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2. XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data

Author

Withnell, Eloise, Zhang, Xiaoyu, Sun, Kai, and Guo, Yike

Subjects
Quantitative Biology - Genomics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This "black box" problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE) based interpretable deep learning model for cancer classification using high-dimensional omics data. XOmiVAE is capable of revealing the contribution of each gene and latent dimension for each classification prediction, and the correlation between each gene and each latent dimension. It is also demonstrated that XOmiVAE can explain not only the supervised classification but the unsupervised clustering results from the deep learning network. To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by VAE. The explainable results generated by XOmiVAE were validated by both the performance of downstream tasks and the biomedical knowledge. In our experiments, XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and academic literature, which shows great potential for novel biomedical knowledge discovery from deep learning models., Comment: 12 pages, 7 figures, 10 tables

Published
2021
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4. COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

Author

Abbasizanjani, Hoda, Ahmed, Nida, Ahmed, Badar, Akbari, Ashley, Akinoso-Imran, Abdul Qadr, Allara, Elias, Allery, Freya, Angelantonio, Emanuele Di, Ashworth, Mark, Ayyar-Gupta, Vandana, Babu-Narayan, Sonya, Bacon, Seb, Ball, Steve, Banerjee, Ami, Barber, Mark, Barrett, Jessica, Bennie, Marion, Berry, Colin, Beveridge, Jennifer, Birney, Ewan, Bojanić, Lana, Bolton, Thomas, Bone, Anna, Boyle, Jon, Braithwaite, Tasanee, Bray, Ben, Briffa, Norman, Brind, David, Brown, Katherine, Buch, Maya, Canoy, Dexter, Caputo, Massimo, Carragher, Raymond, Carson, Alan, Cezard, Genevieve, Chang, Jen-Yu Amy, Cheema, Kate, Chin, Richard, Chudasama, Yogini, Cooper, Jennifer, Copland, Emma, Crallan, Rebecca, Cripps, Rachel, Cromwell, David, Curcin, Vasa, Curry, Gwenetta, Dale, Caroline, Danesh, John, Das-Munshi, Jayati, Dashtban, Ashkan, Davies, Alun, Davies, Joanna, Davies, Gareth, Davies, Neil, Day, Joshua, Delmestri, Antonella, Denaxas, Spiros, Denholm, Rachel, Dennis, John, Denniston, Alastair, Deo, Salil, Dhillon, Baljean, Docherty, Annemarie, Dong, Tim, Douiri, Abdel, Downs, Johnny, Dregan, Alexandru, Ellins, Elizabeth A, Elwenspoek, Martha, Falck, Fabian, Falter, Florian, Fan, Yat Yi, Firth, Joseph, Fraser, Lorna, Friebel, Rocco, Gavrieli, Amir, Gerstung, Moritz, Gilbert, Ruth, Gillies, Clare, Glickman, Myer, Goldacre, Ben, Goldacre, Raph, Greaves, Felix, Green, Mark, Grieco, Luca, Griffiths, Rowena, Gurdasani, Deepti, Halcox, Julian, Hall, Nick, Hama, Tuankasfee, Handy, Alex, Hansell, Anna, Hardelid, Pia, Hardy, Flavien, Harris, Daniel, Harrison, Camille, Harron, Katie, Hassaine, Abdelaali, Hassan, Lamiece, Healey, Russell, Hemingway, Harry, Henderson, Angela, Herz, Naomi, Heyl, Johannes, Hidajat, Mira, Higginson, Irene, Hinchliffe, Rosie, Hippisley-Cox, Julia, Ho, Frederick, Hocaoglu, Mevhibe, Hollings, Sam, Horne, Elsie, Hughes, David, Humberstone, Ben, Inouye, Mike, Ip, Samantha, Islam, Nazrul, Jackson, Caroline, Jenkins, David, Jiang, Xiyun, Johnson, Shane, Kadam, Umesh, Kallis, Costas, Karim, Zainab, Kasan, Jake, Katsoulis, Michalis, Kavanagh, Kim, Kee, Frank, Keene, Spencer, Kent, Seamus, Khalid, Sara, Khawaja, Anthony, Khunti, Kamlesh, Killick, Richard, Kinnear, Deborah, Knight, Rochelle, Kolamunnage-Dona, Ruwanthi, Kontopantelis, Evan, Kurdi, Amanj, Lacey, Ben, Lai, Alvina, Lambarth, Andrew, Larzjan, Milad Nazarzadeh, Lawler, Deborah, Lawrence, Thomas, Lawson, Claire, Li, Qiuju, Li, Ken, Llinares, Miguel Bernabeu, Lorgelly, Paula, Lowe, Deborah, Lyons, Jane, Lyons, Ronan, Machado, Pedro, Macleod, Mary Joan, Macleod, John, Malgapo, Evaleen, Mamas, Mamas, Mamouei, Mohammad, Manohar, Sinduja, Mapeta, Rutendo, Martelli, Javiera Leniz, Martos, David Moreno, Mateen, Bilal, McCarthy, Aoife, Melville, Craig, Milton, Rebecca, Mizani, Mehrdad, Moncusi, Marta Pineda, Morales, Daniel, Mordi, Ify, Morrice, Lynn, Morris, Carole, Morris, Eva, Mu, Yi, Mueller, Tanja, Murdock, Lars, Nafilyan, Vahé, Nicholson, George, Nikiphorou, Elena, Nolan, John, Norris, Tom, Norris, Ruth, North, Laura, North, Teri-Louise, O'Connell, Dan, Oliver, Dominic, Oluyase, Adejoke, Olvera-Barrios, Abraham, Omigie, Efosa, Onida, Sarah, Padmanabhan, Sandosh, Palmer, Tom, Pasea, Laura, Patel, Riyaz, Payne, Rupert, Pell, Jill, Petitjean, Carmen, Pherwani, Arun, Pickrell, Owen, Pierotti, Livia, Pirmohamed, Munir, Priedon, Rouven, Prieto-Alhambra, Dani, Proudfoot, Alastair, Quinn, Terry, Quint, Jennifer, Raffetti, Elena, Rahimi, Kazem, Rao, Shishir, Razieh, Cameron, Roberts, Brian, Rogers, Caroline, Rossdale, Jennifer, Salim, Safa, Samani, Nilesh, Sattar, Naveed, Schnier, Christian, Schwartz, Roy, Selby, David, Seminog, Olena, Shabnam, Sharmin, Shah, Ajay, Shelton, Jon, Sheppard, James, Sinha, Shubhra, Skrypak, Mirek, Slapkova, Martina, Sleeman, Katherine, Smith, Craig, Sofat, Reecha, Sosenko, Filip, Sperrin, Matthew, Steeg, Sarah, Sterne, Jonathan, Stoica, Serban, Sudell, Maria, Sudlow, Cathie, Sun, Luanluan, Suseeladevi, Arun Karthikeyan, Sweeting, Michael, Sydes, Matt, Takhar, Rohan, Tang, Howard, Thygesen, Johan, Tilston, George, Tochel, Claire, Toit, Clea du, Tomlinson, Christopher, Toms, Renin, Torabi, Fatemeh, Torralbo, Ana, Townson, Julia, Tufail, Adnan, Tungamirai, Tapiwa, Varma, Susheel, Vollmer, Sebastian, Walker, Venexia, Wang, Tianxiao, Wang, Huan, Warwick, Alasdair, Watkinson, Ruth, Watson, Harry, Whiteley, William, Whittaker, Hannah, Wilde, Harry, Wilkinson, Tim, Williams, Gareth, Williams, Michelle, Williams, Richard, Withnell, Eloise, Wolfe, Charles, Wood, Angela, Wright, Lucy, Wu, Honghan, Wu, Jinge, Wu, Jianhua, Yates, Tom, Zaccardi, Francesco, Zhang, Haoting, Zhang, Huayu, Zuccolo, Luisa, Thygesen, Johan H, Mizani, Mehrdad A, Banerjee, Amitava, Lai, Alvina G, Li, Kezhi, Mateen, Bilal A, Sterne, Jonathan A C, Pagel, Christina, and Whiteley, William N

