess, Hulme, William, Nab, Linda, Fisher, Louis, MacKenna, Brian, Andrews, Colm, Curtis, Helen, Hopcroft, Lisa, Green, Amelia, Patalay, Praveetha, Maddock, Jane, Patel, Kishan, Stafford, Jean, Jacques, Wels, Tilling, Kate, Macleod, John, McElroy, Eoin, Shah, Anoop, Silverwood, Richard, Denaxas, Spiros, Flaig, Robin, McCartney, Daniel, Campbell, Archie, Tomlinson, Laurie, Tazare, John, Zheng, Bang, Smeeth, Liam, Herrett, Emily, Cowling, Thomas, Mansfield, Kate, Costello, Ruth E., Wang, Kevin, Mansfield, Kathryn, Mahalingasivam, Viyaasan, Douglas, Ian, Langan, Sinead, Brophy, Sinead, Parker, Michael, Kennedy, Jonathan, McEachan, Rosie, Wright, John, Willan, Kathryn, Badrick, Ellena, Santorelli, Gillian, Yang, Tiffany, Hou, Bo, Steptoe, Andrew, Giorgio, Di Gessa, Zhu, Jingmin, Zaninotto, Paola, Wood, Angela, Cezard, Genevieve, Ip, Samantha, Bolton, Tom, Sampri, Alexia, Rafeti, Elena, Almaghrabi, Fatima, Sheikh, Aziz, Shah, Syed A., Katikireddi, Vittal, Shaw, Richard, Hamilton, Olivia, Green, Michael, Kromydas, Theocharis, Kopasker, Daniel, Greaves, Felix, Willans, Robert, Glen, Fiona, Sharp, Steve, Hughes, Alun, Wong, Andrew, Howes, Lee Hamill, Rapala, Alicja, Nigrelli, Lidia, McArdle, Fintan, Beckford, Chelsea, Raman, Betty, Dobson, Richard, Folarin, Amos, Stewart, Callum, Ranjan, Yatharth, Carpentieri, Jd, Sheard, Laura, Fang, Chao, Baz, Sarah, Gibson, Andy, Kellas, John, Neubauer, Stefan, Piechnik, Stefan, Lukaschuk, Elena, Saunders, Laura C., Wild, James M., Smith, Stephen, Jezzard, Peter, Tunnicliffe, Elizabeth, Sanders, Zeena-Britt, Finnigan, Lucy, Ferreira, Vanessa, Green, Mark, Rhead, Rebecca, Kibble, Milla, Wei, Yinghui, Lemanska, Agnieszka, Perez-Reche, Francisco, Piehlmaier, Dominik, Teece, Lucy, Parker, Edward, Walker, Alex J., Inglesby, Peter, Curtis, Helen J., Morton, Caroline E., Morley, Jessica, Mehrkar, Amir, Bacon, Sebastian C.J., Hickman, George, Croker, Richard, Evans, David, Ward, Tom, DeVito, Nicholas J., Green, Amelia C.A., Massey, Jon, Smith, Rebecca M., Hulme, William J., Davy, Simon, Andrews, Colm D., Hopcroft, Lisa E.M., Drysdale, Henry, Dillingham, Iain, Park, Robin Y., Higgins, Rose, Cunningham, Christine, Wiedemann, Milan, Maude, Steven, Macdonald, Orla, Butler-Cole, Ben F.C., O'Dwyer, Thomas, Stables, Catherine L., Wood, Christopher, Brown, Andrew D., Speed, Victoria, Bridges, Lucy, Schaffer, Andrea L., Walters, Caroline E., Rentsch, Christopher T., Bhaskaran, Krishnan, Schultze, Anna, Williamson, Elizabeth J., McDonald, Helen I., Tomlinson, Laurie A., Mathur, Rohini, Eggo, Rosalind M., Wing, Kevin, Wong, Angel Y.S., Grieve, Richard, Grint, Daniel J., Mansfield, Kathryn E., Douglas, Ian J., Evans, Stephen J.W., Walker, Jemma L., Cowling, Thomas E., Herrett, Emily L., Parker, Edward P.K., Bates, Christopher, Cockburn, Jonathan, Parry, John, Hester, Frank, Harper, Sam, O'Hanlon, Shaun, Eavis, Alex, Jarvis, Richard, Avramov, Dima, Griffiths, Paul, Fowles, Aaron, Parkes, Nasreen, Nicholson, Brian, Perera, Rafael, Harrison, David, Khunti, Kamlesh, Sterne, Jonathan AC., Quint, Jennifer, Henderson, Alasdair D., Carreira, Helena, Bidulka, Patrick, Warren-Gash, Charlotte, Hayes, Joseph F., Quint, Jennifer K., Katikireddi, Srinivasa Vittal, and Langan, Sinéad M.

