Genetic Profiling and Comorbidities of Zika Infection von Moni, Mohammad Ali, Lio, Pietro

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AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning von Charoenkwan, Phasit, Ahmed, Saeed, Nantasenamat, Chanin, Quinn, Julian M. W., Moni, Mohammad Ali, Lio’, Pietro, Shoombuatong, Watshara

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SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins von Charoenkwan, Phasit, Schaduangrat, Nalini, Moni, Mohammad Ali, Lio’, Pietro, Manavalan, Balachandran, Shoombuatong, Watshara

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SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins von Ahmad, Saeed, Charoenkwan, Phasit, Quinn, Julian M. W., Moni, Mohammad Ali, Hasan, Md Mehedi, Lio’, Pietro, Shoombuatong, Watshara

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Multi-omic analysis of signalling factors in inflammatory comorbidities von Xiao, Hui, Bartoszek, Krzysztof, Lio', Pietro

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iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features von Charoenkwan, Phasit, Nantasenamat, Chanin, Hasan, Md Mehedi, Moni, Mohammad Ali, Lio, Pietro, Shoombuatong, Watshara

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PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning von Charoenkwan, Phasit, Pipattanaboon, Chonlatip, Nantasenamat, Chanin, Hasan, Md Mehedi, Moni, Mohammad Ali, Lio’, Pietro, Shoombuatong, Watshara

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StackDPPIV: A novel computational approach for accurate prediction of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides von Charoenkwan, Phasit, Nantasenamat, Chanin, Hasan, Md Mehedi, Moni, Mohammad Ali, Lio', Pietro, Manavalan, Balachandran, Shoombuatong, Watshara

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Comorbidity: a multidimensional approach von Capobianco, Enrico, Lio’, Pietro

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Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides von Charoenkwan, Phasit, Chumnanpuen, Pramote, Schaduangrat, Nalini, Lio’, Pietro, Moni, Mohammad Ali, Shoombuatong, Watshara

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How artificial intelligence and machine learning can help healthcare systems respond to COVID-19 von van der Schaar, Mihaela, Alaa, Ahmed M., Floto, Andres, Gimson, Alexander, Scholtes, Stefan, Wood, Angela, McKinney, Eoin, Jarrett, Daniel, Lio, Pietro, Ercole, Ari

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StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy von Schaduangrat, Nalini, Anuwongcharoen, Nuttapat, Moni, Mohammad Ali, Lio’, Pietro, Charoenkwan, Phasit, Shoombuatong, Watshara

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Predicting factors for survival of breast cancer patients using machine learning techniques von Ganggayah, Mogana Darshini, Taib, Nur Aishah, Har, Yip Cheng, Lio, Pietro, Dhillon, Sarinder Kaur

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Privacy-Preserving Asynchronous Federated Learning Mechanism for Edge Network Computing von Lu, Xiaofeng, Liao, Yuying, Lio, Pietro, Hui, Pan

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Single-cell RNA-sequencing uncovers transcriptional states and fate decisions in haematopoiesis von Athanasiadis, Emmanouil I., Botthof, Jan G., Andres, Helena, Ferreira, Lauren, Lio, Pietro, Cvejic, Ana

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DADIM: A distance adjustment dynamic influence map model von Lu, Xiaofeng, Wang, Xiaoming, Lio’, Pietro, Hui, Pan

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HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN von Islam, Md Shofiqul, Hasan, Khondokar Fida, Sultana, Sunjida, Uddin, Shahadat, Lio’, Pietro, Quinn, Julian M.W., Moni, Mohammad Ali

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AI-Based Reconstruction for Fast MRI-A Systematic Review and Meta-Analysis von Chen, Yutong, Schonlieb, Carola-Bibiane, Lio, Pietro, Leiner, Tim, Dragotti, Pier Luigi, Wang, Ge, Rueckert, Daniel, Firmin, David, Yang, Guang

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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans von Roberts, Michael, Driggs, Derek, Thorpe, Matthew, Gilbey, Julian, Yeung, Michael, Ursprung, Stephan, Aviles-Rivero, Angelica I., Etmann, Christian, McCague, Cathal, Beer, Lucian, Weir-McCall, Jonathan R., Teng, Zhongzhao, Gkrania-Klotsas, Effrossyni, Rudd, James H. F., Sala, Evis, Schönlieb, Carola-Bibiane

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Combining a wavelet change point and the Bayes factor for analysing chromosomal interaction data von Shavit, Yoli, Lio', Pietro

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