Directed networks as a novel way to describe and analyze cardiac excitation : directed graph mapping

Networks provide a powerful methodology with applications in a variety of biological, technological and social systems such as analysis of brain data, social networks, internet search engine algorithms, etc. To date, directed networks have not yet been applied to characterize the excitation of the human heart. In clinical practice, cardiac excitation is recorded by multiple discrete electrodes. During (normal) sinus rhythm or during cardiac arrhythmias, successive excitation connects neighboring electrodes, resulting in their own unique directed network. This in theory makes it a perfect fit for directed network analysis. In this study, we applied directed networks to the heart in order to describe and characterize cardiac arrhythmias. Proof-of-principle was established using in-silico and clinical data. We demonstrated that tools used in network theory analysis allow determination of the mechanism and location of certain cardiac arrhythmias. We show that the robustness of this approach can potentially exceed the existing state-of-the art methodology used in clinics. Furthermore, implementation of these techniques in daily practice can improve the accuracy and speed of cardiac arrhythmia analysis. It may also provide novel insights in arrhythmias that are still incompletely understood

Utvrđivanje povezanosti genotipa i fenotipa hipertrofične kardiomiopatije primenom mašinskog učenja

Hypertrophic cardiomyopathy (HCM) is the most prevailing heritable cardiomyopathy. HCM is diagnosed by the existence of left ventricular hypertrophy despite the lack of abnormal loading conditions causing it. HCM is a heterogeneous disease regarding genetic mutations. Clinical manifestations and prognosis vary widely as well. Some patients are completely asymptomatic, in some others, severe heart failure and sudden cardiac death may arise. Definitive genotype-phenotype associations are still unknown. Machine learning (ML) is a subdiscipline of artificial intelligence, wherein computer algorithms are used for learning complex patterns from data. The aim of this research was to decipher genotype-phenotype associations in HCM using ML. The study was multi-centric and retroprospective, and involved 143 adult HCM patients. Medical and family history, anthropometric measurements, genetic testing, blood markers, transthoracic echocardiography with Doppler, cardiopulmonary exercise testing (CPET), ECG and ECG-holter-monitoring data were collected and further analysed. HCM subphenotypes were identified using clustering. Associations of genotype and phenotype were evaluated used Python modules Scikit-learn and SHapley Additive exPlanation (SHAP). Genotype-specific echocardiogram findings were identified using Python deep learning (DL) and computer vision library Fast AI, by generation of DL models for classification of ultrasonic images, and later analysis of the most decisive image regions. Four HCM subtypes were identified based on the overall phenotypic appearance: cluster 0 (“AHOLD”), distinguishable by aortic root diameter (AO) and lactate dehydrogenase (LDH), with values mostly AO > 30 mm, and LDH > 300 U/L; cluster 1 (“RVSP ASCAOVS”), distinguishable by right ventricle systolic pressure (RVSP), diameter of ascending aorta (AscAO), and aortic leaflet separation diameter (AOvs), with the values of RVSP 27 m/s; cluster 2 (“weight”), recognizable by weight, wherein values being mostly > 95 kg; and cluster 3 (“AV LVOT PG”) distinguishable by aortic valve mean pressure gradient (AV meanPG), aortic valve peak pressure gradient (AV maxPG), and left ventricular outflow tract peak gradient (LVOT maxPG) wherein AV maxPG > 15 mmHg, AV meanPG > 6 mmHg, and LVOT maxPG > 15 mmHg. ML algorithms confirmed that the determination of genotype-phenotype associations in HCM is a cumbersome task. Two phenotypic outcomes that can be predicted from mutated genes are the absence or presence of sinus rhythm and the absence or presence of myocardial injury. Models predicting the absence or presence of sinus rhythm had similar performance when they were built using only causative genes and when using all analyzed genes, indicating potential importance of causative genes and irrelevance of non-causative genes for that outcome. On the other hand, models predicting myocardial injury — infarction had better performance when they were built using all analyzed genes (and not just causative ones), indicating a potentially significant role of non-causative genes in that outcome. The ML algorithms were able to predict phenotypic outcomes — fatigue, dyspnea, chest pain, palpitations, syncope, heart murmur, pretibial edema, systolic anterior motion, papillary muscle abnormalities, hypokinesia, atrial fibrillation (AF), first-degree atrioventricular (AV) block, left bundle branch block (LBBB), right bundle branch block (RBBB), left anterior hemiblock, ST segment abnormalities, and negative T wave — using genotypic and phenotypic data. The combination of a mutation in TNNT2 and peak respiratory exchange ratio (RER) contributed the most in predicting fatigue. The combination of a mutation in MYBPC3 and peak VO2 contributed the most in predicting dyspnea. The combination of a mutation in TNNI3 and high-density lipoprotein (HDL) level contributed the most in predicting chest pain. The combination of a mutation in MYH7 and pacemaker/defibrillator implants in family history, as well as the combination of a mutation in TNNT2 and left atrial volume (LAV), contributed the most in predicting heart murmur. Lastly, the combination of a mutation in MYBPC3 and transmitral maximal pressure gradient (MV maxPG) aided the most in predicting negative T wave. Genotype-specific echocardiogram findings were identified: for