Published
2023
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4. Incidence of diabetes after SARS-CoV-2 infection in England and the implications of COVID-19 vaccination: a retrospective cohort study of 16 million people

Author

Taylor, Kurt, Eastwood, Sophie, Walker, Venexia, Cezard, Genevieve, Knight, Rochelle, Al Arab, Marwa, Wei, Yinghui, Horne, Elsie M F, Teece, Lucy, Forbes, Harriet, Walker, Alex, Fisher, Louis, Massey, Jon, Hopcroft, Lisa E M, Palmer, Tom, Cuitun Coronado, Jose, Ip, Samantha, Davy, Simon, Dillingham, Iain, Morton, Caroline, Greaves, Felix, Macleod, John, Goldacre, Ben, Wood, Angela, Chaturvedi, Nishi, Sterne, Jonathan A C, Denholm, Rachel, Al Arab, Marwa, Almaghrabi, Fatima, Andrews, Colm, Badrick, Ellena, Baz, Sarah, Beckford, Chelsea, Berman, Samantha, Bolton, Tom, Booth, Charlotte, Bowyer, Ruth, Boyd, Andy, Bridger-Staatz, Charis, Brophy, Sinead, Campbell, Archie, Campbell, Kirsteen C, Carnemolla, Alisia, Carpentieri, Jd, Cezard, Genevieve, Chaturvedi, Nishi, Cheetham, Nathan, Costello, Ruth, Cowling, Thomas, Crane, Matthew, Cuitun Coronado, Jose Ignacio, Curtis, Helen, Denaxas, Spiros, Denholm, Rachel, Di Gessa, Giorgio, Dobson, Richard, Douglas, Ian, Evans, Katharine M, Fang, Chao, Ferreira, Vanessa, Finnigan, Lucy, Fisher, Louis, Flaig, Robin, Folarin, Amos, Forbes, Harriet, Foster, Diane, Fox, Laura, Freydin, Maxim, Garcia, Paz, Gibson, Andy, Glen, Fiona, Goldacre, Ben, Goncalves Soares, Ana, Greaves, Felix, Green, Amelia, Green, Mark, Green, Michael, Griffith, Gareth, Hamill Howes, Lee, Hamilton, Olivia, Herbet, Annie, Herrett, Emily, Hopcroft, Lisa, Horne, Elsie, Hou, Bo, Hughes, Alun, Hulme, William, Huntley, Lizzie, Ip, Samantha, Jacques, Wels, Jezzard, Peter, Jones, Louise, Kanagaratnam, Arun, Karthikeyan Suseeladevi, Arun, Katikireddi, Vittal, Kellas, John, Kennedy, Jonathan I, Kibble, Milla, Knight, Rochelle, Knueppel, Anika, Kopasker, Daniel, Kromydas, Theocharis, Kwong, Alex, Langan, Sinead, Lemanska, Agnieszka, Lukaschuk, Elena, Mackenna, Brain, Macleod, John, Maddock, Jane, Mahalingasivam, Viyaasan, Mansfield, Kathryn, McArdle, Fintan, McCartney, Daniel, McEachan, Rosie, McElroy, Eoin, McLachlan, Stela, Mitchell, Ruth, Moltrecht, Bettina, Morley, Jess, Nab, Linda, Neubauer, Stefan, Nigrelli, Lidia, North, Teri, Northstone, Kate, Oakley, Jacqui, Palmer, Tom, Park, Chloe, Parker, Michael, Parsons, Sam, Patalay, Praveetha, Patel, Kishan, Perez-Reche, Francisco, Piechnik, Stefan, Piehlmaier, Dominik, Ploubidis, George, Rafeti, Elena, Raman, Betty, Ranjan, Yatharth, Rapala, Alicja, Rhead, Rebecca, Roberts, Amy, Sampri, Alexia, Sanders, Zeena-Britt, Santorelli, Gillian, Saunders, Laura C, Shah, Anoop, Shah, Syed Ahmar, Sharp, Steve, Shaw, Richard, Sheard, Laura, Sheikh, Aziz, Silverwood, Richard, Smeeth, Liam, Smith, Stephen, Stafford, Jean, Steptoe, Andrew, Sterne, Jonathan, Steves, Claire, Stewart, Callum, Taylor, Kurt, Tazare, John, Teece, Lucy, Thomas, Richard, Thompson, Ellen, Tilling, Kate, Timpson, Nicholas, Tomlinson, Laurie, Toms, Renin, Tunnicliffe, Elizabeth, Turner, Emma L, Walker, Alex, Walker, Venexia, Walter, Scott, Wang, Kevin, Wei, Yinghui, Whitehorn, Rebecca, Wielgoszewska, Bozena, Wild, James M, Willan, Kathryn, Willans, Robert, Williams, Dylan, Wong, Andrew, Wood, Angela, Woodward, Hannah, Wright, John, Yang, Tiffany, Zaninotto, Paola, Zheng, Bang, and Zhu, Jingmin

Abstract

Some studies have shown that the incidence of type 2 diabetes increases after a diagnosis of COVID-19, although the evidence is not conclusive. However, the effects of the COVID-19 vaccine on this association, or the effect on other diabetes subtypes, are not clear. We aimed to investigate the association between COVID-19 and incidence of type 2, type 1, gestational and non-specific diabetes, and the effect of COVID- 19 vaccination, up to 52 weeks after diagnosis.

Published
2024
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5. A toolbox of machine learning software to support microbiome analysis