Published
2022
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7. COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

Author

Thygesen, Johan H, primary, Tomlinson, Christopher, additional, Hollings, Sam, additional, Mizani, Mehrdad A, additional, Handy, Alex, additional, Akbari, Ashley, additional, Banerjee, Amitava, additional, Cooper, Jennifer, additional, Lai, Alvina G, additional, Li, Kezhi, additional, Mateen, Bilal A, additional, Sattar, Naveed, additional, Sofat, Reecha, additional, Torralbo, Ana, additional, Wu, Honghan, additional, Wood, Angela, additional, Sterne, Jonathan A C, additional, Pagel, Christina, additional, Whiteley, William N, additional, Sudlow, Cathie, additional, Hemingway, Harry, additional, Denaxas, Spiros, additional, Abbasizanjani, Hoda, additional, Ahmed, Nida, additional, Ahmed, Badar, additional, Akinoso-Imran, Abdul Qadr, additional, Allara, Elias, additional, Allery, Freya, additional, Angelantonio, Emanuele Di, additional, Ashworth, Mark, additional, Ayyar-Gupta, Vandana, additional, Babu-Narayan, Sonya, additional, Bacon, Seb, additional, Ball, Steve, additional, Banerjee, Ami, additional, Barber, Mark, additional, Barrett, Jessica, additional, Bennie, Marion, additional, Berry, Colin, additional, Beveridge, Jennifer, additional, Birney, Ewan, additional, Bojanić, Lana, additional, Bolton, Thomas, additional, Bone, Anna, additional, Boyle, Jon, additional, Braithwaite, Tasanee, additional, Bray, Ben, additional, Briffa, Norman, additional, Brind, David, additional, Brown, Katherine, additional, Buch, Maya, additional, Canoy, Dexter, additional, Caputo, Massimo, additional, Carragher, Raymond, additional, Carson, Alan, additional, Cezard, Genevieve, additional, Chang, Jen-Yu Amy, additional, Cheema, Kate, additional, Chin, Richard, additional, Chudasama, Yogini, additional, Copland, Emma, additional, Crallan, Rebecca, additional, Cripps, Rachel, additional, Cromwell, David, additional, Curcin, Vasa, additional, Curry, Gwenetta, additional, Dale, Caroline, additional, Danesh, John, additional, Das-Munshi, Jayati, additional, Dashtban, Ashkan, additional, Davies, Alun, additional, Davies, Joanna, additional, Davies, Gareth, additional, Davies, Neil, additional, Day, Joshua, additional, Delmestri, Antonella, additional, Denholm, Rachel, additional, Dennis, John, additional, Denniston, Alastair, additional, Deo, Salil, additional, Dhillon, Baljean, additional, Docherty, Annemarie, additional, Dong, Tim, additional, Douiri, Abdel, additional, Downs, Johnny, additional, Dregan, Alexandru, additional, Ellins, Elizabeth A, additional, Elwenspoek, Martha, additional, Falck, Fabian, additional, Falter, Florian, additional, Fan, Yat Yi, additional, Firth, Joseph, additional, Fraser, Lorna, additional, Friebel, Rocco, additional, Gavrieli, Amir, additional, Gerstung, Moritz, additional, Gilbert, Ruth, additional, Gillies, Clare, additional, Glickman, Myer, additional, Goldacre, Ben, additional, Goldacre, Raph, additional, Greaves, Felix, additional, Green, Mark, additional, Grieco, Luca, additional, Griffiths, Rowena, additional, Gurdasani, Deepti, additional, Halcox, Julian, additional, Hall, Nick, additional, Hama, Tuankasfee, additional, Hansell, Anna, additional, Hardelid, Pia, additional, Hardy, Flavien, additional, Harris, Daniel, additional, Harrison, Camille, additional, Harron, Katie, additional, Hassaine, Abdelaali, additional, Hassan, Lamiece, additional, Healey, Russell, additional, Henderson, Angela, additional, Herz, Naomi, additional, Heyl, Johannes, additional, Hidajat, Mira, additional, Higginson, Irene, additional, Hinchliffe, Rosie, additional, Hippisley-Cox, Julia, additional, Ho, Frederick, additional, Hocaoglu, Mevhibe, additional, Horne, Elsie, additional, Hughes, David, additional, Humberstone, Ben, additional, Inouye, Mike, additional, Ip, Samantha, additional, Islam, Nazrul, additional, Jackson, Caroline, additional, Jenkins, David, additional, Jiang, Xiyun, additional, Johnson, Shane, additional, Kadam, Umesh, additional, Kallis, Costas, additional, Karim, Zainab, additional, Kasan, Jake, additional, Katsoulis, Michalis, additional, Kavanagh, Kim, additional, Kee, Frank, additional, Keene, Spencer, additional, Kent, Seamus, additional, Khalid, Sara, additional, Khawaja, Anthony, additional, Khunti, Kamlesh, additional, Killick, Richard, additional, Kinnear, Deborah, additional, Knight, Rochelle, additional, Kolamunnage-Dona, Ruwanthi, additional, Kontopantelis, Evan, additional, Kurdi, Amanj, additional, Lacey, Ben, additional, Lai, Alvina, additional, Lambarth, Andrew, additional, Larzjan, Milad