mutations in the MYH7 gene (vs. mutation not detected), the most discriminative structures are the left ventricular outflow tract, septum, anterior wall, apex, right ventricle, and mitral apparatus; for mutations in the TNNT2 gene (vs. mutation not detected), the most discriminative structures are septum and right ventricle; while for mutations in MYBPC3 gene (vs. mutation not detected) these are septum, left ventricle, and left ventricle chamber. ML has thus been demonstrated to be useful in deciphering genotype-phenotype associations in HCM.Hipertrofična kardiomiopatija (HCM) je najčešća nasledna kardiomiopatija. Dijagnoza HCM se postavlja na osnovu prisustva hipertrofije leve komore, uz isključivanje drugih uzroka hipertrofije. U pogledu genetičkih mutacija, HCM je heterogena bolest. Kliničke manifestacije i prognoza takođe mogu da budu veoma različite. Kod nekih pacijenata HCM je potpuno asimptomatska, dok kod drugih mogu da se razviju teška srčana insuficijencija i iznenadna srčana smrt. Povezanost genotipa i fenotipa HCM još uvek nije u potpunosti utvrđena. Mašinsko učenje je subdisciplina veštačke inteligencije u kojoj se kompjuterski algoritmi koriste za učenje kompleksnih šablona iz podataka. Cilj ovog istraživanja je bilo utvrđivanje povezanosti genotipa i fenotipa HCM primenom mašinskog učenja. Studija je bila multicentrična i retroprospektivna, obuhvatila je 143 odrasla pacijenta sa potvrđenom dijagnozom HCM. Anamnestički podaci, antropometrijska merenja, rezultati genetičkog testiranja, biohemijskih analiza, nalazi transtorakalne ehokardiografije sa doplerom, kardiopulmonalnog testa fizičkim opterećenjem, elektrokardiograma (EKG) i EKG-holter-monitoringa su prikupljeni i korišćeni u daljoj analizi. HCM subfenotipi su identifikovani klasterizacijom. Povezanost genotipa i fenotipa je evaluirana korišćenjem Python modula Scikit-learn i SHapley Additive exPlanation (SHAP). Genotip-specifični nalazi ehokardiograma su identifikovani korišćenjem Python biblioteke za duboko učenje i računarski vid Fast AI, izradom modela za klasifikaciju ehokardiograma i naknadnom analizom regiona koji su najviše doprineli razlikovanju klasa. Četiri podtipa HCM su identifikovana na osnovu svih dostupnih podataka o fenotipu: klaster 0 (“AHOLD”), koji se razlikuje od ostalih na osnovu prečnika korena aorte (AO) i laktat dehidrogenaze (LDH), pri čemu su vrednosti AO > 30 mm i LDH > 300 U/L; klaster 1 (“RVSP ASCAOVS”), koji se razlikuje od ostalih na osnovu sistolnog pritiska desne komore (RVSP), dijametra ascedentne aorte (AscAO), i separacije aortnih kuspisa (AOvs), pri čemu su vrednosti AOvs > 27 m/s, AscAO 95 kg; i klaster 3 (“AV LVOT PG”) koji se razlikuje od ostalih na osnovu srednjeg gradijenta pritisaka nad aortnom valvulom (AV meanPG), maksimalnog gradijenta pritisaka nad aortnom valvulom (AV maxPG), i maksimalnog gradijenta pritisaka nad izlaznim traktom leve komore (LVOT maxPG), pri čemu su vrednosti AV maxPG > 15 mmHg, AV meanPG > 6 mmHg, i LVOT maxPG > 15 mmHg. Algoritmi mašinskog učenja su potvrdili da utvrđivanje povezanosti genotipa i fenotipa HCM nije jednostavan zadatak. Predikcija ishoda fenotipa na osnovu informacije o mutiranim genima je moguća za prisustvo ili odsustvo sinusnog ritma i prisustvo ili odsustvo oštećenja miokarda. Modeli koji vrše predikciju prisustva ili odsustva sinusnog ritma su imali slične performanse kada su izrađeni samo na osnovu uzročnih gena za HCM i kada su izrađeni na osnovu svih analiziranih gena što sugeriše mogući značaj uzročnih gena za HCM i irelevantnost drugih analiziranih gena za ovaj ishod. Modeli koji vrše predikciju oštećenja miokarda su imali bolje performanse kada su korišćeni podaci o svim analiziranim genima (a ne samo o uzročnim genima za HCM), što sugeriše moguću važnu ulogu gena koji nisu uzročni, za ovaj ishod. Algoritmi mašinskog učenja su izvršili predikciju sledećih ishoda na osnovu podataka o genotipu i fenotipu: zamor, dispneja, bol u grudima, palpitacije, sinkopa, šum na srcu, pretibijalni edem, pokretanje mitralnog zalistka unapred (SAM), abnormalnost papilarnih mišića, hipokinezija, atrijalna fibrilacija, atrioventrikularni blok prvog stepena, blok leve grane (LBBB), blok desne grane (RBBB), prednji levi hemiblok, abnormalnosti ST segmenta, i negativni T talas. Prilikom predikcije zamora, najveći doprinos je imala kombinacija mutacije u TNNT2 i maksimalnog odnosa disajne razmene (RER). Prilikom predikcije dispneje najveći doprinos imala je kombinacija mutacije u MYBPC3 i vršne potrošnje kiseonika (peak VO2). Prilikom predikcije bola u grudima, najveći doprinos je imala kombinacija mutacije u TNNI3 i koncentracije lipoproteina visoke gustine (eng. high-density lipoprotein, HDL). Prilikom predikcije šuma na srcu najveći doprinos imala je kombinacija mutacije u MYH7 i podatka o implantiranju pejsmejkera/defibrilatora u porodičnoj istoriji, kao i kombinacija mutacije u TNNT2 i zapremine leve pretkomore (LAV). Prilikom predikcije negativnog T talasa, najveći doprinos imala je kombinacija mutacije u MYBPC3 i vrednosti transmitralnog maksimalnog gradijenta pritiska (MV maxPG). Identifikovani su genotip-specifični nalazi ehokardiograma: za mutaciju u MYH7 genu (nasuprot negativnom rezultatu na mutacije u analiziranim genima), strukture koje najviše utiču na raspoznavanje su septum, izlazni trakt leve komore (LVOT), prednji zid, vrh srca, desna komora i mitralni aparat; za mutaciju u TNNT2 genu (nasuprot negativnom rezultatu na mutacije u analiziranim genima) strukture koje najviše utiču na raspoznavanje su septum i desna komora; dok su za mutaciju u MYBPC3 genu (nasuprot negativnom rezultatu na mutacije u analiziranim genima) ove strukture septum, leva komora i šupljina leve komore. Mašinsko učenje je na ovaj način doprinelo u određenoj meri izučavanju povezanosti genotipa i fenotipa HCM