Author

Marcos-Zambrano, Laura Judith, primary, López-Molina, Víctor Manuel, additional, Bakir-Gungor, Burcu, additional, Frohme, Marcus, additional, Karaduzovic-Hadziabdic, Kanita, additional, Klammsteiner, Thomas, additional, Ibrahimi, Eliana, additional, Lahti, Leo, additional, Loncar-Turukalo, Tatjana, additional, Dhamo, Xhilda, additional, Simeon, Andrea, additional, Nechyporenko, Alina, additional, Pio, Gianvito, additional, Przymus, Piotr, additional, Sampri, Alexia, additional, Trajkovik, Vladimir, additional, Lacruz-Pleguezuelos, Blanca, additional, Aasmets, Oliver, additional, Araujo, Ricardo, additional, Anagnostopoulos, Ioannis, additional, Aydemir, Önder, additional, Berland, Magali, additional, Calle, M. Luz, additional, Ceci, Michelangelo, additional, Duman, Hatice, additional, Gündoğdu, Aycan, additional, Havulinna, Aki S., additional, Kaka Bra, Kardokh Hama Najib, additional, Kalluci, Eglantina, additional, Karav, Sercan, additional, Lode, Daniel, additional, Lopes, Marta B., additional, May, Patrick, additional, Nap, Bram, additional, Nedyalkova, Miroslava, additional, Paciência, Inês, additional, Pasic, Lejla, additional, Pujolassos, Meritxell, additional, Shigdel, Rajesh, additional, Susín, Antonio, additional, Thiele, Ines, additional, Truică, Ciprian-Octavian, additional, Wilmes, Paul, additional, Yilmaz, Ercument, additional, Yousef, Malik, additional, Claesson, Marcus Joakim, additional, Truu, Jaak, additional, and Carrillo de Santa Pau, Enrique, additional

Published
2023
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6. Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action

Author

D’Elia, Domenica, primary, Truu, Jaak, additional, Lahti, Leo, additional, Berland, Magali, additional, Papoutsoglou, Georgios, additional, Ceci, Michelangelo, additional, Zomer, Aldert, additional, Lopes, Marta B., additional, Ibrahimi, Eliana, additional, Gruca, Aleksandra, additional, Nechyporenko, Alina, additional, Frohme, Marcus, additional, Klammsteiner, Thomas, additional, Pau, Enrique Carrillo-de Santa, additional, Marcos-Zambrano, Laura Judith, additional, Hron, Karel, additional, Pio, Gianvito, additional, Simeon, Andrea, additional, Suharoschi, Ramona, additional, Moreno-Indias, Isabel, additional, Temko, Andriy, additional, Nedyalkova, Miroslava, additional, Apostol, Elena-Simona, additional, Truică, Ciprian-Octavian, additional, Shigdel, Rajesh, additional, Telalović, Jasminka Hasić, additional, Bongcam-Rudloff, Erik, additional, Przymus, Piotr, additional, Jordamović, Naida Babić, additional, Falquet, Laurent, additional, Tarazona, Sonia, additional, Sampri, Alexia, additional, Isola, Gaetano, additional, Pérez-Serrano, David, additional, Trajkovik, Vladimir, additional, Klucar, Lubos, additional, Loncar-Turukalo, Tatjana, additional, Havulinna, Aki S., additional, Jansen, Christian, additional, Bertelsen, Randi J., additional, and Claesson, Marcus Joakim, additional

Published
2023
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7. Ethnic differences in the indirect effects of the COVID-19 pandemic on clinical monitoring and hospitalisations for non-COVID conditions in England: a population-based, observational cohort study using the OpenSAFELY platform

