{"id":2194,"date":"2023-01-06T10:54:02","date_gmt":"2023-01-06T09:54:02","guid":{"rendered":"https:\/\/www.uni.lu\/fstm-fr\/events\/machine-learning-seminar-meeting-how-can-we-make-tumour-predictions-when-we-do-not-understand-everything\/"},"modified":"2023-01-06T10:54:02","modified_gmt":"2023-01-06T09:54:02","slug":"machine-learning-seminar-meeting-how-can-we-make-tumour-predictions-when-we-do-not-understand-everything","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/fstm-fr\/events\/machine-learning-seminar-meeting-how-can-we-make-tumour-predictions-when-we-do-not-understand-everything\/","title":{"rendered":"Machine Learning Seminar meeting: How can we make tumour predictions when we do not understand everything?"},"content":{"rendered":"

Abstract:<\/strong><\/p>

In clinical reality, the need of quantitative tumour growth and progression predictions is pivotal for designing individualised therapies. To achieve this a plethora of examinations is conducted to assess the tumour lesion state, spanning from blood sample analysis, clinical imaging (e.g.,\u00a0CT, MRI), biopsy sampling, -omics screening etc. Such medical data correspond to snapshots in time of the patient\u2019s state and in the current standard of care (SoC) their collection relies on the patient’s clinical presentation. This implies that we cannot acquire many data timepoints hampering the personalised calibration of mathematical models and their corresponding prediction potential. Moreover, many clinical data types are not useful in informing phenotypic plasticity models hindering their clinical applicability.\u00a0<\/p>

In a nutshell, the use of phenotypic plasticity models in the current cancer SoC faces the following challenges: (C1) data collection is sparse in time since it relies on patient\u2019s clinical presentation, (C2) we lack the knowledge of the precise pathways involved in regulating phenotypic plasticity mechanisms, and (C3) medical data cannot always inform mathematical models. Overcoming the afore-mentioned challenges to predict the future of a disease and propose an appropriate treatment (e.g., choice of a drug targeting proteins expressed in the tumour) is a formidable but not impossible task. In this talk I will present a novel methodology that combines mechanistic modelling and machine learning in delivering clinically relevant tumour growth predictions.<\/p>

Prof. Haralampos Hatzikirou<\/a><\/strong> is an Associate Professor at the Faculty of Mathematics at Khalifa University. His research is focused on developing a mathematical theory for cell decision-making in pathophysiological multicellular systems that has profound implications in tissue development and in biomedical problems, such as cancer or bacterial infections. Also, he develops novel methods that fuse mechanistic modelling and machine learning.<\/p>

The Machine Learning Seminar<\/strong> is a regular weekly seminar series aiming to harbour presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. The focus is thus on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences. More information about the ML Seminar, together with video recordings from past meetings you will find here: https:\/\/legato-team.eu\/seminars\/<\/a><\/p>

To register please send a mail to\u00a0Dr. Jakub Lengiewicz<\/a>.<\/strong><\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"

