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The basal ganglia are evolutionary conserved nuclei located at the base of the forebrain. They are a central hub in the control of motion and their dysfunctions lead to a variety of movement related disorders, including Parkinson's disease (PD). The largest nucleus and main input stage of the basal ganglia is the striatum. It receives excitatory glutamatergic projections primarily from cortex and thalamus as well as modulatory dopaminergic input from the substantia nigra pars compacta and the ventral tegmental area. Striatal output is mediated by the direct and indirect pathway striatal projection neurons (dSPNs and iSPNs, respectively). In rodents, they account for 95% of the neurons, while the remaining 5% are interneurons, which do not project outside the striatum. The aim of this thesis is to develop an in silico striatal microcircuit in health and PD, and to compare these two networks using electrophysiological simulations and topological analysis. The neuron types included are the striatal projection neurons (dSPN and iSPN) and three of the main interneuron classes: FS, LTS and ChIN. Their multi-compartmental models are based on detailed morphological reconstructions, ion channels expression and electrophysiological ex vivo rodents experimental data from control and PD brains. In Paper A, a comparison between two methods commonly used to model ion channels was presented. In Paper B, the healthy striatal microcircuit was created. We presented a modelling framework called Snudda. It enables the creation of large-scale networks by: placing neurons using appropriate density, predicting synaptic connectivity based on touch detection and a set of pruning rules, setting up external input and modulation, and finally running the simulations. It is written in Python and uses the NEURON simulator. In Paper C, we conducted a computational study on the reciprocal interaction between ChIN and LTS interneurons. Specifically, we simulate the inhibition of LTS via muscarinic M, Basala ganglierna, som är placerade vid basen av framhjärnan, är evolutionärt konserverade kärnor. De utgör ett centralt nav för kontrollen av motoriken, och dysfunktioner i basala ganglierna leder till en mängd olika rörelserelaterade störningar, inklusive Parkinsons sjukdom (PD).Den största kärnan, är den del av basala ganglierna och som fungerar som det huvudsakliga input steget, är striatum. Den tar emot excitatoriska glutamaterga projektioner främst från cortex och thalamus såväl som modulerande dopaminerga input från substantia nigra pars compacta och det ventrala tegmentumområ det. Striatum projicerar till andra delar av basala ganglierna via de direkta och indirekta striatala projektionsneuronerna (dSPNs respektive iSPNs). De utaör 95% av neuronerna hos möss, medan de återstå ende 5% är interneuroner, som inte projicerar utanför striatum. Syftet med denna avhandling är att bygga en in silico modell av det lokala striatala neuronnätverket som kan användas för att förstå både det friska nätverket och hur det förändras vid PD. Dessa nätverksmodeller jämförs sedan med hjälp av biofysikaliskt detaljerade simuleringar samt genom användandet av topologisk analys. De neurontyper som ingår i modellen är de striatala projektionsneuronerna (dSPN och iSPN) och tre av de huvudsakliga interneurontyperna: FS, LTS och ChIN. Multi-kompartmentmodeller av dessa neurontyper baseras på detaljerade morfologiska rekonstruktioner av neuron, genuttryck av jonkanaler samt elektrofysiologiska ex vivo experimentella data från möss som representerar kontroll- och PD-hjärnor. I artikel A presenterades en jämförelse mellan två metoder som vanligtvis används för att modellera jonkanaler. I artikel B byggde vi en modell av det lokala striatala neuronnätverket. Vi beskriver ett ramverk som heter Snudda för att bygga nätverket. Det möjliggör skapandet av storskaliga modellnätverk genom att: först placera neuroner med den densitet som uppmätts; sedan prediceras synapsernas placering baserat p, QC 20231121

The investigation of the evolutionary history of organisms, both at the cellular level and at the species level, is a relevant research topic in computational biology. These investigations lead to a deeper understanding of developmental history, cancer progression, the genetic similarity of species, and more. One way to study the relations between single cells or species is to examine the differences in their genomes, including single nucleotide and copy number variations. The genetic materials need to be extracted and sequenced to be used in the analyses, but this data preparation is prone to errors. The development of sophisticated, probabilistic models is of the utmost importance in handling technological artifacts and including uncertainty in the analysis. In this compilation thesis, we studied various questions and presented four papers to address different challenges. First, we focused on single cells from healthy tissue and developed a probabilistic model to reconstruct the cell lineage tree. This task is challenging in several aspects; i) the healthy cells have a low mutation rate and, therefore, do not introduce many mutations at each cell division, ii) healthy cells usually do not have significant structural variations to improve the analysis, and iii) the sequencing technology introduces errors, and some of these errors are hard to distinguish from the mutations. With the experimental studies, we showed that our model is fast, robust, and accurately reconstructs lineage trees. Second, we focused on cancer cells. One research topic is identifying structural variations in the cancer cells' genomes and subsequently grouping the cells with similar genome profiles. This two-step process is vulnerable; the imperfections in the first step can irreversibly impact the analysis in the second step. To address this problem, we developed a variational inference-based model that simultaneously does copy number profiling and cell clustering. In addition, we extende, Undersökningen av organismers evolutionära historia, både på cellnivå och artnivå, är ett relevant forskningsämne inom beräkningsbiologi. Dessa studier leder till en djupare förståelse för utveckling, cancerprogression, arternas genetiska likhet med mera. Ett sätt att studera relationerna mellan enskilda celler eller arter är att undersöka skillnaderna i deras genom, inklusive enbaspolymorfier och kopienummervariationer. Det genetiska materialet behöver extraheras och sekvenseras för att användas i analyserna, men fel kan uppstå under databeredningen. Utvecklingen av sofistikerade, probabilistiska modeller är av yttersta vikt vid hantering av tekniska artefakter och inkludering av osäkerhet i analysen. I denna sammanställningsavhandling studerade vi olika frågeställningar och presenterade fyra artiklar för att ta itu med olika utmaningar. Först fokuserade vi på enstaka celler från frisk vävnad och utvecklade en probabilistisk modell för att rekonstruera cellhärkomstträdet. Denna uppgift är utmanande ur flera aspekter; i) de friska cellerna har en låg mutationshastighet och introducerar därför inte många mutationer vid varje celldelning, ii) friska celler har vanligtvis inte signifikanta strukturella variationer för att förbättra analysen; och iii) sekvenseringsteknologin introducerar fel, och några av dessa fel är svåra att skilja från mutationerna. Med den experimentella studien visade vi att vår modell är snabb, robust och exakt rekonstruerar härstamningsträd. För det andra fokuserade vi på cancerceller. Ett forskningsämne är att identifiera strukturella variationer i cancercellernas genom och därefter gruppera cellerna med liknande genomprofiler. Denna tvåstegsprocess är fragil; ofullkomligheterna i det första steget kan oåterkalleligt påverka analysen i det andra steget. För att lösa detta problem utvecklade vi en variationsbaserad modell som simultant utför kopienummerprofilering och cellklustring. Dessutom utökade vi modellen för att inkorporera enskilda enbas, Organizmaların evrimsel tarihinin hem hücresel hem de tür düzeyinde incelenmesi hesaplamalı biyolojide alâkalı bir araştırma konusudur. Bu konudaki araştırmalar, gelişim tarihi, kanser ilerlemesi, türlerin genetik benzerliği ve daha fazlası hakkında daha derin bir anlayışa rehberlik eder. Tek hücreler veya türler arasındaki ilişkileri incelemenin bir yolu, tek nükleotit ve kopya sayısı varyasyonları dahil olmak üzere genomlarındaki farklılıkları incelemektir. Analizlerde kullanılmak üzere genetik materyallerin çıkarılması ve dizilenmesi gerekir, ancak bu verilerin hazırlanması hatalara eğimlidir. Sofistike, olasılıksal modellerin geliştirilmesi, teknolojik hataların ele alınmasında ve belirsizliğin analize dahil edilmesinde son derece önemlidir. Bu derleme tezinde, çeşitli soruları inceledik ve farklı zorlukları ele almak için dört makale sunduk. İlk olarak, sağlıklı dokudaki tek hücrelere odaklandık ve hücre soy ağacını yeniden yapılandırmak için olasılıksal bir model geliştirdik. Bu görev birkaç açıdan zorlayıcıdır; i) sağlıklı hücreler düşük bir mutasyon oranına sahiptir, bu nedenle her hücre bölünmesinde pek çok mutasyon ortaya çıkarmazlar, ii) sağlıklı hücreler genellikle analizi geliştirmek için kayda değer yapısal varyasyonlara sahip değildirler; ve iii) dizileme teknolojisi hatalar ortaya çıkarır ve bu hataların bazılarını mutasyonlardan ayırt etmek zordur. Deneysel çalışmalar ile modelimizin hızlı ve gürbüz olduğunu, ve soy ağaçlarını doğru bir şekilde yeniden yapılandırdığını gösterdik. İkinci olarak, kanser hücrelerine odaklandık. Kanser hücrelerinin genomlarındaki yapısal varyasyonları belirlemek ve ardından benzer genom profillerine sahip hücreleri gruplandırmaktır bir araştırma konusudur. Bu iki adımlı sürecin hatalara zafiyeti vardır; ilk adımdaki kusurlar, ikinci adımdaki analizi geri döndürülemez şekilde etkileyebilir. Bu sorunu çözmek için, aynı anda kopya numarası profili ve hücre kümelemesi yapan varyasyonel çıkarıma dayalı bir model geliştirdik., QC 20230125

The investigation of the evolutionary history of organisms, both at the cellular level and at the species level, is a relevant research topic in computational biology. These investigations lead to a deeper understanding of developmental history, cancer progression, the genetic similarity of species, and more. One way to study the relations between single cells or species is to examine the differences in their genomes, including single nucleotide and copy number variations. The genetic materials need to be extracted and sequenced to be used in the analyses, but this data preparation is prone to errors. The development of sophisticated, probabilistic models is of the utmost importance in handling technological artifacts and including uncertainty in the analysis. In this compilation thesis, we studied various questions and presented four papers to address different challenges. First, we focused on single cells from healthy tissue and developed a probabilistic model to reconstruct the cell lineage tree. This task is challenging in several aspects; i) the healthy cells have a low mutation rate and, therefore, do not introduce many mutations at each cell division, ii) healthy cells usually do not have significant structural variations to improve the analysis, and iii) the sequencing technology introduces errors, and some of these errors are hard to distinguish from the mutations. With the experimental studies, we showed that our model is fast, robust, and accurately reconstructs lineage trees. Second, we focused on cancer cells. One research topic is identifying structural variations in the cancer cells' genomes and subsequently grouping the cells with similar genome profiles. This two-step process is vulnerable; the imperfections in the first step can irreversibly impact the analysis in the second step. To address this problem, we developed a variational inference-based model that simultaneously does copy number profiling and cell clustering. In addition, we extende, Undersökningen av organismers evolutionära historia, både på cellnivå och artnivå, är ett relevant forskningsämne inom beräkningsbiologi. Dessa studier leder till en djupare förståelse för utveckling, cancerprogression, arternas genetiska likhet med mera. Ett sätt att studera relationerna mellan enskilda celler eller arter är att undersöka skillnaderna i deras genom, inklusive enbaspolymorfier och kopienummervariationer. Det genetiska materialet behöver extraheras och sekvenseras för att användas i analyserna, men fel kan uppstå under databeredningen. Utvecklingen av sofistikerade, probabilistiska modeller är av yttersta vikt vid hantering av tekniska artefakter och inkludering av osäkerhet i analysen. I denna sammanställningsavhandling studerade vi olika frågeställningar och presenterade fyra artiklar för att ta itu med olika utmaningar. Först fokuserade vi på enstaka celler från frisk vävnad och utvecklade en probabilistisk modell för att rekonstruera cellhärkomstträdet. Denna uppgift är utmanande ur flera aspekter; i) de friska cellerna har en låg mutationshastighet och introducerar därför inte många mutationer vid varje celldelning, ii) friska celler har vanligtvis inte signifikanta strukturella variationer för att förbättra analysen; och iii) sekvenseringsteknologin introducerar fel, och några av dessa fel är svåra att skilja från mutationerna. Med den experimentella studien visade vi att vår modell är snabb, robust och exakt rekonstruerar härstamningsträd. För det andra fokuserade vi på cancerceller. Ett forskningsämne är att identifiera strukturella variationer i cancercellernas genom och därefter gruppera cellerna med liknande genomprofiler. Denna tvåstegsprocess är fragil; ofullkomligheterna i det första steget kan oåterkalleligt påverka analysen i det andra steget. För att lösa detta problem utvecklade vi en variationsbaserad modell som simultant utför kopienummerprofilering och cellklustring. Dessutom utökade vi modellen för att inkorporera enskilda enbas, Organizmaların evrimsel tarihinin hem hücresel hem de tür düzeyinde incelenmesi hesaplamalı biyolojide alâkalı bir araştırma konusudur. Bu konudaki araştırmalar, gelişim tarihi, kanser ilerlemesi, türlerin genetik benzerliği ve daha fazlası hakkında daha derin bir anlayışa rehberlik eder. Tek hücreler veya türler arasındaki ilişkileri incelemenin bir yolu, tek nükleotit ve kopya sayısı varyasyonları dahil olmak üzere genomlarındaki farklılıkları incelemektir. Analizlerde kullanılmak üzere genetik materyallerin çıkarılması ve dizilenmesi gerekir, ancak bu verilerin hazırlanması hatalara eğimlidir. Sofistike, olasılıksal modellerin geliştirilmesi, teknolojik hataların ele alınmasında ve belirsizliğin analize dahil edilmesinde son derece önemlidir. Bu derleme tezinde, çeşitli soruları inceledik ve farklı zorlukları ele almak için dört makale sunduk. İlk olarak, sağlıklı dokudaki tek hücrelere odaklandık ve hücre soy ağacını yeniden yapılandırmak için olasılıksal bir model geliştirdik. Bu görev birkaç açıdan zorlayıcıdır; i) sağlıklı hücreler düşük bir mutasyon oranına sahiptir, bu nedenle her hücre bölünmesinde pek çok mutasyon ortaya çıkarmazlar, ii) sağlıklı hücreler genellikle analizi geliştirmek için kayda değer yapısal varyasyonlara sahip değildirler; ve iii) dizileme teknolojisi hatalar ortaya çıkarır ve bu hataların bazılarını mutasyonlardan ayırt etmek zordur. Deneysel çalışmalar ile modelimizin hızlı ve gürbüz olduğunu, ve soy ağaçlarını doğru bir şekilde yeniden yapılandırdığını gösterdik. İkinci olarak, kanser hücrelerine odaklandık. Kanser hücrelerinin genomlarındaki yapısal varyasyonları belirlemek ve ardından benzer genom profillerine sahip hücreleri gruplandırmaktır bir araştırma konusudur. Bu iki adımlı sürecin hatalara zafiyeti vardır; ilk adımdaki kusurlar, ikinci adımdaki analizi geri döndürülemez şekilde etkileyebilir. Bu sorunu çözmek için, aynı anda kopya numarası profili ve hücre kümelemesi yapan varyasyonel çıkarıma dayalı bir model geliştirdik., QC 20230125