Recent Trends in Computational Research on Diseases

Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields. Statistical methodologies based on high-performance computing and big data analysis are now indispensable for the qualitative and quantitative understanding of experimental results. In fact, the last few decades have witnessed drastic improvements in high-throughput experiments in health science, for example, mass spectrometry, DNA microarray, next generation sequencing, etc. Those methods have been providing massive data involving four major branches of omics (genomics, transcriptomics, proteomics, and metabolomics). Information about amino acid sequences, protein structures, and molecular structures are fundamental data for the prediction of bioactivity of chemical compounds when screening drugs. On the other hand, cell imaging, clinical imaging, and personal healthcare devices are also providing important data concerning the human body and disease. In parallel, various methods of mathematical modelling such as machine learning have developed rapidly. All of these types of data can be utilized in computational approaches to understand disease mechanisms, diagnosis, prognosis, drug discovery, drug repositioning, disease biomarkers, driver mutations, copy number variations, disease pathways, and much more. In this Special Issue, we have published 8 excellent papers dedicated to a variety of computational problems in the biomedical field from the genomic level to the whole-person physiological level

Utvrđivanje povezanosti genotipa i fenotipa hipertrofične kardiomiopatije primenom mašinskog učenja

Hypertrophic cardiomyopathy (HCM) is the most prevailing heritable cardiomyopathy. HCM is diagnosed by the existence of left ventricular hypertrophy despite the lack of abnormal loading conditions causing it. HCM is a heterogeneous disease regarding genetic mutations. Clinical manifestations and prognosis vary widely as well. Some patients are completely asymptomatic, in some others, severe heart failure and sudden cardiac death may arise. Definitive genotype-phenotype associations are still unknown. Machine learning (ML) is a subdiscipline of artificial intelligence, wherein computer algorithms are used for learning complex patterns from data. The aim of this research was to decipher genotype-phenotype associations in HCM using ML. The study was multi-centric and retroprospective, and involved 143 adult HCM patients. Medical and family history, anthropometric measurements, genetic testing, blood markers, transthoracic echocardiography with Doppler, cardiopulmonary exercise testing (CPET), ECG and ECG-holter-monitoring data were collected and further analysed. HCM subphenotypes were identified using clustering. Associations of genotype and phenotype were evaluated used Python modules Scikit-learn and SHapley Additive exPlanation (SHAP). Genotype-specific echocardiogram findings were identified using Python deep learning (DL) and computer vision library Fast AI, by generation of DL models for classification of ultrasonic images, and later analysis of the most decisive image regions. Four HCM subtypes were identified based on the overall phenotypic appearance: cluster 0 (“AHOLD”), distinguishable by aortic root diameter (AO) and lactate dehydrogenase (LDH), with values mostly AO > 30 mm, and LDH > 300 U/L; cluster 1 (“RVSP ASCAOVS”), distinguishable by right ventricle systolic pressure (RVSP), diameter of ascending aorta (AscAO), and aortic leaflet separation diameter (AOvs), with the values of RVSP 27 m/s; cluster 2 (“weight”), recognizable by weight, wherein values being mostly > 95 kg; and cluster 3 (“AV LVOT PG”) distinguishable by aortic valve mean pressure gradient (AV meanPG), aortic valve peak pressure gradient (AV maxPG), and left ventricular outflow tract peak gradient (LVOT maxPG) wherein AV maxPG > 15 mmHg, AV meanPG > 6 mmHg, and LVOT maxPG > 15 mmHg. ML