Abstract:In clinical reality, the need of quantitative tumour growth and progression predictions is pivotal for designing individualised therapies. To achieve this a plethora of examinations is conducted to assess the tumour lesion state, spanning from blood sample analysis, clinical imaging (e.g.,\u00a0CT, MRI), biopsy sampling, -omics screening etc. Such medical data correspond to snapshots in time of the patient\u2019s state and in the current standard of care (SoC) their collection relies on the patient’s clinical presentation. This implies that we cannot acquire many data timepoints hampering the personalised calibration of mathematical models and their corresponding prediction potential. Moreover, many clinical data types are not useful in informing phenotypic plasticity models hindering their clinical applicability.\u00a0In a nutshell, the use of phenotypic plasticity models in the current cancer SoC faces the following challenges: (C1) data collection is sparse in time since it relies on patient\u2019s clinical presentation, (C2) we lack the knowledge of the precise pathways involved in regulating phenotypic plasticity mechanisms, and (C3) medical data cannot always inform mathematical models. Overcoming the afore-mentioned challenges to predict the future of a disease and propose an appropriate treatment (e.g., choice of a drug targeting proteins expressed in the tumour) is a formidable but not impossible task. In this talk I will present a novel methodology that combines mechanistic modelling and machine learning in delivering clinically relevant tumour growth predictions.Prof. Haralampos Hatzikirou is an Associate Professor at the Faculty of Mathematics at Khalifa University. His research is focused on developing a mathematical theory for cell decision-making in pathophysiological multicellular systems that has profound implications in tissue development and in biomedical problems, such as cancer or bacterial infections. Also, he develops novel methods that fuse mechanistic modelling and machine learning.The Machine Learning Seminar is a regular weekly seminar series aiming to harbour presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. The focus is thus on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences. More information about the ML Seminar, together with video recordings from past meetings you will find here: https:\/\/legato-team.eu\/seminars\/To register please send a mail to\u00a0Dr. Jakub Lengiewicz.<\/p>\n","protected":false},"author":0,"featured_media":2195,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"event_start_date":"2023-01-11 10:00:00","event_end_date":"2023-01-11 11:00:00","event_speaker_name":"Prof. Haralampos Hatzikirou (Faculty of Mathematics, Khalifa University, United Arab Emirates)","event_speaker_link":"","event_is_online":false,"event_location":"Online","event_street":"","event_location_link":"","event_zip_code":"","event_city":"","event_country":"LU"},"events-topic":[302],"events-type":[],"organisation":[42,24],"authorship":[],"acf":[],"yoast_head":"\nMachine Learning Seminar meeting: How can we make tumour predictions when we do not understand everything? - FSTM I Uni.lu<\/title>\n<meta name=\"description\" content=\"Abstract:In clinical reality, the need of quantitative tumour growth and progression predictions is pivotal for designing individualised therapies. 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Moreover, many clinical data types are not useful in informing phenotypic plasticity models hindering their clinical applicability.\u00a0In a nutshell, the use of phenotypic plasticity models in the current cancer SoC faces the following challenges: (C1) data collection is sparse in time since it relies on patient\u2019s clinical presentation, (C2) we lack the knowledge of the precise pathways involved in regulating phenotypic plasticity mechanisms, and (C3) medical data cannot always inform mathematical models. Overcoming the afore-mentioned challenges to predict the future of a disease and propose an appropriate treatment (e.g., choice of a drug targeting proteins expressed in the tumour) is a formidable but not impossible task. In this talk I will present a novel methodology that combines mechanistic modelling and machine learning in delivering clinically relevant tumour growth predictions.Prof. Haralampos Hatzikirou is an Associate Professor at the Faculty of Mathematics at Khalifa University. His research is focused on developing a mathematical theory for cell decision-making in pathophysiological multicellular systems that has profound implications in tissue development and in biomedical problems, such as cancer or bacterial infections. Also, he develops novel methods that fuse mechanistic modelling and machine learning.The Machine Learning Seminar is a regular weekly seminar series aiming to harbour presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. 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Moreover, many clinical data types are not useful in informing phenotypic plasticity models hindering their clinical applicability.\u00a0In a nutshell, the use of phenotypic plasticity models in the current cancer SoC faces the following challenges: (C1) data collection is sparse in time since it relies on patient\u2019s clinical presentation, (C2) we lack the knowledge of the precise pathways involved in regulating phenotypic plasticity mechanisms, and (C3) medical data cannot always inform mathematical models. Overcoming the afore-mentioned challenges to predict the future of a disease and propose an appropriate treatment (e.g., choice of a drug targeting proteins expressed in the tumour) is a formidable but not impossible task. In this talk I will present a novel methodology that combines mechanistic modelling and machine learning in delivering clinically relevant tumour growth predictions.Prof. Haralampos Hatzikirou is an Associate Professor at the Faculty of Mathematics at Khalifa University. His research is focused on developing a mathematical theory for cell decision-making in pathophysiological multicellular systems that has profound implications in tissue development and in biomedical problems, such as cancer or bacterial infections. Also, he develops novel methods that fuse mechanistic modelling and machine learning.The Machine Learning Seminar is a regular weekly seminar series aiming to harbour presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. 