Author

Costello, Ruth E., primary, Tazare, John, additional, Piehlmaier, Dominik, additional, Herrett, Emily, additional, Parker, Edward P.K., additional, Zheng, Bang, additional, Mansfield, Kathryn E., additional, Henderson, Alasdair D., additional, Carreira, Helena, additional, Bidulka, Patrick, additional, Wong, Angel Y.S., additional, Warren-Gash, Charlotte, additional, Hayes, Joseph F., additional, Quint, Jennifer K., additional, MacKenna, Brian, additional, Mehrkar, Amir, additional, Eggo, Rosalind M., additional, Katikireddi, Srinivasa Vittal, additional, Tomlinson, Laurie, additional, Langan, Sinéad M., additional, Mathur, Rohini, additional, Chaturvedi, Nishi, additional, Park, Chloe, additional, Carnemolla, Alisia, additional, Williams, Dylan, additional, Knueppel, Anika, additional, Boyd, Andy, additional, Turner, Emma L., additional, Evans, Katharine M., additional, Thomas, Richard, additional, Berman, Samantha, additional, McLachlan, Stela, additional, Crane, Matthew, additional, Whitehorn, Rebecca, additional, Oakley, Jacqui, additional, Foster, Diane, additional, Woodward, Hannah, additional, Campbell, Kirsteen C., additional, Timpson, Nicholas, additional, Kwong, Alex, additional, Soares, Ana Goncalves, additional, Griffith, Gareth, additional, Toms, Renin, additional, Jones, Louise, additional, Annie, Herbert, additional, Mitchell, Ruth, additional, Palmer, Tom, additional, Sterne, Jonathan, additional, Walker, Venexia, additional, Huntley, Lizzie, additional, Fox, Laura, additional, Denholm, Rachel, additional, Knight, Rochelle, additional, Northstone, Kate, additional, Kanagaratnam, Arun, additional, Horne, Elsie, additional, Forbes, Harriet, additional, North, Teri, additional, Taylor, Kurt, additional, Arab, Marwa A.L., additional, Walker, Scott, additional, Coronado, Jose I.C., additional, Karthikeyan, Arun S., additional, Ploubidis, George, additional, Moltrecht, Bettina, additional, Booth, Charlotte, additional, Parsons, Sam, additional, Wielgoszewska, Bozena, additional, Bridger-Staatz, Charis, additional, Steves, Claire, additional, Thompson, Ellen, additional, Garcia, Paz, additional, Cheetham, Nathan, additional, Bowyer, Ruth, additional, Freydin, Maxim, additional, Roberts, Amy, additional, Goldacre, Ben, additional, Walker, Alex, additional, Morley, Jess, additional, Hulme, William, additional, Nab, Linda, additional, Fisher, Louis, additional, Andrews, Colm, additional, Curtis, Helen, additional, Hopcroft, Lisa, additional, Green, Amelia, additional, Patalay, Praveetha, additional, Maddock, Jane, additional, Patel, Kishan, additional, Stafford, Jean, additional, Jacques, Wels, additional, Tilling, Kate, additional, Macleod, John, additional, McElroy, Eoin, additional, Shah, Anoop, additional, Silverwood, Richard, additional, Denaxas, Spiros, additional, Flaig, Robin, additional, McCartney, Daniel, additional, Campbell, Archie, additional, Smeeth, Liam, additional, Cowling, Thomas, additional, Mansfield, Kate, additional, Costello, Ruth E., additional, Wang, Kevin, additional, Mansfield, Kathryn, additional, Mahalingasivam, Viyaasan, additional, Douglas, Ian, additional, Langan, Sinead, additional, Brophy, Sinead, additional, Parker, Michael, additional, Kennedy, Jonathan, additional, McEachan, Rosie, additional, Wright, John, additional, Willan, Kathryn, additional, Badrick, Ellena, additional, Santorelli, Gillian, additional, Yang, Tiffany, additional, Hou, Bo, additional, Steptoe, Andrew, additional, Giorgio, Di Gessa, additional, Zhu, Jingmin, additional, Zaninotto, Paola, additional, Wood, Angela, additional, Cezard, Genevieve, additional, Ip, Samantha, additional, Bolton, Tom, additional, Sampri, Alexia, additional, Rafeti, Elena, additional, Almaghrabi, Fatima, additional, Sheikh, Aziz, additional, Shah, Syed A., additional, Katikireddi, Vittal, additional, Shaw, Richard, additional, Hamilton, Olivia, additional, Green, Michael, additional, Kromydas, Theocharis, additional, Kopasker, Daniel, additional, Greaves, Felix, additional, Willans, Robert, additional, Glen, Fiona, additional, Sharp, Steve, additional, Hughes, Alun, additional, Wong, Andrew, additional, Howes, Lee Hamill, additional, Rapala, Alicja, additional, Nigrelli, Lidia, additional, McArdle, Fintan, additional, Beckford, Chelsea, additional, Raman, Betty, additional, Dobson, Richard, additional, Folarin, Amos, additional, Stewart, Callum, additional, Ranjan, Yatharth, additional, Carpentieri, Jd, additional, Sheard, Laura, additional, Fang, Chao, additional, Baz, Sarah, additional, Gibson, Andy, additional, Kellas, John, additional, Neubauer, Stefan, additional, Piechnik, Stefan, additional, Lukaschuk, Elena, additional, Saunders, Laura C., additional, Wild, James M., additional, Smith, Stephen, additional, Jezzard, Peter, additional, Tunnicliffe, Elizabeth, additional, Sanders, Zeena-Britt, additional, Finnigan, Lucy, additional, Ferreira, Vanessa, additional, Green, Mark, additional, Rhead, Rebecca, additional, Kibble, Milla, additional, Wei, Yinghui, additional, Lemanska, Agnieszka, additional, Perez-Reche, Francisco, additional, Teece, Lucy, additional, Parker, Edward, additional, Walker, Alex J., additional, Inglesby, Peter, additional, Curtis, Helen J., additional, Morton, Caroline E., additional, Morley, Jessica, additional, Bacon, Sebastian C.J., additional, Hickman, George, additional, Croker, Richard, additional, Evans, David, additional, Ward, Tom, additional, DeVito, Nicholas J., additional, Green, Amelia C.A., additional, Massey, Jon, additional, Smith, Rebecca M., additional, Hulme, William J., additional, Davy, Simon, additional, Andrews, Colm D., additional, Hopcroft, Lisa E.M., additional, Drysdale, Henry, additional, Dillingham, Iain, additional, Park, Robin Y., additional, Higgins, Rose, additional, Cunningham, Christine, additional, Wiedemann, Milan, additional, Maude, Steven, additional, Macdonald, Orla, additional, Butler-Cole, Ben F.C., additional, O'Dwyer, Thomas, additional, Stables, Catherine L., additional, Wood, Christopher, additional, Brown, Andrew D., additional, Speed, Victoria, additional, Bridges, Lucy, additional, Schaffer, Andrea L., additional, Walters, Caroline E., additional, Rentsch, Christopher T., additional, Bhaskaran, Krishnan, additional, Schultze, Anna, additional, Williamson, Elizabeth J., additional, McDonald, Helen I., additional, Tomlinson, Laurie A., additional, Wing, Kevin, additional, Grieve, Richard, additional, Grint, Daniel J., additional, Douglas, Ian J., additional, Evans, Stephen J.W., additional, Walker, Jemma L., additional, Cowling, Thomas E., additional, Herrett, Emily L., additional, Bates, Christopher, additional, Cockburn, Jonathan, additional, Parry, John, additional, Hester, Frank, additional, Harper, Sam, additional, O'Hanlon, Shaun, additional, Eavis, Alex, additional, Jarvis, Richard, additional, Avramov, Dima, additional, Griffiths, Paul, additional, Fowles, Aaron, additional, Parkes, Nasreen, additional, Nicholson, Brian, additional, Perera, Rafael, additional, Harrison, David, additional, Khunti, Kamlesh, additional, Sterne, Jonathan AC., additional, and Quint, Jennifer, additional

Published
2023
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8. A toolbox of machine learning software to support microbiome analysis.