The investigation of the evolutionary history of organisms, both at the cellular level and at the species level, is a relevant research topic in computational biology. These investigations lead to a deeper understanding of developmental history, cancer progression, the genetic similarity of species, and more. One way to study the relations between single cells or species is to examine the differences in their genomes, including single nucleotide and copy number variations. The genetic materials need to be extracted and sequenced to be used in the analyses, but this data preparation is prone to errors. The development of sophisticated, probabilistic models is of the utmost importance in handling technological artifacts and including uncertainty in the analysis. In this compilation thesis, we studied various questions and presented four papers to address different challenges. First, we focused on single cells from healthy tissue and developed a probabilistic model to reconstruct the cell lineage tree. This task is challenging in several aspects; i) the healthy cells have a low mutation rate and, therefore, do not introduce many mutations at each cell division, ii) healthy cells usually do not have significant structural variations to improve the analysis, and iii) the sequencing technology introduces errors, and some of these errors are hard to distinguish from the mutations. With the experimental studies, we showed that our model is fast, robust, and accurately reconstructs lineage trees. Second, we focused on cancer cells. One research topic is identifying structural variations in the cancer cells' genomes and subsequently grouping the cells with similar genome profiles. This two-step process is vulnerable; the imperfections in the first step can irreversibly impact the analysis in the second step. To address this problem, we developed a variational inference-based model that simultaneously does copy number profiling and cell clustering. In addition, we extende, Undersökningen av organismers evolutionära historia, både på cellnivå och artnivå, är ett relevant forskningsämne inom beräkningsbiologi. Dessa studier leder till en djupare förståelse för utveckling, cancerprogression, arternas genetiska likhet med mera. Ett sätt att studera relationerna mellan enskilda celler eller arter är att undersöka skillnaderna i deras genom, inklusive enbaspolymorfier och kopienummervariationer. Det genetiska materialet behöver extraheras och sekvenseras för att användas i analyserna, men fel kan uppstå under databeredningen. Utvecklingen av sofistikerade, probabilistiska modeller är av yttersta vikt vid hantering av tekniska artefakter och inkludering av osäkerhet i analysen. I denna sammanställningsavhandling studerade vi olika frågeställningar och presenterade fyra artiklar för att ta itu med olika utmaningar. Först fokuserade vi på enstaka celler från frisk vävnad och utvecklade en probabilistisk modell för att rekonstruera cellhärkomstträdet. Denna uppgift är utmanande ur flera aspekter; i) de friska cellerna har en låg mutationshastighet och introducerar därför inte många mutationer vid varje celldelning, ii) friska celler har vanligtvis inte signifikanta strukturella variationer för att förbättra analysen; och iii) sekvenseringsteknologin introducerar fel, och några av dessa fel är svåra att skilja från mutationerna. Med den experimentella studien visade vi att vår modell är snabb, robust och exakt rekonstruerar härstamningsträd. För det andra fokuserade vi på cancerceller. Ett forskningsämne är att identifiera strukturella variationer i cancercellernas genom och därefter gruppera cellerna med liknande genomprofiler. Denna tvåstegsprocess är fragil; ofullkomligheterna i det första steget kan oåterkalleligt påverka analysen i det andra steget. För att lösa detta problem utvecklade vi en variationsbaserad modell som simultant utför kopienummerprofilering och cellklustring. Dessutom utökade vi modellen för att inkorporera enskilda enbas, Organizmaların evrimsel tarihinin hem hücresel hem de tür düzeyinde incelenmesi hesaplamalı biyolojide alâkalı bir araştırma konusudur. Bu konudaki araştırmalar, gelişim tarihi, kanser ilerlemesi, türlerin genetik benzerliği ve daha fazlası hakkında daha derin bir anlayışa rehberlik eder. Tek hücreler veya türler arasındaki ilişkileri incelemenin bir yolu, tek nükleotit ve kopya sayısı varyasyonları dahil olmak üzere genomlarındaki farklılıkları incelemektir. Analizlerde kullanılmak üzere genetik materyallerin çıkarılması ve dizilenmesi gerekir, ancak bu verilerin hazırlanması hatalara eğimlidir. Sofistike, olasılıksal modellerin geliştirilmesi, teknolojik hataların ele alınmasında ve belirsizliğin analize dahil edilmesinde son derece önemlidir. Bu derleme tezinde, çeşitli soruları inceledik ve farklı zorlukları ele almak için dört makale sunduk. İlk olarak, sağlıklı dokudaki tek hücrelere odaklandık ve hücre soy ağacını yeniden yapılandırmak için olasılıksal bir model geliştirdik. Bu görev birkaç açıdan zorlayıcıdır; i) sağlıklı hücreler düşük bir mutasyon oranına sahiptir, bu nedenle her hücre bölünmesinde pek çok mutasyon ortaya çıkarmazlar, ii) sağlıklı hücreler genellikle analizi geliştirmek için kayda değer yapısal varyasyonlara sahip değildirler; ve iii) dizileme teknolojisi hatalar ortaya çıkarır ve bu hataların bazılarını mutasyonlardan ayırt etmek zordur. Deneysel çalışmalar ile modelimizin hızlı ve gürbüz olduğunu, ve soy ağaçlarını doğru bir şekilde yeniden yapılandırdığını gösterdik. İkinci olarak, kanser hücrelerine odaklandık. Kanser hücrelerinin genomlarındaki yapısal varyasyonları belirlemek ve ardından benzer genom profillerine sahip hücreleri gruplandırmaktır bir araştırma konusudur. Bu iki adımlı sürecin hatalara zafiyeti vardır; ilk adımdaki kusurlar, ikinci adımdaki analizi geri döndürülemez şekilde etkileyebilir. Bu sorunu çözmek için, aynı anda kopya numarası profili ve hücre kümelemesi yapan varyasyonel çıkarıma dayalı bir model geliştirdik., QC 20230125