algorithms confirmed that the determination of genotype-phenotype associations in HCM is a cumbersome task. Two phenotypic outcomes that can be predicted from mutated genes are the absence or presence of sinus rhythm and the absence or presence of myocardial injury. Models predicting the absence or presence of sinus rhythm had similar performance when they were built using only causative genes and when using all analyzed genes, indicating potential importance of causative genes and irrelevance of non-causative genes for that outcome. On the other hand, models predicting myocardial injury — infarction had better performance when they were built using all analyzed genes (and not just causative ones), indicating a potentially significant role of non-causative genes in that outcome. The ML algorithms were able to predict phenotypic outcomes — fatigue, dyspnea, chest pain, palpitations, syncope, heart murmur, pretibial edema, systolic anterior motion, papillary muscle abnormalities, hypokinesia, atrial fibrillation (AF), first-degree atrioventricular (AV) block, left bundle branch block (LBBB), right bundle branch block (RBBB), left anterior hemiblock, ST segment abnormalities, and negative T wave — using genotypic and phenotypic data. The combination of a mutation in TNNT2 and peak respiratory exchange ratio (RER) contributed the most in predicting fatigue. The combination of a mutation in MYBPC3 and peak VO2 contributed the most in predicting dyspnea. The combination of a mutation in TNNI3 and high-density lipoprotein (HDL) level contributed the most in predicting chest pain. The combination of a mutation in MYH7 and pacemaker/defibrillator implants in family history, as well as the combination of a mutation in TNNT2 and left atrial volume (LAV), contributed the most in predicting heart murmur. Lastly, the combination of a mutation in MYBPC3 and transmitral maximal pressure gradient (MV maxPG) aided the most in predicting negative T wave. Genotype-specific echocardiogram findings were identified: for mutations in the MYH7 gene (vs. mutation not detected), the most discriminative structures are the left ventricular outflow tract, septum, anterior wall, apex, right ventricle, and mitral apparatus; for mutations in the TNNT2 gene (vs. mutation not detected), the most discriminative structures are septum and right ventricle; while for mutations in MYBPC3 gene (vs. mutation not detected) these are septum, left ventricle, and left ventricle chamber. ML has thus been demonstrated to be useful in deciphering genotype-phenotype associations in HCM.Hipertrofična kardiomiopatija (HCM) je najčešća nasledna kardiomiopatija. Dijagnoza HCM se postavlja na osnovu prisustva hipertrofije leve komore, uz isključivanje drugih uzroka hipertrofije. U pogledu genetičkih mutacija, HCM je heterogena bolest. Kliničke manifestacije i prognoza takođe mogu da budu veoma različite. Kod nekih pacijenata HCM je potpuno asimptomatska, dok kod drugih mogu da se razviju teška srčana insuficijencija i iznenadna srčana smrt. Povezanost genotipa i fenotipa HCM još uvek nije u potpunosti utvrđena. Mašinsko učenje je subdisciplina veštačke inteligencije u kojoj se kompjuterski algoritmi koriste za učenje kompleksnih šablona iz podataka. Cilj ovog istraživanja je bilo utvrđivanje povezanosti genotipa i fenotipa HCM primenom mašinskog učenja. Studija je bila multicentrična i retroprospektivna, obuhvatila je 143 odrasla pacijenta sa potvrđenom dijagnozom HCM. Anamnestički podaci, antropometrijska merenja, rezultati genetičkog testiranja, biohemijskih analiza, nalazi transtorakalne ehokardiografije sa doplerom, kardiopulmonalnog testa fizičkim opterećenjem, elektrokardiograma (EKG) i EKG-holter-monitoringa su prikupljeni i korišćeni u daljoj analizi. HCM subfenotipi su identifikovani klasterizacijom. Povezanost genotipa i fenotipa je evaluirana korišćenjem Python modula Scikit-learn i SHapley Additive exPlanation (SHAP). Genotip-specifični nalazi ehokardiograma su identifikovani korišćenjem Python biblioteke za duboko učenje i računarski vid Fast AI, izradom modela za klasifikaciju ehokardiograma i naknadnom analizom regiona koji su najviše doprineli razlikovanju klasa. Četiri podtipa HCM su identifikovana na osnovu svih dostupnih podataka o fenotipu: klaster 0 (“AHOLD”), koji se razlikuje od ostalih na osnovu prečnika korena aorte (AO) i laktat dehidrogenaze (LDH), pri čemu su vrednosti AO > 30 mm i LDH > 300 U/L; klaster 1 (“RVSP ASCAOVS”), koji se razlikuje od ostalih na osnovu sistolnog pritiska desne komore (RVSP), dijametra ascedentne aorte (AscAO), i separacije aortnih kuspisa (AOvs), pri čemu su vrednosti AOvs > 27 m/s, AscAO 95 kg; i klaster 3 (“AV LVOT PG”) koji se razlikuje od ostalih na osnovu srednjeg gradijenta pritisaka nad aortnom valvulom (AV meanPG), maksimalnog gradijenta pritisaka nad aortnom valvulom (AV maxPG), i maksimalnog gradijenta pritisaka nad izlaznim traktom leve komore (LVOT maxPG), pri čemu su vrednosti AV maxPG > 15 mmHg, AV meanPG > 6 mmHg, i LVOT maxPG > 15 mmHg. Algoritmi mašinskog učenja su potvrdili da utvrđivanje povezanosti genotipa i fenotipa HCM nije jednostavan zadatak. Predikcija ishoda fenotipa na osnovu informacije o mutiranim genima je moguća za prisustvo ili odsustvo sinusnog ritma i prisustvo ili odsustvo oštećenja miokarda. Modeli koji vrše predikciju prisustva ili odsustva sinusnog ritma su imali slične performanse kada su izrađeni samo na osnovu uzročnih gena za HCM i kada su izrađeni na osnovu svih analiziranih gena što sugeriše mogući značaj uzročnih gena za HCM i irelevantnost drugih analiziranih gena za ovaj ishod. Modeli koji vrše predikciju oštećenja miokarda su imali bolje performanse kada su korišćeni podaci o svim analiziranim genima (a ne samo o uzročnim genima za HCM), što sugeriše moguću važnu ulogu gena koji nisu uzročni, za ovaj ishod. Algoritmi mašinskog učenja su izvršili predikciju sledećih ishoda na osnovu podataka o genotipu i fenotipu: zamor, dispneja, bol u grudima, palpitacije, sinkopa, šum na srcu, pretibijalni edem, pokretanje mitralnog zalistka unapred (SAM), abnormalnost papilarnih mišića, hipokinezija, atrijalna fibrilacija, atrioventrikularni blok prvog stepena, blok leve grane (LBBB), blok desne grane (RBBB), prednji levi hemiblok, abnormalnosti ST segmenta, i negativni T talas. Prilikom predikcije zamora, najveći doprinos je imala kombinacija mutacije u TNNT2 i maksimalnog odnosa disajne razmene (RER). Prilikom predikcije dispneje najveći doprinos imala je kombinacija mutacije u MYBPC3 i vršne potrošnje kiseonika (peak VO2). Prilikom predikcije bola u grudima, najveći doprinos je imala kombinacija mutacije u TNNI3 i koncentracije lipoproteina visoke gustine (eng. high-density lipoprotein, HDL). Prilikom predikcije šuma na srcu najveći doprinos imala je kombinacija mutacije u MYH7 i podatka o implantiranju pejsmejkera/defibrilatora u porodičnoj istoriji, kao i kombinacija mutacije u TNNT2 i zapremine leve pretkomore (LAV). Prilikom predikcije negativnog T talasa, najveći doprinos imala je kombinacija mutacije u MYBPC3 i vrednosti transmitralnog maksimalnog gradijenta pritiska (MV maxPG). Identifikovani su genotip-specifični nalazi ehokardiograma: za mutaciju u MYH7 genu (nasuprot negativnom rezultatu na mutacije u analiziranim genima), strukture koje najviše utiču na raspoznavanje su septum, izlazni trakt leve komore (LVOT), prednji zid, vrh srca, desna komora i mitralni aparat; za mutaciju u TNNT2 genu (nasuprot negativnom rezultatu na mutacije u analiziranim genima) strukture koje najviše utiču na raspoznavanje su septum i desna komora; dok su za mutaciju u MYBPC3 genu (nasuprot negativnom rezultatu na mutacije u analiziranim genima) ove strukture septum, leva komora i šupljina leve komore. Mašinsko učenje je na ovaj način doprinelo u određenoj meri izučavanju povezanosti genotipa i fenotipa HCM