Author

Judith Marcos-Zambrano, Laura, Manuel López-Molina, Víctor, Bakir-Gungor, Burcu, Frohme, Marcus, Karaduzovic-Hadziabdic, Kanita, Klammsteiner, Thomas, Ibrahimi, Eliana, Lahti, Leo, Loncar-Turukalo, Tatjana, Dhamo, Xhilda, Simeon, Andrea, Nechyporenko, Alina, Pio, Gianvito, Przymus, Piotr, Sampri, Alexia, Trajkovik, Vladimir, Lacruz-Pleguezuelos, Blanca, Aasmets, Oliver, Araujo, Ricardo, and Anagnostopoulos, Ioannis

Subjects
MACHINE learning, SOFTWARE frameworks, MEDICAL scientists, HUMAN microbiota, COMPUTER software, DEEP learning, FEATURE selection
Abstract

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis. [ABSTRACT FROM AUTHOR]

Published
2023
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9. Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action

Author

D'Elia, Domenica, Truu, Jaak, Lahti, Leo, Berland, Magali, Papoutsoglou, Georgios, Ceci, Michelangelo, Zomer, Aldert, Lopes, Marta B, Ibrahimi, Eliana, Gruca, Aleksandra, Nechyporenko, Alina, Frohme, Marcus, Klammsteiner, Thomas, Pau, Enrique Carrillo-de Santa, Marcos-Zambrano, Laura Judith, Hron, Karel, Pio, Gianvito, Simeon, Andrea, Suharoschi, Ramona, Moreno-Indias, Isabel, Temko, Andriy, Nedyalkova, Miroslava, Apostol, Elena-Simona, Truică, Ciprian-Octavian, Shigdel, Rajesh, Telalović, Jasminka Hasić, Bongcam-Rudloff, Erik, Przymus, Piotr, Jordamović, Naida Babić, Falquet, Laurent, Tarazona, Sonia, Sampri, Alexia, Isola, Gaetano, Pérez-Serrano, David, Trajkovik, Vladimir, Klucar, Lubos, Loncar-Turukalo, Tatjana, Havulinna, Aki S, Jansen, Christian, Bertelsen, Randi J, Claesson, Marcus Joakim, D'Elia, Domenica, Truu, Jaak, Lahti, Leo, Berland, Magali, Papoutsoglou, Georgios, Ceci, Michelangelo, Zomer, Aldert, Lopes, Marta B, Ibrahimi, Eliana, Gruca, Aleksandra, Nechyporenko, Alina, Frohme, Marcus, Klammsteiner, Thomas, Pau, Enrique Carrillo-de Santa, Marcos-Zambrano, Laura Judith, Hron, Karel, Pio, Gianvito, Simeon, Andrea, Suharoschi, Ramona, Moreno-Indias, Isabel, Temko, Andriy, Nedyalkova, Miroslava, Apostol, Elena-Simona, Truică, Ciprian-Octavian, Shigdel, Rajesh, Telalović, Jasminka Hasić, Bongcam-Rudloff, Erik, Przymus, Piotr, Jordamović, Naida Babić, Falquet, Laurent, Tarazona, Sonia, Sampri, Alexia, Isola, Gaetano, Pérez-Serrano, David, Trajkovik, Vladimir, Klucar, Lubos, Loncar-Turukalo, Tatjana, Havulinna, Aki S, Jansen, Christian, Bertelsen, Randi J, and Claesson, Marcus Joakim

Abstract

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

Published
2023

10. Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action

Author

Klinische infectiologie en microb. lab., Infectious Diseases and Immunology - KLIF, D'Elia, Domenica, Truu, Jaak, Lahti, Leo, Berland, Magali, Papoutsoglou, Georgios, Ceci, Michelangelo, Zomer, Aldert, Lopes, Marta B, Ibrahimi, Eliana, Gruca, Aleksandra, Nechyporenko, Alina, Frohme, Marcus, Klammsteiner, Thomas, Pau, Enrique Carrillo-de Santa, Marcos-Zambrano, Laura Judith, Hron, Karel, Pio, Gianvito, Simeon, Andrea, Suharoschi, Ramona, Moreno-Indias, Isabel, Temko, Andriy, Nedyalkova, Miroslava, Apostol, Elena-Simona, Truică, Ciprian-Octavian, Shigdel, Rajesh, Telalović, Jasminka Hasić, Bongcam-Rudloff, Erik, Przymus, Piotr, Jordamović, Naida Babić, Falquet, Laurent, Tarazona, Sonia, Sampri, Alexia, Isola, Gaetano, Pérez-Serrano, David, Trajkovik, Vladimir, Klucar, Lubos, Loncar-Turukalo, Tatjana, Havulinna, Aki S, Jansen, Christian, Bertelsen, Randi J, Claesson, Marcus Joakim, Klinische infectiologie en microb. lab., Infectious Diseases and Immunology - KLIF, D'Elia, Domenica, Truu, Jaak, Lahti, Leo, Berland, Magali, Papoutsoglou, Georgios, Ceci, Michelangelo, Zomer, Aldert, Lopes, Marta B, Ibrahimi, Eliana, Gruca, Aleksandra, Nechyporenko, Alina, Frohme, Marcus, Klammsteiner, Thomas, Pau, Enrique Carrillo-de Santa, Marcos-Zambrano, Laura Judith, Hron, Karel, Pio, Gianvito, Simeon, Andrea, Suharoschi, Ramona, Moreno-Indias, Isabel, Temko, Andriy, Nedyalkova, Miroslava, Apostol, Elena-Simona, Truică, Ciprian-Octavian, Shigdel, Rajesh, Telalović, Jasminka Hasić, Bongcam-Rudloff, Erik, Przymus, Piotr, Jordamović, Naida Babić, Falquet, Laurent, Tarazona, Sonia, Sampri, Alexia, Isola, Gaetano, Pérez-Serrano, David, Trajkovik, Vladimir, Klucar, Lubos, Loncar-Turukalo, Tatjana, Havulinna, Aki S, Jansen, Christian, Bertelsen, Randi J, and Claesson, Marcus Joakim