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Moreover, many clinical data types are not useful in informing phenotypic plasticity models hindering their clinical applicability.\u00a0In a nutshell, the use of phenotypic plasticity models in the current cancer SoC faces the following challenges: (C1) data collection is sparse in time since it relies on patient\u2019s clinical presentation, (C2) we lack the knowledge of the precise pathways involved in regulating phenotypic plasticity mechanisms, and (C3) medical data cannot always inform mathematical models. Overcoming the afore-mentioned challenges to predict the future of a disease and propose an appropriate treatment (e.g., choice of a drug targeting proteins expressed in the tumour) is a formidable but not impossible task. In this talk I will present a novel methodology that combines mechanistic modelling and machine learning in delivering clinically relevant tumour growth predictions.Prof. Haralampos Hatzikirou is an Associate Professor at the Faculty of Mathematics at Khalifa University. His research is focused on developing a mathematical theory for cell decision-making in pathophysiological multicellular systems that has profound implications in tissue development and in biomedical problems, such as cancer or bacterial infections. Also, he develops novel methods that fuse mechanistic modelling and machine learning.The Machine Learning Seminar is a regular weekly seminar series aiming to harbour presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. The focus is thus on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences. More information about the ML Seminar, together with video recordings from past meetings you will find here: https:\/\/legato-team.eu\/seminars\/To register please send a mail to\u00a0Dr. Jakub Lengiewicz.","breadcrumb":{"@id":"https:\/\/www.uni.lu\/fstm-fr\/events\/machine-learning-seminar-meeting-how-can-we-make-tumour-predictions-when-we-do-not-understand-everything\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.uni.lu\/fstm-fr\/events\/machine-learning-seminar-meeting-how-can-we-make-tumour-predictions-when-we-do-not-understand-everything\/"]}]},{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.uni.lu\/fstm-fr\/events\/machine-learning-seminar-meeting-how-can-we-make-tumour-predictions-when-we-do-not-understand-everything\/#primaryimage","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2023\/01\/machine_learning_seminar_meeting_how_can_we_make_tumour_predictions_when_we_do_not_understand_everything.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2023\/01\/machine_learning_seminar_meeting_how_can_we_make_tumour_predictions_when_we_do_not_understand_everything.jpg","width":800,"height":600},{"@type":"BreadcrumbList","@id":"https:\/\/www.uni.lu\/fstm-fr\/events\/machine-learning-seminar-meeting-how-can-we-make-tumour-predictions-when-we-do-not-understand-everything\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.uni.lu\/fr"},{"@type":"ListItem","position":2,"name":"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine","item":"https:\/\/www.uni.lu\/fstm-fr\/"},{"@type":"ListItem","position":3,"name":"Events","item":"https:\/\/www.uni.lu\/fstm-fr\/events\/"},{"@type":"ListItem","position":4,"name":"Machine Learning Seminar meeting: How can we make tumour predictions when we do not understand everything?"}]},{"@type":"WebSite","@id":"https:\/\/www.uni.lu\/fstm-fr\/#website","url":"https:\/\/www.uni.lu\/fstm-fr\/","name":"FSTM","description":"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine I Uni.lu","publisher":{"@id":"https:\/\/www.uni.lu\/fstm-fr\/#organization"},"alternateName":"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine I Universit\u00e9 du Luxembourg","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.uni.lu\/fstm-fr\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"fr-FR"},{"@type":"Organization","@id":"https:\/\/www.uni.lu\/fstm-fr\/#organization","name":"FSTM - Universit\u00e9 du Luxembourg I Uni.lu","alternateName":"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine","url":"https:\/\/www.uni.lu\/fstm-fr\/","logo":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.uni.lu\/fstm-fr\/#\/schema\/logo\/image\/","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2026\/03\/03111744\/FSTM_SM-Profile_1600x1600px-scaled.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2026\/03\/03111744\/FSTM_SM-Profile_1600x1600px-scaled.jpg","width":2560,"height":2560,"caption":"FSTM - Universit\u00e9 du Luxembourg I Uni.lu"},"image":{"@id":"https:\/\/www.uni.lu\/fstm-fr\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/fstm.uni.lu\/","https:\/\/www.linkedin.com\/showcase\/fstm-uni-lu"]}]}},"_links":{"self":[{"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events\/2194"}],"collection":[{"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events"}],"about":[{"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/types\/events"}],"replies":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/comments?post=2194"}],"version-history":[{"count":0,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events\/2194\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/media\/2195"}],"wp:attachment":[{"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/media?parent=2194"}],"wp:term":[{"taxonomy":"events-topic","embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events-topic?post=2194"},{"taxonomy":"events-type","embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events-type?post=2194"},{"taxonomy":"organisation","embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/organisation?post=2194"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}