Stories from different worlds in the universe of complex systems: A journey through microstructural dynamics and emergent behaviours in the human heart and financial markets

A physical system is said to be complex if it exhibits unpredictable structures, patterns or regularities emerging from microstructural dynamics involving a large number of components. The study of complex systems, known as complexity science, is maturing into an independent and multidisciplinary area of research seeking to understand microscopic interactions and macroscopic emergence across a broad spectrum systems, such as the human brain and the economy, by combining specific modelling techniques, data analytics, statistics and computer simulations. In this dissertation we examine two different complex systems, the human heart and financial markets, and present various research projects addressing specific problems in these areas. Cardiac fibrillation is a diffuse pathology in which the periodic planar electrical conduction across the cardiac tissue is disrupted and replaced by fast and disorganised electrical waves. In spite of a century-long history of research, numerous debates and disputes on the mechanisms of cardiac fibrillation are still unresolved while the outcomes of clinical treatments remain far from satisfactory. In this dissertation we use cellular automata and mean-field models to qualitatively replicate the onset and maintenance of cardiac fibrillation from the interactions among neighboring cells and the underlying topology of the cardiac tissue. We use these models to study the transition from paroxysmal to persistent atrial fibrillation, the mechanisms through which the gap-junction enhancer drug Rotigaptide terminates cardiac fibrillation and how focal and circuital drivers of fibrillation may co-exist as projections of transmural electrical activities. Financial markets are hubs in which heterogeneous participants, such as humans and algorithms, adopt different strategic behaviors to exchange financial assets. In recent decades the widespread adoption of algorithmic trading, the electronification of financial transactions, the increased competition among trading venues and the use of sophisticated financial instruments drove the transformation of financial markets into a global and interconnected complex system. In this thesis we introduce agent-based and state-space models to describe specific microstructural dynamics in the stock and foreign exchange markets. We use these models to replicate the emergence of cross-currency correlations from the interactions between heterogeneous participants in the currency market and to disentangle the relationships between price fluctuations, market liquidity and demand/supply imbalances in the stock market.Open Acces

Discovering new drug-drug interactions using data science: Applications to drug-induced Long QT Syndrome