Published
2023

13. Occupational Exposure Assessment Tools in Europe: A Comprehensive Inventory Overview

Author

Peters, Susan, Vienneau, Danielle, Sampri, Alexia, Turner, Michelle C, Castaño-Vinyals, Gemma, Bugge, Merete, Vermeulen, Roel, Peters, Susan, Vienneau, Danielle, Sampri, Alexia, Turner, Michelle C, Castaño-Vinyals, Gemma, Bugge, Merete, and Vermeulen, Roel

Abstract

OBJECTIVES: The Network on the Coordination and Harmonisation of European Occupational Cohorts (OMEGA-NET) was set up to enable optimization of the use of industrial and general population cohorts across Europe to advance aetiological research. High-quality harmonized exposure assessment is crucial to derive comparable results and to enable pooled analyses. To facilitate a harmonized research strategy, a concerted effort is needed to catalogue available occupational exposure information. We here aim to provide a first comprehensive overview of exposure assessment tools that could be used for occupational epidemiological studies.METHODS: An online inventory was set up to collect meta-data on exposure assessment tools. Occupational health researchers were invited via newsletters, editorials, and individual e-mails to provide details of job-exposure matrices (JEMs), exposure databases, and occupational coding systems and their associated crosswalks to translate codes between different systems, with a focus on Europe.RESULTS: Meta-data on 36 general population JEMs, 11 exposure databases, and 29 occupational coding systems from more than 10 countries have been collected up to August 2021. A wide variety of exposures were covered in the JEMs on which data were entered, with dusts and fibres (in 14 JEMs) being the most common types. Fewer JEMs covered organization of work (5) and biological factors (4). Dusts and fibres were also the most common exposures included in the databases (7 out of 11), followed by solvents and pesticides (both in 6 databases).CONCLUSIONS: This inventory forms the basis for a searchable web-based database of meta-data on existing occupational exposure information, to support researchers in finding the available tools for assessing occupational exposures in their cohorts, and future efforts for harmonization of exposure assessment. This inventory remains open for further additions, to enlarge its coverage and include newly

Published
2022

14. Occupational Exposure Assessment Tools in Europe: A Comprehensive Inventory Overview

Author

IRAS OH Epidemiology Chemical Agents, dIRAS RA-2, Peters, Susan, Vienneau, Danielle, Sampri, Alexia, Turner, Michelle C, Castaño-Vinyals, Gemma, Bugge, Merete, Vermeulen, Roel, IRAS OH Epidemiology Chemical Agents, dIRAS RA-2, Peters, Susan, Vienneau, Danielle, Sampri, Alexia, Turner, Michelle C, Castaño-Vinyals, Gemma, Bugge, Merete, and Vermeulen, Roel

Published
2022

17. Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

Author

Moreno-Indias, Isabel, Lahti, Leo, Nedyalkova, Miroslava, Elbere, Ilze, Roshchupkin, Gennady, Adilovic, Muhamed, Aydemir, Onder, Bakir-Gungor, Burcu, Santa Pau, Enrique Carrillo-de, D’Elia, Domenica, Desai, Mahesh S., Falquet, Laurent, Gundogdu, Aycan, Hron, Karel, Klammsteiner, Thomas, Lopes, Marta B., Marcos-Zambrano, Laura Judith, Marques, Cláudia, Mason, Michael, May, Patrick, Pašić, Lejla, Pio, Gianvito, Pongor, Sándor, Promponas, Vasilis J., Przymus, Piotr, Saez-Rodriguez, Julio, Sampri, Alexia, Shigdel, Rajesh, Stres, Blaz, Suharoschi, Ramona, Truu, Jaak, Truică, Ciprian-Octavian, Vilne, Baiba, Vlachakis, Dimitrios, Yilmaz, Ercument, Zeller, Georg, Zomer, Aldert L., Gómez-Cabrero, David, Claesson, Marcus J., Moreno-Indias, Isabel, Lahti, Leo, Nedyalkova, Miroslava, Elbere, Ilze, Roshchupkin, Gennady, Adilovic, Muhamed, Aydemir, Onder, Bakir-Gungor, Burcu, Santa Pau, Enrique Carrillo-de, D’Elia, Domenica, Desai, Mahesh S., Falquet, Laurent, Gundogdu, Aycan, Hron, Karel, Klammsteiner, Thomas, Lopes, Marta B., Marcos-Zambrano, Laura Judith, Marques, Cláudia, Mason, Michael, May, Patrick, Pašić, Lejla, Pio, Gianvito, Pongor, Sándor, Promponas, Vasilis J., Przymus, Piotr, Saez-Rodriguez, Julio, Sampri, Alexia, Shigdel, Rajesh, Stres, Blaz, Suharoschi, Ramona, Truu, Jaak, Truică, Ciprian-Octavian, Vilne, Baiba, Vlachakis, Dimitrios, Yilmaz, Ercument, Zeller, Georg, Zomer, Aldert L., Gómez-Cabrero, David, and Claesson, Marcus J.