Nazarzadeh, additional, Lawler, Deborah, additional, Lawrence, Thomas, additional, Lawson, Claire, additional, Li, Qiuju, additional, Li, Ken, additional, Llinares, Miguel Bernabeu, additional, Lorgelly, Paula, additional, Lowe, Deborah, additional, Lyons, Jane, additional, Lyons, Ronan, additional, Machado, Pedro, additional, Macleod, Mary Joan, additional, Macleod, John, additional, Malgapo, Evaleen, additional, Mamas, Mamas, additional, Mamouei, Mohammad, additional, Manohar, Sinduja, additional, Mapeta, Rutendo, additional, Martelli, Javiera Leniz, additional, Martos, David Moreno, additional, Mateen, Bilal, additional, McCarthy, Aoife, additional, Melville, Craig, additional, Milton, Rebecca, additional, Mizani, Mehrdad, additional, Moncusi, Marta Pineda, additional, Morales, Daniel, additional, Mordi, Ify, additional, Morrice, Lynn, additional, Morris, Carole, additional, Morris, Eva, additional, Mu, Yi, additional, Mueller, Tanja, additional, Murdock, Lars, additional, Nafilyan, Vahé, additional, Nicholson, George, additional, Nikiphorou, Elena, additional, Nolan, John, additional, Norris, Tom, additional, Norris, Ruth, additional, North, Laura, additional, North, Teri-Louise, additional, O'Connell, Dan, additional, Oliver, Dominic, additional, Oluyase, Adejoke, additional, Olvera-Barrios, Abraham, additional, Omigie, Efosa, additional, Onida, Sarah, additional, Padmanabhan, Sandosh, additional, Palmer, Tom, additional, Pasea, Laura, additional, Patel, Riyaz, additional, Payne, Rupert, additional, Pell, Jill, additional, Petitjean, Carmen, additional, Pherwani, Arun, additional, Pickrell, Owen, additional, Pierotti, Livia, additional, Pirmohamed, Munir, additional, Priedon, Rouven, additional, Prieto-Alhambra, Dani, additional, Proudfoot, Alastair, additional, Quinn, Terry, additional, Quint, Jennifer, additional, Raffetti, Elena, additional, Rahimi, Kazem, additional, Rao, Shishir, additional, Razieh, Cameron, additional, Roberts, Brian, additional, Rogers, Caroline, additional, Rossdale, Jennifer, additional, Salim, Safa, additional, Samani, Nilesh, additional, Schnier, Christian, additional, Schwartz, Roy, additional, Selby, David, additional, Seminog, Olena, additional, Shabnam, Sharmin, additional, Shah, Ajay, additional, Shelton, Jon, additional, Sheppard, James, additional, Sinha, Shubhra, additional, Skrypak, Mirek, additional, Slapkova, Martina, additional, Sleeman, Katherine, additional, Smith, Craig, additional, Sosenko, Filip, additional, Sperrin, Matthew, additional, Steeg, Sarah, additional, Sterne, Jonathan, additional, Stoica, Serban, additional, Sudell, Maria, additional, Sun, Luanluan, additional, Suseeladevi, Arun Karthikeyan, additional, Sweeting, Michael, additional, Sydes, Matt, additional, Takhar, Rohan, additional, Tang, Howard, additional, Thygesen, Johan, additional, Tilston, George, additional, Tochel, Claire, additional, Toit, Clea du, additional, Toms, Renin, additional, Torabi, Fatemeh, additional, Townson, Julia, additional, Tufail, Adnan, additional, Tungamirai, Tapiwa, additional, Varma, Susheel, additional, Vollmer, Sebastian, additional, Walker, Venexia, additional, Wang, Tianxiao, additional, Wang, Huan, additional, Warwick, Alasdair, additional, Watkinson, Ruth, additional, Watson, Harry, additional, Whiteley, William, additional, Whittaker, Hannah, additional, Wilde, Harry, additional, Wilkinson, Tim, additional, Williams, Gareth, additional, Williams, Michelle, additional, Williams, Richard, additional, Withnell, Eloise, additional, Wolfe, Charles, additional, Wright, Lucy, additional, Wu, Jinge, additional, Wu, Jianhua, additional, Yates, Tom, additional, Zaccardi, Francesco, additional, Zhang, Haoting, additional, Zhang, Huayu, additional, and Zuccolo, Luisa, additional