The investigation of the evolutionary history of organisms, both at the cellular level and at the species level, is a relevant research topic in computational biology. These investigations lead to a deeper understanding of developmental history, cancer progression, the genetic similarity of species, and more. One way to study the relations between single cells or species is to examine the differences in their genomes, including single nucleotide and copy number variations. The genetic materials need to be extracted and sequenced to be used in the analyses, but this data preparation is prone to errors. The development of sophisticated, probabilistic models is of the utmost importance in handling technological artifacts and including uncertainty in the analysis. In this compilation thesis, we studied various questions and presented four papers to address different challenges. First, we focused on single cells from healthy tissue and developed a probabilistic model to reconstruct the cell lineage tree. This task is challenging in several aspects; i) the healthy cells have a low mutation rate and, therefore, do not introduce many mutations at each cell division, ii) healthy cells usually do not have significant structural variations to improve the analysis, and iii) the sequencing technology introduces errors, and some of these errors are hard to distinguish from the mutations. With the experimental studies, we showed that our model is fast, robust, and accurately reconstructs lineage trees. Second, we focused on cancer cells. One research topic is identifying structural variations in the cancer cells' genomes and subsequently grouping the cells with similar genome profiles. This two-step process is vulnerable; the imperfections in the first step can irreversibly impact the analysis in the second step. To address this problem, we developed a variational inference-based model that simultaneously does copy number profiling and cell clustering. In addition, we extende, Undersökningen av organismers evolutionära historia, både på cellnivå och artnivå, är ett relevant forskningsämne inom beräkningsbiologi. Dessa studier leder till en djupare förståelse för utveckling, cancerprogression, arternas genetiska likhet med mera. Ett sätt att studera relationerna mellan enskilda celler eller arter är att undersöka skillnaderna i deras genom, inklusive enbaspolymorfier och kopienummervariationer. Det genetiska materialet behöver extraheras och sekvenseras för att användas i analyserna, men fel kan uppstå under databeredningen. Utvecklingen av sofistikerade, probabilistiska modeller är av yttersta vikt vid hantering av tekniska artefakter och inkludering av osäkerhet i analysen. I denna sammanställningsavhandling studerade vi olika frågeställningar och presenterade fyra artiklar för att ta itu med olika utmaningar. Först fokuserade vi på enstaka celler från frisk vävnad och utvecklade en probabilistisk modell för att rekonstruera cellhärkomstträdet. Denna uppgift är utmanande ur flera aspekter; i) de friska cellerna har en låg mutationshastighet och introducerar därför inte många mutationer vid varje celldelning, ii) friska celler har vanligtvis inte signifikanta strukturella variationer för att förbättra analysen; och iii) sekvenseringsteknologin introducerar fel, och några av dessa fel är svåra att skilja från mutationerna. Med den experimentella studien visade vi att vår modell är snabb, robust och exakt rekonstruerar härstamningsträd. För det andra fokuserade vi på cancerceller. Ett forskningsämne är att identifiera strukturella variationer i cancercellernas genom och därefter gruppera cellerna med liknande genomprofiler. Denna tvåstegsprocess är fragil; ofullkomligheterna i det första steget kan oåterkalleligt påverka analysen i det andra steget. För att lösa detta problem utvecklade vi en variationsbaserad modell som simultant utför kopienummerprofilering och cellklustring. Dessutom utökade vi modellen för att inkorporera enskilda enbas, Organizmaların evrimsel tarihinin hem hücresel hem de tür düzeyinde incelenmesi hesaplamalı biyolojide alâkalı bir araştırma konusudur. Bu konudaki araştırmalar, gelişim tarihi, kanser ilerlemesi, türlerin genetik benzerliği ve daha fazlası hakkında daha derin bir anlayışa rehberlik eder. Tek hücreler veya türler arasındaki ilişkileri incelemenin bir yolu, tek nükleotit ve kopya sayısı varyasyonları dahil olmak üzere genomlarındaki farklılıkları incelemektir. Analizlerde kullanılmak üzere genetik materyallerin çıkarılması ve dizilenmesi gerekir, ancak bu verilerin hazırlanması hatalara eğimlidir. Sofistike, olasılıksal modellerin geliştirilmesi, teknolojik hataların ele alınmasında ve belirsizliğin analize dahil edilmesinde son derece önemlidir. Bu derleme tezinde, çeşitli soruları inceledik ve farklı zorlukları ele almak için dört makale sunduk. İlk olarak, sağlıklı dokudaki tek hücrelere odaklandık ve hücre soy ağacını yeniden yapılandırmak için olasılıksal bir model geliştirdik. Bu görev birkaç açıdan zorlayıcıdır; i) sağlıklı hücreler düşük bir mutasyon oranına sahiptir, bu nedenle her hücre bölünmesinde pek çok mutasyon ortaya çıkarmazlar, ii) sağlıklı hücreler genellikle analizi geliştirmek için kayda değer yapısal varyasyonlara sahip değildirler; ve iii) dizileme teknolojisi hatalar ortaya çıkarır ve bu hataların bazılarını mutasyonlardan ayırt etmek zordur. Deneysel çalışmalar ile modelimizin hızlı ve gürbüz olduğunu, ve soy ağaçlarını doğru bir şekilde yeniden yapılandırdığını gösterdik. İkinci olarak, kanser hücrelerine odaklandık. Kanser hücrelerinin genomlarındaki yapısal varyasyonları belirlemek ve ardından benzer genom profillerine sahip hücreleri gruplandırmaktır bir araştırma konusudur. Bu iki adımlı sürecin hatalara zafiyeti vardır; ilk adımdaki kusurlar, ikinci adımdaki analizi geri döndürülemez şekilde etkileyebilir. Bu sorunu çözmek için, aynı anda kopya numarası profili ve hücre kümelemesi yapan varyasyonel çıkarıma dayalı bir model geliştirdik., QC 20230125