Abstract

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Published
2021

18. Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

Author

Klinische infectiologie en microb. lab., dI&I I&I-4, Moreno-Indias, Isabel, Lahti, Leo, Nedyalkova, Miroslava, Elbere, Ilze, Roshchupkin, Gennady, Adilovic, Muhamed, Aydemir, Onder, Bakir-Gungor, Burcu, Santa Pau, Enrique Carrillo-de, D’Elia, Domenica, Desai, Mahesh S., Falquet, Laurent, Gundogdu, Aycan, Hron, Karel, Klammsteiner, Thomas, Lopes, Marta B., Marcos-Zambrano, Laura Judith, Marques, Cláudia, Mason, Michael, May, Patrick, Pašić, Lejla, Pio, Gianvito, Pongor, Sándor, Promponas, Vasilis J., Przymus, Piotr, Saez-Rodriguez, Julio, Sampri, Alexia, Shigdel, Rajesh, Stres, Blaz, Suharoschi, Ramona, Truu, Jaak, Truică, Ciprian-Octavian, Vilne, Baiba, Vlachakis, Dimitrios, Yilmaz, Ercument, Zeller, Georg, Zomer, Aldert L., Gómez-Cabrero, David, Claesson, Marcus J., Klinische infectiologie en microb. lab., dI&I I&I-4, Moreno-Indias, Isabel, Lahti, Leo, Nedyalkova, Miroslava, Elbere, Ilze, Roshchupkin, Gennady, Adilovic, Muhamed, Aydemir, Onder, Bakir-Gungor, Burcu, Santa Pau, Enrique Carrillo-de, D’Elia, Domenica, Desai, Mahesh S., Falquet, Laurent, Gundogdu, Aycan, Hron, Karel, Klammsteiner, Thomas, Lopes, Marta B., Marcos-Zambrano, Laura Judith, Marques, Cláudia, Mason, Michael, May, Patrick, Pašić, Lejla, Pio, Gianvito, Pongor, Sándor, Promponas, Vasilis J., Przymus, Piotr, Saez-Rodriguez, Julio, Sampri, Alexia, Shigdel, Rajesh, Stres, Blaz, Suharoschi, Ramona, Truu, Jaak, Truică, Ciprian-Octavian, Vilne, Baiba, Vlachakis, Dimitrios, Yilmaz, Ercument, Zeller, Georg, Zomer, Aldert L., Gómez-Cabrero, David, and Claesson, Marcus J.

Published
2021

19. Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

Author

Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Luxembourg Institute of Health - LIH [research center], COST Action CA1813, CORE grant (C18/BM/12585940) [sponsor], Moreno-Indias, Isabel, Lahti, Leo, Nedyalkova, Miroslava, Elbere, Ilze, Roshchupkin, Gennady, Adilovic, Muhamed, Aydemir, Onder, Bakir-Gungor, Burcu, Santa Pau, Enrique Carrillo-De, D’Elia, Domenica, Desai, Mahesh, Falquet, Laurent, Gundogdu, Aycan, Hron, Karel, Klammsteiner, Thomas, Lopes, Marta B., Marcos-Zambrano, Laura Judith, Marques, Cláudia, Mason, Michael, May, Patrick, Pašić, Lejla, Pio, Gianvito, Pongor, Sándor, Promponas, Vasilis J., Przymus, Piotr, Saez-Rodriguez, Julio, Sampri, Alexia, Shigdel, Rajesh, Stres, Blaz, Suharoschi, Ramona, Truu, Jaak, Truică, Ciprian-Octavian, Vilne, Baiba, Vlachakis, Dimitrios, Yilmaz, Ercument, Zeller, Georg, Zomer, Aldert L., Gómez-Cabrero, David, Claesson, Marcus J., Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Luxembourg Institute of Health - LIH [research center], COST Action CA1813, CORE grant (C18/BM/12585940) [sponsor], Moreno-Indias, Isabel, Lahti, Leo, Nedyalkova, Miroslava, Elbere, Ilze, Roshchupkin, Gennady, Adilovic, Muhamed, Aydemir, Onder, Bakir-Gungor, Burcu, Santa Pau, Enrique Carrillo-De, D’Elia, Domenica, Desai, Mahesh, Falquet, Laurent, Gundogdu, Aycan, Hron, Karel, Klammsteiner, Thomas, Lopes, Marta B., Marcos-Zambrano, Laura Judith, Marques, Cláudia, Mason, Michael, May, Patrick, Pašić, Lejla, Pio, Gianvito, Pongor, Sándor, Promponas, Vasilis J., Przymus, Piotr, Saez-Rodriguez, Julio, Sampri, Alexia, Shigdel, Rajesh, Stres, Blaz, Suharoschi, Ramona, Truu, Jaak, Truică, Ciprian-Octavian, Vilne, Baiba, Vlachakis, Dimitrios, Yilmaz, Ercument, Zeller, Georg, Zomer, Aldert L., Gómez-Cabrero, David, and Claesson, Marcus J.