Published
2022
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8. Linked electronic health records for research on a nationwide cohort of more than 54 million people in England:data resource

Author

Wood, Angela, Denholm, Rachel, Hollings, Sam, Cooper, Jennifer, Ip, Samantha, Walker, Venexia, Denaxas, Spiros, Akbari, Ashley, Banerjee, Amitava, Whiteley, William, Lai, Alvina, Sterne, Jonathan, Sudlow, Cathie, CVD-COVID-UK Consortium, Douiri, Abdel, Akinoso-Imran, Abdul Qadr, Jonas, Adrian, Shah, Ajay, Handy, Alex, Davies, Alun, Kurdi, Amanj, Hansell, Anna, Docherty, Annemarie, Pherwani, Arun, Dashtban, Ashkan, Bray, Ben, Cairns, Ben, Goldacre, Ben, Humberstone, Ben, Mateen, Bilal, Doble, Brett, Roberts, Brian, Morris, Carole, Dale, Caroline, Rogers, Caroline, Wolfe, Charles, Tomlinson, Christopher, Lawson, Claire, Du Toit, Clea, Berry, Colin, Smith, Craig, O’Connell, Dan, Harris, Daniel, Brind, David, Cromwell, David, Hughes, David, Martos, David Moreno, Ringham, Debbie, Lawler, Deborah, Lowe, Deborah, Nikiphorou, Elena, Withnell, Eloise, Di Angelantonio, Emanuele, Morris, Eva, Birney, Ewan, Falck, Fabian, Torabi, Fatemeh, Greaves, Felix, Falter, Florian, Zaccardi, Francesco, Kee, Frank, Davies, Gareth, Nicholson, George, Curry, Gwenetta, Zhang, Haoting, Hemingway, Harry, Wilde, Harry, Abbasizanjani, Hoda, Wu, Honghan, Tang, Howard, Wang, Huan, Mordi, Ify, MacArthur, Jackie, Lyons, Jane, Beveridge, Jennifer, Barrett, Jessica, Wu, Jianhua, Thygesen, Johan, Danesh, John, Dennis, John, Boyle, Jon, Halcox, Julian, Khunti, Kamlesh, Cheema, Kate, Brown, Katherine, Li, Ken, Kavanagh, Kim, North, Laura, Pasea, Laura, Ellins, Libby, Pierotti, Livia, Wright, Lucy, Martin, Lydia, Morrice, Lynn, Mamas, Mamas, Bennie, Marion, Barber, Mark, Macleod, Mary Joan, Caputo, Massimo, Buch, Maya, Mizani, Mehrdad, Katsoulis, Michalis, Gravenor, Mike, Inouye, Mike, Skrypak, Mirek, Gerstung, Moritz, Pirmohamed, Munir, Glickman, Myer, Herz, Naomi, Davies, Neil, Hall, Nick, Samani, Nilesh, Seminog, Olena, Lorgelly, Paula, Machado, Pedro, Li, Qiuju, Goldacre, Raph, Carragher, Raymond, Sofat, Reecha, Takhar, Rohan, Lyons, Ronan, Priedon, Rouven, Griffiths, Rowena, Payne, Rupert, Kolamunnage-Dona, Ruwanthi, Salim, Safa, Padmanabhan, Sandosh, Onida, Sarah, Kent, Seamus, Bacon, Seb, Manohar, Sinduja, Babu-Narayan, Sonya, Keene, Spencer, Varma, Susheel, Lawrence, Thomas, Wang, Tianxiao, Wilkinson, Tim, Norris, Tom, Palmer, Tom, Nafilyan, Vahé, Wood, Angela [0000-0002-7937-304X], and Apollo - University of Cambridge Repository