The investigation of the evolutionary history of organisms, both at the cellular level and at the species level, is a relevant research topic in computational biology. These investigations lead to a deeper understanding of developmental history, cancer progression, the genetic similarity of species, and more. One way to study the relations between single cells or species is to examine the differences in their genomes, including single nucleotide and copy number variations. The genetic materials need to be extracted and sequenced to be used in the analyses, but this data preparation is prone to errors. The development of sophisticated, probabilistic models is of the utmost importance in handling technological artifacts and including uncertainty in the analysis. In this compilation thesis, we studied various questions and presented four papers to address different challenges. First, we focused on single cells from healthy tissue and developed a probabilistic model to reconstruct the cell lineage tree. This task is challenging in several aspects; i) the healthy cells have a low mutation rate and, therefore, do not introduce many mutations at each cell division, ii) healthy cells usually do not have significant structural variations to improve the analysis, and iii) the sequencing technology introduces errors, and some of these errors are hard to distinguish from the mutations. With the experimental studies, we showed that our model is fast, robust, and accurately reconstructs lineage trees. Second, we focused on cancer cells. One research topic is identifying structural variations in the cancer cells' genomes and subsequently grouping the cells with similar genome profiles. This two-step process is vulnerable; the imperfections in the first step can irreversibly impact the analysis in the second step. To address this problem, we developed a variational inference-based model that simultaneously does copy number profiling and cell clustering. In addition, we extende, Undersökningen av organismers evolutionära historia, både på cellnivå och artnivå, är ett relevant forskningsämne inom beräkningsbiologi. Dessa studier leder till en djupare förståelse för utveckling, cancerprogression, arternas genetiska likhet med mera. Ett sätt att studera relationerna mellan enskilda celler eller arter är att undersöka skillnaderna i deras genom, inklusive enbaspolymorfier och kopienummervariationer. Det genetiska materialet behöver extraheras och sekvenseras för att användas i analyserna, men fel kan uppstå under databeredningen. Utvecklingen av sofistikerade, probabilistiska modeller är av yttersta vikt vid hantering av tekniska artefakter och inkludering av osäkerhet i analysen. I denna sammanställningsavhandling studerade vi olika frågeställningar och presenterade fyra artiklar för att ta itu med olika utmaningar. Först fokuserade vi på enstaka celler från frisk vävnad och utvecklade en probabilistisk modell för att rekonstruera cellhärkomstträdet. Denna uppgift är utmanande ur flera aspekter; i) de friska cellerna har en låg mutationshastighet och introducerar därför inte många mutationer vid varje celldelning, ii) friska celler har vanligtvis inte signifikanta strukturella variationer för att förbättra analysen; och iii) sekvenseringsteknologin introducerar fel, och några av dessa fel är svåra att skilja från mutationerna. Med den experimentella studien visade vi att vår modell är snabb, robust och exakt rekonstruerar härstamningsträd. För det andra fokuserade vi på cancerceller. Ett forskningsämne är att identifiera strukturella variationer i cancercellernas genom och därefter gruppera cellerna med liknande genomprofiler. Denna tvåstegsprocess är fragil; ofullkomligheterna i det första steget kan oåterkalleligt påverka analysen i det andra steget. För att lösa detta problem utvecklade vi en variationsbaserad modell som simultant utför kopienummerprofilering och cellklustring. Dessutom utökade vi modellen för att inkorporera enskilda enbas, Organizmaların evrimsel tarihinin hem hücresel hem de tür düzeyinde incelenmesi hesaplamalı biyolojide alâkalı bir araştırma konusudur. Bu konudaki araştırmalar, gelişim tarihi, kanser ilerlemesi, türlerin genetik benzerliği ve daha fazlası hakkında daha derin bir anlayışa rehberlik eder. Tek hücreler veya türler arasındaki ilişkileri incelemenin bir yolu, tek nükleotit ve kopya sayısı varyasyonları dahil olmak üzere genomlarındaki farklılıkları incelemektir. Analizlerde kullanılmak üzere genetik materyallerin çıkarılması ve dizilenmesi gerekir, ancak bu verilerin hazırlanması hatalara eğimlidir. Sofistike, olasılıksal modellerin geliştirilmesi, teknolojik hataların ele alınmasında ve belirsizliğin analize dahil edilmesinde son derece önemlidir. Bu derleme tezinde, çeşitli soruları inceledik ve farklı zorlukları ele almak için dört makale sunduk. İlk olarak, sağlıklı dokudaki tek hücrelere odaklandık ve hücre soy ağacını yeniden yapılandırmak için olasılıksal bir model geliştirdik. Bu görev birkaç açıdan zorlayıcıdır; i) sağlıklı hücreler düşük bir mutasyon oranına sahiptir, bu nedenle her hücre bölünmesinde pek çok mutasyon ortaya çıkarmazlar, ii) sağlıklı hücreler genellikle analizi geliştirmek için kayda değer yapısal varyasyonlara sahip değildirler; ve iii) dizileme teknolojisi hatalar ortaya çıkarır ve bu hataların bazılarını mutasyonlardan ayırt etmek zordur. Deneysel çalışmalar ile modelimizin hızlı ve gürbüz olduğunu, ve soy ağaçlarını doğru bir şekilde yeniden yapılandırdığını gösterdik. İkinci olarak, kanser hücrelerine odaklandık. Kanser hücrelerinin genomlarındaki yapısal varyasyonları belirlemek ve ardından benzer genom profillerine sahip hücreleri gruplandırmaktır bir araştırma konusudur. Bu iki adımlı sürecin hatalara zafiyeti vardır; ilk adımdaki kusurlar, ikinci adımdaki analizi geri döndürülemez şekilde etkileyebilir. Bu sorunu çözmek için, aynı anda kopya numarası profili ve hücre kümelemesi yapan varyasyonel çıkarıma dayalı bir model geliştirdik., QC 20230125

Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculating the likelihood of our noisy datasets and use it to build our Markov Chain Monte Carlo (MCMC) inference algorithm, ToMExO. Together with a set of engineered MCMC moves, our fast likelihood calculations enable us to work with datasets with hundreds of genes and thousands of tumors, which cannot be dealt with using available cancer progression analysis methods. We demonstrate our method’s performance on several synthetic datasets covering various scenarios for cancer progression dynamics. Then, a comparison against two state-of-the-art methods on a moderate-size biological dataset shows the merits of our algorithm in identifying significant and valid patterns. Finally, we present our analyses of several large biological datasets, including colorectal cancer, glioblastoma, and pancreatic cancer. In all the analyses, we validate the results using a set of method-independent metrics testing the causality and significance of the relations identified by ToMExO or competing methods., QC 20230130

Visual relationship detection is fundamental for holistic image understanding. However, the localization and classification of (subject, predicate, object) triplets remain challenging tasks, due to the combinatorial explosion of possible relationships, their long-tailed distribution in natural images, and an expensive annotation process. This paper introduces a novel weakly-supervised method for visual relationship detection that relies on minimal image-level predicate labels. A graph neural network is trained to classify predicates in images from a graph representation of detected objects, implicitly encoding an inductive bias for pairwise relations. We then frame relationship detection as the explanation of such a predicate classifier, i.e. we obtain a complete relation by recovering the subject and object of a predicted predicate. We present results comparable to recent fully- and weakly-supervised methods on three diverse and challenging datasets: HICO-DET for human-object interaction, Visual Relationship Detection for generic object-to-object relations, and UnRel for unusual triplets; demonstrating robustness to non-comprehensive annotations and good few-shot generalization., QC 20200929

Visual relationship detection is fundamental for holistic image understanding. However, the localization and classification of (subject, predicate, object) triplets remain challenging tasks, due to the combinatorial explosion of possible relationships, their long-tailed distribution in natural images, and an expensive annotation process. This paper introduces a novel weakly-supervised method for visual relationship detection that relies on minimal image-level predicate labels. A graph neural network is trained to classify predicates in images from a graph representation of detected objects, implicitly encoding an inductive bias for pairwise relations. We then frame relationship detection as the explanation of such a predicate classifier, i.e. we obtain a complete relation by recovering the subject and object of a predicted predicate. We present results comparable to recent fully- and weakly-supervised methods on three diverse and challenging datasets: HICO-DET for human-object interaction, Visual Relationship Detection for generic object-to-object relations, and UnRel for unusual triplets; demonstrating robustness to non-comprehensive annotations and good few-shot generalization., QC 20200929

Visual relationship detection is fundamental for holistic image understanding. However, the localization and classification of (subject, predicate, object) triplets remain challenging tasks, due to the combinatorial explosion of possible relationships, their long-tailed distribution in natural images, and an expensive annotation process. This paper introduces a novel weakly-supervised method for visual relationship detection that relies on minimal image-level predicate labels. A graph neural network is trained to classify predicates in images from a graph representation of detected objects, implicitly encoding an inductive bias for pairwise relations. We then frame relationship detection as the explanation of such a predicate classifier, i.e. we obtain a complete relation by recovering the subject and object of a predicted predicate. We present results comparable to recent fully- and weakly-supervised methods on three diverse and challenging datasets: HICO-DET for human-object interaction, Visual Relationship Detection for generic object-to-object relations, and UnRel for unusual triplets; demonstrating robustness to non-comprehensive annotations and good few-shot generalization., QC 20200929

Visual relationship detection is fundamental for holistic image understanding. However, the localization and classification of (subject, predicate, object) triplets remain challenging tasks, due to the combinatorial explosion of possible relationships, their long-tailed distribution in natural images, and an expensive annotation process. This paper introduces a novel weakly-supervised method for visual relationship detection that relies on minimal image-level predicate labels. A graph neural network is trained to classify predicates in images from a graph representation of detected objects, implicitly encoding an inductive bias for pairwise relations. We then frame relationship detection as the explanation of such a predicate classifier, i.e. we obtain a complete relation by recovering the subject and object of a predicted predicate. We present results comparable to recent fully- and weakly-supervised methods on three diverse and challenging datasets: HICO-DET for human-object interaction, Visual Relationship Detection for generic object-to-object relations, and UnRel for unusual triplets; demonstrating robustness to non-comprehensive annotations and good few-shot generalization., QC 20200929