Abstract

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Published
2021

20. Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

Author

Moreno-Indias, Isabel, primary, Lahti, Leo, additional, Nedyalkova, Miroslava, additional, Elbere, Ilze, additional, Roshchupkin, Gennady, additional, Adilovic, Muhamed, additional, Aydemir, Onder, additional, Bakir-Gungor, Burcu, additional, Santa Pau, Enrique Carrillo-de, additional, D’Elia, Domenica, additional, Desai, Mahesh S., additional, Falquet, Laurent, additional, Gundogdu, Aycan, additional, Hron, Karel, additional, Klammsteiner, Thomas, additional, Lopes, Marta B., additional, Marcos-Zambrano, Laura Judith, additional, Marques, Cláudia, additional, Mason, Michael, additional, May, Patrick, additional, Pašić, Lejla, additional, Pio, Gianvito, additional, Pongor, Sándor, additional, Promponas, Vasilis J., additional, Przymus, Piotr, additional, Saez-Rodriguez, Julio, additional, Sampri, Alexia, additional, Shigdel, Rajesh, additional, Stres, Blaz, additional, Suharoschi, Ramona, additional, Truu, Jaak, additional, Truică, Ciprian-Octavian, additional, Vilne, Baiba, additional, Vlachakis, Dimitrios, additional, Yilmaz, Ercument, additional, Zeller, Georg, additional, Zomer, Aldert L., additional, Gómez-Cabrero, David, additional, and Claesson, Marcus J., additional

Published
2021
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21. Probabilistic Approaches to Overcome Content Heterogeneity in Data Integration: A Study Case in Systematic Lupus Erythematosus.

Author

SAMPRI, Alexia, GEIFMAN, Nophar, SUEUR, Helen LE, DOHERTY, Patrick, COUCH, Philip, BRUCE, Ian, and PEEK, Niels

Abstract

Integrating data from different sources into homogeneous dataset increases the opportunities to study human health. However, disparate data collections are often heterogeneous, which complicates their integration. In this paper, we focus on the issue of content heterogeneity in data integration. Traditional approaches for resolving content heterogeneity map all source datasets to a common data model that includes only shared data items, and thus omit all items that vary between datasets. Based on an example of three datasets in Systemic Lupus Erythematosus, we describe and experimentally evaluate a probabilistic data integration approach which propagates the uncertainty resulting from content heterogeneity into statistical inference, avoiding the need to map to a common data model. [ABSTRACT FROM AUTHOR]

Published
2020
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25. Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action.

Author

D'Elia D, Truu J, Lahti L, Berland M, Papoutsoglou G, Ceci M, Zomer A, Lopes MB, Ibrahimi E, Gruca A, Nechyporenko A, Frohme M, Klammsteiner T, Pau ECS, Marcos-Zambrano LJ, Hron K, Pio G, Simeon A, Suharoschi R, Moreno-Indias I, Temko A, Nedyalkova M, Apostol ES, Truică CO, Shigdel R, Telalović JH, Bongcam-Rudloff E, Przymus P, Jordamović NB, Falquet L, Tarazona S, Sampri A, Isola G, Pérez-Serrano D, Trajkovik V, Klucar L, Loncar-Turukalo T, Havulinna AS, Jansen C, Bertelsen RJ, and Claesson MJ

Abstract

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices., Competing Interests: CJ is employed by Biome diagnostics GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 D’Elia, Truu, Lahti, Berland, Papoutsoglou, Ceci, Zomer, Lopes, Ibrahimi, Gruca, Nechyporenko, Frohme, Klammsteiner, Pau, Marcos-Zambrano, Hron, Pio, Simeon, Suharoschi, Moreno-Indias, Temko, Nedyalkova, Apostol, Truică, Shigdel, Telalović, Bongcam-Rudloff, Przymus, Jordamović, Falquet, Tarazona, Sampri, Isola, Pérez-Serrano, Trajkovik, Klucar, Loncar-Turukalo, Havulinna, Jansen, Bertelsen and Claesson.)

Published
2023
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26. Occupational Exposure Assessment Tools in Europe: A Comprehensive Inventory Overview.

Author

Peters S, Vienneau D, Sampri A, Turner MC, Castaño-Vinyals G, Bugge M, and Vermeulen R

Subjects
Dust, Europe, Humans, Occupations, Occupational Exposure, Occupational Health
Abstract

Objectives: The Network on the Coordination and Harmonisation of European Occupational Cohorts (OMEGA-NET) was set up to enable optimization of the use of industrial and general population cohorts across Europe to advance aetiological research. High-quality harmonized exposure assessment is crucial to derive comparable results and to enable pooled analyses. To facilitate a harmonized research strategy, a concerted effort is needed to catalogue available occupational exposure information. We here aim to provide a first comprehensive overview of exposure assessment tools that could be used for occupational epidemiological studies., Methods: An online inventory was set up to collect meta-data on exposure assessment tools. Occupational health researchers were invited via newsletters, editorials, and individual e-mails to provide details of job-exposure matrices (JEMs), exposure databases, and occupational coding systems and their associated crosswalks to translate codes between different systems, with a focus on Europe., Results: Meta-data on 36 general population JEMs, 11 exposure databases, and 29 occupational coding systems from more than 10 countries have been collected up to August 2021. A wide variety of exposures were covered in the JEMs on which data were entered, with dusts and fibres (in 14 JEMs) being the most common types. Fewer JEMs covered organization of work (5) and biological factors (4). Dusts and fibres were also the most common exposures included in the databases (7 out of 11), followed by solvents and pesticides (both in 6 databases)., Conclusions: This inventory forms the basis for a searchable web-based database of meta-data on existing occupational exposure information, to support researchers in finding the available tools for assessing occupational exposures in their cohorts, and future efforts for harmonization of exposure assessment. This inventory remains open for further additions, to enlarge its coverage and include newly developed tools., (© The Author(s) 2021. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

Published
2022
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