Commonly prescribed small molecule drugs can have net-positive and well-understood safety profiles when prescribed individually, but unexpected consequences when taken at the same time. Detection of these drug-drug interactions (DDIs) continues to be a critical and unmet area of translational research. The Centers for Disease Control and Prevention (CDC) estimate that one third of Americans are concurrently taking two or more prescription drugs, and DDIs are estimated to be responsible for 17% of all drug adverse events. The consequences of DDIs can be relatively minor (headache, skin rash) or much more severe (bleeding, liver toxicity). At a cellular level, DDIs can occur as a result of both drugs competing for metabolism (known as pharmacokinetic interactions) or targeting the same protein target or biological pathway (pharmacodynamic interactions). Clinical trials typically focus on the effects of individual drugs, leaving DDIs to usually be discovered only after the drugs have been approved. One of the most carefully studied drug adverse events is long QT syndrome (LQTS), an unexpected change in the heart's electrical activity that can lead to a potentially fatal ventricular tachycardia known as torsades de pointes (TdP). Some patients have genetic mutations that lead to congenital forms of LQTS, while drug-induced LQTS typically occurs via block of the hERG potassium channel (KCNH2) responsible for ventricular repolarization. After a number of high profile drugs were withdrawn from the market due to discovered risk of TdP, the FDA issued guidelines so that pharmaceutical companies could anticipate and test for this side effect before a new drug is approved. These recommendations have helped prevent new QT-prolonging drugs from entering the market, but nonetheless over 180 approved drugs have been associated with drug-induced LQTS. While information on individual QT-prolonging drugs is thus readily available to clinicians, little has remained known about DDIs (QT-DDIs). There are many more commonly prescribed drugs that are safe when given individually but could increase TdP risk when administered together. This troubling situation is compounded by the fact that traditional post-market surveillance algorithms are poorly equipped to sensitively and specifically detect DDIs. Data science – the application of rigorous analytical methods to large datasets – offers an opportunity for predicting previously unknown QT-DDIs. Some biomedical datasets (such as drug-target binding affinities and experiments to determine protein-protein interactions) have been collected explicitly for research, while other valuable datasets (such as electronic health records) were initially recorded for billing purposes. Each data modality has its own important set of advantages and disadvantages, and integrative data science approaches can incorporate multiple types of data to help account for these limitations. In this thesis we develop new data sciences techniques that combine clinical, biological, chemical, and genetic data. These approaches are explicitly designed to be robust to biased and missing data. We apply these new methodologies to (1) predict new QT-DDIs, (2) validate them experimentally, and (3) investigate their molecular and genetic mechanisms. We exemplify this approach in the discovery of a previously unknown QT-DDI between ceftriaxone (cephalosporin antibiotic) and lansoprazole (proton pump inhibitor); importantly, both drugs have no cardiac indications and are safe when given individually. The clinical data mining, drug target prediction, biological network analysis, genetic ancestry prediction, and experimental validation methods described in this thesis form the basis for a comprehensive pipeline to predict QT-DDIs rapidly and robustly. They also provide an opportunity for further enriching our understanding of LQTS biology and ultimately enabling the design of safer drugs

Modelling and Estimation of Spatiotemporal Cardiac Electrical Dynamics

The heart is a complex biological system in which electrical activation signals initiate at the pacemaker cells, propagate through the heart tissue to both trigger and synchronise the mechanical contractions. Abnormalities in the cardiac electrical signals lead to dangerous cardiac arrhythmias. Therefore, understanding the functionalities of the cardiac electrical activity is essential for the development of novel techniques to facilitate advanced diagnosis and treatment for arrhythmia. By combining experimental or clinical electrophysiology data with mathematical models, system theoretic approaches can be used to provide quantitative insights into the normal and pathological mechanisms of the cardiac electrical activity. This thesis proposes model-based estimation methods to reconstruct and quantify the underlying spatiotemporal cardiac electrical dynamics from the cardiac electrogram measurements. Firstly, a statistical model-based estimation framework is proposed to reconstruct the tissue dynamics from the cardiac electrogram measurements. The reconstruction of the tissue dynamics is based on an integrated model of cardiac electrical activity, which incorporates the cardiac action potential dynamics at the cell-level, tissue-level and extracellular-level. The dynamics of the cardiac tissue is described using the monodomain tissue model, which is coupled with the continuous version of modified Mitchell-Schaeffer model. The resulting model equations are of infinite-dimensional form, which is converted into a finite-dimensional state-space representation via a model reduction method. In order to estimate the hidden state variables of the tissue dynamics from the cardiac electrogram measurements, a combined detection-estimation framework using a single filter unscented-transform based smoothing algorithm is proposed. The detection step in the proposed method enables the inclusion of localised stimulus events into the model-based estimation framework. The performance of the proposed algorithms are demonstrated using the modelled cardiac activation patterns of normal and reentrant conditions, in both one-dimensional and two-dimensional tissue field. The findings from this proposed study illustrate that the hidden state variables of the tissue model can be estimated from the electrogram measurements, simultaneously by detecting the stimulus events. Therefore, this method shows that the complex spatiotemporal cardiac activity can be reconstructed from the coarse electrograms using the state estimation methods. Secondly, a complex network modelling approach is proposed to quantify the spatiotemporal organisation of electrical activation during human ventricular fibrillation. The proposed network modelling approach includes three different methods based on correlation analysis, graph theoretical measures and hierarchical clustering. Using the proposed approach, the level of spatiotemporal organisation is quantified during three episodes of VF in ten patients, recorded using multi-electrode epicardial recordings with 30 s coronary perfusion, 150 s global myocardial ischaemia and 30 s reflow. The findings show a steady decline in spatiotemporal organisation from the onset of VF with coronary perfusion. Following this, a transient increases in spatiotemporal organisation is observed during global myocardial ischaemia. However, the decline in spatiotemporal organisation continued during reflow. The results are consistent across all patients, and are consistent with the numbers of phase singularities. The findings show that the complex spatiotemporal patterns can be studied using complex network analysis