Subjects
Male, COVID-19/diagnosis, 030204 cardiovascular system & hematology, Cohort Studies, 0302 clinical medicine, COVID-19 Testing, Epidemiology, Electronic Health Records, Primary Health Care/statistics & numerical data, 030212 general & internal medicine, Child, Stroke, Cardiovascular Diseases/diagnosis, education.field_of_study, General Medicine, Middle Aged, Hospitalization, England, Cardiovascular Diseases, Child, Preschool, Cohort, Female, Medical emergency, Medical Record Linkage, Cohort study, Adult, medicine.medical_specialty, COVID-19 Vaccines, Adolescent, Population, MEDLINE, 03 medical and health sciences, Young Adult, Intensive care, medicine, Humans, England/epidemiology, education, Aged, Hospitalization/statistics & numerical data, Primary Health Care, business.industry, SARS-CoV-2, Public health, Research, Infant, Newborn, COVID-19, Infant, medicine.disease, United Kingdom, business
Abstract

Objective To describe a novel England-wide electronic health record (EHR) resource enabling whole population research on covid-19 and cardiovascular disease while ensuring data security and privacy and maintaining public trust. Design Data resource comprising linked person level records from national healthcare settings for the English population, accessible within NHS Digital’s new trusted research environment. Setting EHRs from primary care, hospital episodes, death registry, covid-19 laboratory test results, and community dispensing data, with further enrichment planned from specialist intensive care, cardiovascular, and covid-19 vaccination data. Participants 54.4 million people alive on 1 January 2020 and registered with an NHS general practitioner in England. Main outcome measures Confirmed and suspected covid-19 diagnoses, exemplar cardiovascular conditions (incident stroke or transient ischaemic attack and incident myocardial infarction) and all cause mortality between 1 January and 31 October 2020. Results The linked cohort includes more than 96% of the English population. By combining person level data across national healthcare settings, data on age, sex, and ethnicity are complete for around 95% of the population. Among 53.3 million people with no previous diagnosis of stroke or transient ischaemic attack, 98 721 had a first ever incident stroke or transient ischaemic attack between 1 January and 31 October 2020, of which 30% were recorded only in primary care and 4% only in death registry records. Among 53.2 million people with no previous diagnosis of myocardial infarction, 62 966 had an incident myocardial infarction during follow-up, of which 8% were recorded only in primary care and 12% only in death registry records. A total of 959 470 people had a confirmed or suspected covid-19 diagnosis (714 162 in primary care data, 126 349 in hospital admission records, 776 503 in covid-19 laboratory test data, and 50 504 in death registry records). Although 58% of these were recorded in both primary care and covid-19 laboratory test data, 15% and 18%, respectively, were recorded in only one. Conclusions This population-wide resource shows the importance of linking person level data across health settings to maximise completeness of key characteristics and to ascertain cardiovascular events and covid-19 diagnoses. Although this resource was initially established to support research on covid-19 and cardiovascular disease to benefit clinical care and public health and to inform healthcare policy, it can broaden further to enable a wide range of research.

Published
2021
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