Visual relationship detection is fundamental for holistic image understanding. However, the localization and classification of (subject, predicate, object) triplets remain challenging tasks, due to the combinatorial explosion of possible relationships, their long-tailed distribution in natural images, and an expensive annotation process. This paper introduces a novel weakly-supervised method for visual relationship detection that relies on minimal image-level predicate labels. A graph neural network is trained to classify predicates in images from a graph representation of detected objects, implicitly encoding an inductive bias for pairwise relations. We then frame relationship detection as the explanation of such a predicate classifier, i.e. we obtain a complete relation by recovering the subject and object of a predicted predicate. We present results comparable to recent fully- and weakly-supervised methods on three diverse and challenging datasets: HICO-DET for human-object interaction, Visual Relationship Detection for generic object-to-object relations, and UnRel for unusual triplets; demonstrating robustness to non-comprehensive annotations and good few-shot generalization., QC 20200929

Vision transformers have demonstrated the potential to outperform CNNs in a variety of vision tasks. But the computational and memory requirements of these models prohibit their use in many applications, especially those that depend on high-resolution images, such as medical image classification. Efforts to train ViTs more efficiently are overly complicated, necessitating architectural changes or intricate training schemes. In this work, we show that standard ViT models can be efficiently trained at high resolution by randomly dropping input image patches. This simple approach, PatchDropout, reduces FLOPs and memory by at least 50% in standard natural image datasets such as IMAGENET, and those savings only increase with image size. On CSAW, a high-resolution medical dataset, we observe a 5. savings in computation and memory using PatchDropout, along with a boost in performance. For practitioners with a fixed computational or memory budget, PatchDropout makes it possible to choose image resolution, hyperparameters, or model size to get the most performance out of their model., QC 20230731

Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples. This allows the model to recognize nuclei in new and different experiments efficiently without requiring expert annotations, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments. It can also be used online, integrated into CellProfiler and freely downloaded at www.nucleaizer.org. A record of this paper's transparent peer review process is included in the Supplemental Information. Microscopy image analysis of single cells can be challenging but also eased and improved. We developed a deep learning method to segment cell nuclei. Our strategy is adapting to unexpected circumstances automatically by synthesizing artificial microscopy images in such a domain as training samples., QC 20201013

Phylogenetics is a classical methodology in computational biology that today has become highly relevant for medical investigation of single-cell data, e.g., in the context of cancer development. The exponential size of the tree space is, unfortunately, a substantial obstacle for Bayesian phylogenetic inference using Markov chain Monte Carlo based methods since these rely on local operations. And although more recent variational inference (VI) based methods offer speed improvements, they rely on expensive auto-differentiation operations for learning the variational parameters. We propose VaiPhy, a remarkably fast VI based algorithm for approximate posterior inference in an augmented tree space. VaiPhy produces marginal log-likelihood estimates on par with the state-of-the-art methods on real data and is considerably faster since it does not require auto-differentiation. Instead, VaiPhy combines coordinate ascent update equations with two novel sampling schemes: (i) SLANTIS, a proposal distribution for tree topologies in the augmented tree space, and (ii) the JC sampler, to the best of our knowledge, the first-ever scheme for sampling branch lengths directly from the popular Jukes-Cantor model. We compare VaiPhy in terms of density estimation and runtime. Additionally, we evaluate the reproducibility of the baselines. We provide our code on GitHub: https://github.com/Lagergren-Lab/VaiPhy., Part of ISBN 9781713871088QC 20230712

Diagnostic Captioning is described as the automatic text generation from a collection of X-RAY images and it can assist inexperienced doctors and radiologists to reduce clinical errors or help experienced professionals to increase their productivity. Therefore, tools that would help doctors and radiologists produce higher quality reports in less time could be of high interest for medical imaging departments, as well as significantly impact deep learning research within the biomedical domain. With our participation in ImageCLEFmedical 2022 Caption evaluation campaign, we have attempted to address both concept detection and caption prediction tasks by developing baselines based on Deep Neural Networks; including image encoders, classifiers and text generators. Our group, NeuralDynamicsLab at KTH Royal Institute of Technology, within the school of Electrical Engineering and Computer Science, ranked 4th in the former and 5th in the latter task., QC 20230614

Transfer learning is a standard technique to transfer knowledge from one domain to another. For applications in medical imaging, transfer from ImageNet has become the de-facto approach, despite differences in the tasks and image characteristics between the domains. However, it is unclear what factors determine whether - and to what extent transfer learning to the medical domain is useful. The longstanding assumption that features from the source domain get reused has recently been called into question. Through a series of experiments on several medical image benchmark datasets, we explore the relationship between transfer learning, data size, the capacity and inductive bias of the model, as well as the distance between the source and target domain. Our findings suggest that transfer learning is beneficial in most cases, and we characterize the important role feature reuse plays in its success., Part of proceedings ISBN 978-1-6654-6946-3QC 20230131

In variational inference (VI), the marginal log-likelihood is estimated using the standard evidence lower bound (ELBO), or improved versions as the importance weighted ELBO (IWELBO). We propose the multiple importance sampling ELBO (MISELBO), a versatile yet simple framework. MISELBO is applicable in both amortized and classical VI, and it uses ensembles, e.g., deep ensembles, of independently inferred variational approximations. As far as we are aware, the concept of deep ensembles in amortized VI has not previously been established. We prove that MISELBO provides a tighter bound than the average of standard ELBOs, and demonstrate empirically that it gives tighter bounds than the average of IWELBOs. MISELBO is evaluated in density-estimation experiments that include MNIST and several real-data phylogenetic tree inference problems. First, on the MNIST dataset, MISELBO boosts the density-estimation performances of a state-of-the-art model, nouveau VAE. Second, in the phylogenetic tree inference setting, our framework enhances a state-of-the-art VI algorithm that uses normalizing flows. On top of the technical benefits of MISELBO, it allows to unveil connections between VI and recent advances in the importance sampling literature, paving the way for further methodological advances., QC 20221104

Multiscale modeling by means of co-simulation is a powerful tool to address many vital questions in neuroscience. It can for example be applied in the study of the process of learning and memory formation in the brain. At the same time the co-simulation technique makes it possible to take advantage of interoperability between existing tools and multi-physics models as well as distributed computing. However, the theoretical basis for multiscale modeling is not sufficiently understood. There is, for example, a need of efficient and accurate numerical methods for time integration. When time constants of model components are different by several orders of magnitude, individual dynamics and mathematical definitions of each component all together impose stability, accuracy and efficiency challenges for the time integrator. Following our numerical investigations in Brocke et al. (Frontiers in Computational Neuroscience, 10, 97, 2016), we present a new multirate algorithm that allows us to handle each component of a large system with a step size appropriate to its time scale. We take care of error estimates in a recursive manner allowing individual components to follow their discretization time course while keeping numerical error within acceptable bounds. The method is developed with an ultimate goal of minimizing the communication between the components. Thus it is especially suitable for co-simulations. Our preliminary results support our confidence that the multirate approach can be used in the class of problems we are interested in. We show that the dynamics ofa communication signal as well as an appropriate choice of the discretization order between system components may have a significant impact on the accuracy of the coupled simulation. Although, the ideas presented in the paper have only been tested on a single model, it is likely that they can be applied to other problems without loss of generality. We believe that this work may significantly contribute to the est, QC 20170609

Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screening-time detection is commonly caused by the tumor being obscured by its surrounding breast tissues, a phenomenon called masking. To study and benchmark mammographic masking of cancer, in this work we introduce CSAW-M, the largest public mammographic dataset, collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We also trained deep learning models on CSAW-M to estimate the masking level and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers – without being explicitly trained for these tasks – than its breast density counterparts., QC 20231218

Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screening-time detection is commonly caused by the tumor being obscured by its surrounding breast tissues, a phenomenon called masking. To study and benchmark mammographic masking of cancer, in this work we introduce CSAW-M, the largest public mammographic dataset, collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We also trained deep learning models on CSAW-M to estimate the masking level and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers – without being explicitly trained for these tasks – than its breast density counterparts., QC 20231218

Mixtures of Hidden Markov Models (MHMM) are widely used for clustering of sequential data, by letting each cluster correspond to a Hidden Markov Model (HMM). Expectation Maximization (EM) is the standard approach for learning the parameters of an MHMM. However, due to the non-convexity of the objective function, EM can converge to poor local optima. To tackle this problem, we propose a novel method, the Orthogonal Mixture of Hidden Markov Models (oMHMM), which aims to direct the search away from candidate solutions that include very similar HMMs, since those do not fully exploit the power of the mixture model. The directed search is achieved by including a penalty in the objective function that favors higher orthogonality between the transition matrices of the HMMs. Experimental results on both simulated and real-world datasets show that the oMHMM consistently finds equally good or better local optima than the standard EM for an MHMM; for some datasets, the clustering performance is significantly improved by our novel oMHMM (up to 55 percentage points w.r.t. the v-measure). Moreover, the oMHMM may also decrease the computational cost substantially, reducing the number of iterations down to a fifth of those required by MHMM using standard EM., QC 20211203Conference ISBN 978-3-030-67658-2; 978-3-030-67657-5

Background: Lateral gene transfer (LGT) is an evolutionary process that has an important role in biology. It challenges the traditional binary tree-like evolution of species and is attracting increasing attention of the molecular biologists due to its involvement in antibiotic resistance. A number of attempts have been made to model LGT in the presence of gene duplication and loss, but reliably placing LGT events in the species tree has remained a challenge. Results: In this paper, we propose probabilistic methods that samples reconciliations of the gene tree with a dated species tree and computes maximum a posteriori probabilities. The MCMC-based method uses the probabilistic model DLTRS, that integrates LGT, gene duplication, gene loss, and sequence evolution under a relaxed molecular clock for substitution rates. We can estimate posterior distributions on gene trees and, in contrast to previous work, the actual placement of potential LGT, which can be used to, e.g., identify "highways" of LGT. Conclusions: Based on a simulation study, we conclude that the method is able to infer the true LGT events on gene tree and reconcile it to the correct edges on the species tree in most cases. Applied to two biological datasets, containing gene families from Cyanobacteria and Molicutes, we find potential LGTs highways that corroborate other studies as well as previously undetected examples., QC 20170303

The ability to accurately estimate risk of developing breast cancer would be invaluable for clinical decision-making. One promising new approach is to integrate image-based risk models based on deep neural networks. However, one must take care when using such models, as selection of training data influences the patterns the network will learn to identify. With this in mind, we trained networks using three different criteria to select the positive training data (i.e. images from patients that will develop cancer): an inherent risk model trained on images with no visible signs of cancer, a cancer signs model trained on images containing cancer or early signs of cancer, and a conflated model trained on all images from patients with a cancer diagnosis. We find that these three models learn distinctive features that focus on different patterns, which translates to contrasts in performance. Short-term risk is best estimated by the cancer signs model, whilst long-term risk is best estimated by the inherent risk model. Carelessly training with all images conflates inherent risk with early cancer signs, and yields sub-optimal estimates in both regimes. As a consequence, conflated models may lead physicians to recommend preventative action when early cancer signs are already visible., QC 20210323

In this preregistration submission, we propose an empirical study of how networks handle changes in complexity of the data. We investigate the effect of network capacity on generalization performance in the face of increasing data complexity. For this, we measure the generalization error for an image classification task where the number of classes steadily increases. We compare a number of modern architectures at different scales in this setting. The methodology, setup, and hypotheses described in this proposal were evaluated by peer review before experiments were conducted., QC 20200625

We show that training a deep network using batch normalization is equivalent to approximate inference in Bayesian models. We further demonstrate that this finding allows us to make meaningful estimates of the model uncertainty using conventional architectures, without modifications to the network or the training procedure. Our approach is thoroughly validated by measuring the quality of uncertainty in a series of empirical experiments on different tasks. It outperforms baselines with strong statistical significance, and displays competitive performance with recent Bayesian approaches, Note difference in volume number in ISI recordQC 20230922

We show that training a deep network using batch normalization is equivalent to approximate inference in Bayesian models. We further demonstrate that this finding allows us to make meaningful estimates of the model uncertainty using conventional architectures, without modifications to the network or the training procedure. Our approach is thoroughly validated by measuring the quality of uncertainty in a series of empirical experiments on different tasks. It outperforms baselines with strong statistical significance, and displays competitive performance with recent Bayesian approaches, Note difference in volume number in ISI recordQC 20230922

We show that training a deep network using batch normalization is equivalent to approximate inference in Bayesian models. We further demonstrate that this finding allows us to make meaningful estimates of the model uncertainty using conventional architectures, without modifications to the network or the training procedure. Our approach is thoroughly validated by measuring the quality of uncertainty in a series of empirical experiments on different tasks. It outperforms baselines with strong statistical significance, and displays competitive performance with recent Bayesian approaches, Note difference in volume number in ISI recordQC 20230922

We show that training a deep network using batch normalization is equivalent to approximate inference in Bayesian models. We further demonstrate that this finding allows us to make meaningful estimates of the model uncertainty using conventional architectures, without modifications to the network or the training procedure. Our approach is thoroughly validated by measuring the quality of uncertainty in a series of empirical experiments on different tasks. It outperforms baselines with strong statistical significance, and displays competitive performance with recent Bayesian approaches, Note difference in volume number in ISI recordQC 20230922

In this paper we develop a new method for numerically approximating sensitivitiesin parameter-dependent ordinary differential equations (ODEs). Our approach,intended for situations where the standard forward and adjoint sensitivity analysisbecome too computationally costly for practical purposes, is based on the PeanoBaker series from control theory. We give a representation, using this series, for thesensitivity matrix S of an ODE system and use the representation to construct anumerical method for approximating S. We prove that, under standard regularityassumptions, the error of our method scales as O(∆t2max), where ∆tmax is the largesttime step used when numerically solving the ODE. We illustrate the performanceof the method in several numerical experiments, taken from both the systemsbiology setting and more classical dynamical systems. The experiments show thesought-after improvement in running time of our method compared to the forwardsensitivity approach. For example, in experiments involving a random linear system,the forward approach requires roughly √n longer computational time, where n isthe dimension of the parameter space, than our proposed method., QC 20230824

In this paper we develop a new method for numerically approximating sensitivitiesin parameter-dependent ordinary differential equations (ODEs). Our approach,intended for situations where the standard forward and adjoint sensitivity analysisbecome too computationally costly for practical purposes, is based on the PeanoBaker series from control theory. We give a representation, using this series, for thesensitivity matrix S of an ODE system and use the representation to construct anumerical method for approximating S. We prove that, under standard regularityassumptions, the error of our method scales as O(∆t2max), where ∆tmax is the largesttime step used when numerically solving the ODE. We illustrate the performanceof the method in several numerical experiments, taken from both the systemsbiology setting and more classical dynamical systems. The experiments show thesought-after improvement in running time of our method compared to the forwardsensitivity approach. For example, in experiments involving a random linear system,the forward approach requires roughly √n longer computational time, where n isthe dimension of the parameter space, than our proposed method., QC 20230824