Nonlinear Stochastic Dynamic Systems Approach for Personalized Prognostics of Cardiorespiratory Disorders

This research investigates an approach rooted in nonlinear stochastic dynamic systems principles for personalized prognostics of cardiorespiratory disorders in the emerging point-of-care (POC) treatment contexts. Such an approach necessitates new methods for (a) quantitative and personalized modeling of underlying cardiovascular system dynamics to serve as a virtual instrument to derive surrogate (hemodynamic) signals, (b) high-specificity diagnostics to identify and localize disorders, (c) real-time prediction to provide forecasts of impending disorder episodes, and (d) personalized prognosis of the short-term variations of the risk, necessary for effective treatment decisions, based on estimating the distribution of the times remaining till the onset of an anomaly episode. The specific contributions of the dissertation work are as follows: 1. Quantitative modeling for real-time synthesis of hemodynamic signals. Features extracted from ECG signals were used to construct atrioventricular excitation inputs to a nonlinear deterministic lumped parameter model of cardiovascular system dynamics. The model-derived hemodynamic signals, personalized to an individual's physiological and anatomical conditions, would lead to cost-effective virtual medical instruments necessary for personalized POC prognostics. 2. Random graph representation of the complex cardiac dynamics for disorder diagnostics. The quantifiers of a random walk on a network reconstructed from vectorcardiogram (VCG) were investigated for the detection and localization of cardiovascular disorders. Extensive tests with signals from PTB database of PhysioNet databank suggest that locations of myocardial infarction can be determined accurately (sensitivity of ~88% and specificity of ~92%) from tracking certain consistently estimated invariants of this random walk representation. 3. Nonparametric prediction modeling of disorder episodes. A Dirichlet process based mixture Gaussian process was utilized to track and forecast the evolution of the complex nonlinear and nonstationary cardiorespiratory dynamics underlying of the measured signal features and health states. Extensive sleep tests suggest that the method can predict an impending sleep apnea episode to accuracies (R^2) of 83% and 77% for 1 step and 3 step-ahead predictions, respectively.4. Color-coded random graph representation of the state space for personalized prognostic modeling. The prognostic model used the stochastic evolution of the transition pathways from a normal state to an anomalous state in the color-coded state space network to estimate the distribution of the remaining useful life. The prognostic model was validated using the data from ECG Apnea Database (Physionet.org). The model can predict the estimated time till a disorder (apnea episode) onset to within 15% of the observed times 1-45 min ahead of their inception.Industrial Engineering & Managemen

Biochemical and electrophysiological markers predictive of return of spontaneous circulation and post-resuscitation outcome

The majority of patients resuscitated from cardiac arrest (CA) subsequently die due to post-cardiac arrest syndrome (PCAS), whose mechanisms are only partially understood. We adopted an approach of untargeted/targeted plasma metabolomics in rats to identify metabolites involved in the mechanisms of PCAS to be tested as predictors of outcome. Activation of the kynurenine pathway (KP) for tryptophan (TRP) degradation was demonstrated in rats, pigs and in a small cohort of patients. Decreases in TRP occurred during the post-CA period and were accompanied by significant increases in KP metabolites, 3-hydroxyanthranilic acid (3 -HAA) and kynurenic acid in each species, that persisted up to 3-5 days post-CA (p<0.01). KP metabolites changes were significantly related to the severity of myocardial and cerebral injuries and survival. Finally, when tested in 155 patients resuscitated from CA, KP metabolites were significantly higher in patients with poor outcomes. The quality of chest compression (CC) is another major issue for cardiopulmonary resuscitation (CPR) success and survival. The decision whether to interrupt CC to deliver a defibrillation (DF) is difficult. The potential benefit of a DF guided by a real time ventricular fibrillation (YF) waveform analysis would maximize DF success, minimize CC interruptions and myocardial damage by repetitive and unnecessary DFs. We evaluated amplitude spectrum area (AMSA) as predictor of DF outcome in two large databases of out-of-hospital VFs, from US (609 patients) and Italy (1.617 patients). AMSA was significantly higher prior to a successful DF than prior to an unsuccessful one (p<0.0001). Thresholds for prediction of successful and unsuccessful DFs were 16-17 mV-Hz for success and <7 mV-Hz for failure, with a positive predictive value of 80% and a negative predictive value of 97%. AMSA was a better predictor of DF outcome (AUC 0.86, p<0.0001) compared to other VF parameters, i.e. amplitude and frequencies. In conclusion, AMSA would be a useful tool for guiding CPR