In this paper we develop a new method for numerically approximating sensitivitiesin parameter-dependent ordinary differential equations (ODEs). Our approach,intended for situations where the standard forward and adjoint sensitivity analysisbecome too computationally costly for practical purposes, is based on the PeanoBaker series from control theory. We give a representation, using this series, for thesensitivity matrix S of an ODE system and use the representation to construct anumerical method for approximating S. We prove that, under standard regularityassumptions, the error of our method scales as O(∆t2max), where ∆tmax is the largesttime step used when numerically solving the ODE. We illustrate the performanceof the method in several numerical experiments, taken from both the systemsbiology setting and more classical dynamical systems. The experiments show thesought-after improvement in running time of our method compared to the forwardsensitivity approach. For example, in experiments involving a random linear system,the forward approach requires roughly √n longer computational time, where n isthe dimension of the parameter space, than our proposed method., QC 20230824

In this paper we develop a new method for numerically approximating sensitivitiesin parameter-dependent ordinary differential equations (ODEs). Our approach,intended for situations where the standard forward and adjoint sensitivity analysisbecome too computationally costly for practical purposes, is based on the PeanoBaker series from control theory. We give a representation, using this series, for thesensitivity matrix S of an ODE system and use the representation to construct anumerical method for approximating S. We prove that, under standard regularityassumptions, the error of our method scales as O(∆t2max), where ∆tmax is the largesttime step used when numerically solving the ODE. We illustrate the performanceof the method in several numerical experiments, taken from both the systemsbiology setting and more classical dynamical systems. The experiments show thesought-after improvement in running time of our method compared to the forwardsensitivity approach. For example, in experiments involving a random linear system,the forward approach requires roughly √n longer computational time, where n isthe dimension of the parameter space, than our proposed method., QC 20230824

Cancer is an evolutionary process involving the accumulation of somatic mutations in the genome. The tumor’s evolution is known to be highly influenced by specific somatic mutations in so-called cancer driver genes. Cancer progression models are computational tools used to infer the interactions among cancer driver genes by analyzing the pattern of absence/presence of mutations in different tumors of a cohort. In an abundance of subclonal mutations, discarding the heterogeneity of tumors and investigating the interrelations among the driver genes solely based on tumor-level data can result in misleading interpretations. In this paper, we introduce a computational approach to infer cancer progression models from the clone-level data gathered from a cohort of tumors. Our method leverages the rich clone-level data to identify the patterns of interactions among cancer driver genes and produce significantly more robust and reliable cancer progression models. Using a novel efficient Markov Chain Monte Carlo inference algorithm, our method provides outstanding scalability to the rapidly increasing size of available datasets. Using an extensive set of synthetic data experiments, we demonstrate the performance of our inference method in recovering the generative progression models. Finally, we present our analysis of two sub-types of lung cancer using biological multi-regional bulk data., QC 20230131

Cancer is an evolutionary process involving the accumulation of somatic mutations in the genome. The tumor’s evolution is known to be highly influenced by specific somatic mutations in so-called cancer driver genes. Cancer progression models are computational tools used to infer the interactions among cancer driver genes by analyzing the pattern of absence/presence of mutations in different tumors of a cohort. In an abundance of subclonal mutations, discarding the heterogeneity of tumors and investigating the interrelations among the driver genes solely based on tumor-level data can result in misleading interpretations. In this paper, we introduce a computational approach to infer cancer progression models from the clone-level data gathered from a cohort of tumors. Our method leverages the rich clone-level data to identify the patterns of interactions among cancer driver genes and produce significantly more robust and reliable cancer progression models. Using a novel efficient Markov Chain Monte Carlo inference algorithm, our method provides outstanding scalability to the rapidly increasing size of available datasets. Using an extensive set of synthetic data experiments, we demonstrate the performance of our inference method in recovering the generative progression models. Finally, we present our analysis of two sub-types of lung cancer using biological multi-regional bulk data., QC 20230131

Cancer is an evolutionary process involving the accumulation of somatic mutations in the genome. The tumor’s evolution is known to be highly influenced by specific somatic mutations in so-called cancer driver genes. Cancer progression models are computational tools used to infer the interactions among cancer driver genes by analyzing the pattern of absence/presence of mutations in different tumors of a cohort. In an abundance of subclonal mutations, discarding the heterogeneity of tumors and investigating the interrelations among the driver genes solely based on tumor-level data can result in misleading interpretations. In this paper, we introduce a computational approach to infer cancer progression models from the clone-level data gathered from a cohort of tumors. Our method leverages the rich clone-level data to identify the patterns of interactions among cancer driver genes and produce significantly more robust and reliable cancer progression models. Using a novel efficient Markov Chain Monte Carlo inference algorithm, our method provides outstanding scalability to the rapidly increasing size of available datasets. Using an extensive set of synthetic data experiments, we demonstrate the performance of our inference method in recovering the generative progression models. Finally, we present our analysis of two sub-types of lung cancer using biological multi-regional bulk data., QC 20230131

Cancer is an evolutionary process involving the accumulation of somatic mutations in the genome. The tumor’s evolution is known to be highly influenced by specific somatic mutations in so-called cancer driver genes. Cancer progression models are computational tools used to infer the interactions among cancer driver genes by analyzing the pattern of absence/presence of mutations in different tumors of a cohort. In an abundance of subclonal mutations, discarding the heterogeneity of tumors and investigating the interrelations among the driver genes solely based on tumor-level data can result in misleading interpretations. In this paper, we introduce a computational approach to infer cancer progression models from the clone-level data gathered from a cohort of tumors. Our method leverages the rich clone-level data to identify the patterns of interactions among cancer driver genes and produce significantly more robust and reliable cancer progression models. Using a novel efficient Markov Chain Monte Carlo inference algorithm, our method provides outstanding scalability to the rapidly increasing size of available datasets. Using an extensive set of synthetic data experiments, we demonstrate the performance of our inference method in recovering the generative progression models. Finally, we present our analysis of two sub-types of lung cancer using biological multi-regional bulk data., QC 20230131

Cancer is an evolutionary process involving the accumulation of somatic mutations in the genome. The tumor’s evolution is known to be highly influenced by specific somatic mutations in so-called cancer driver genes. Cancer progression models are computational tools used to infer the interactions among cancer driver genes by analyzing the pattern of absence/presence of mutations in different tumors of a cohort. In an abundance of subclonal mutations, discarding the heterogeneity of tumors and investigating the interrelations among the driver genes solely based on tumor-level data can result in misleading interpretations. In this paper, we introduce a computational approach to infer cancer progression models from the clone-level data gathered from a cohort of tumors. Our method leverages the rich clone-level data to identify the patterns of interactions among cancer driver genes and produce significantly more robust and reliable cancer progression models. Using a novel efficient Markov Chain Monte Carlo inference algorithm, our method provides outstanding scalability to the rapidly increasing size of available datasets. Using an extensive set of synthetic data experiments, we demonstrate the performance of our inference method in recovering the generative progression models. Finally, we present our analysis of two sub-types of lung cancer using biological multi-regional bulk data., QC 20230131