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Gyros Protein Technologies AB produce instruments designed to perform automated immunoassaying on plastic CDs with microstructures. While generally being a very robust process, the company had noticed that some runs on the instruments encountered problems. They hypothesised it had to do with the chamber on the CD in which the sample is added to. It was believed that the chamber was not being filled properly, leaving it completely empty or contained with a small amount of air, rather than liquid. This project aimed to investigate this hypothesis and to develop an image analysis solution that could reliably detect these occurrences. An image analysis script was developed which mainly utilised template matching and canny edge detection to assess the presence of air. The analysis had great success in detecting empty chambers and large bubbles of air, while it had some trouble with discerning small bubbles from dirt on top of the CD. Evaluating the analysis on a test set of 1305 images annotated by two people, the analysis managed to score an accuracy of 96.8 % and 99.5 % respectively.

Here we present a similarity-based pairing method for generating compound pairs to train a Siamese Neural Network. In comparison with the conventional exhaustive pairing of N2/2 pairs (N being the sizeof the training set), this method results in N-1 pairs, significantly reducing the training time. It exhibits a better prediction performance consistently on the three physicochemical property datasets, using a multilayer perceptron with the ECFP4 fingerprint. We further include into the Siamese Neural Network the pre-trained Chemformer which extracts task-specific chemical features from the input SMILES strings. With the n-shot learning, we propose a means to measure the prediction uncertainty. Our results demonstrate that the higher accuracy is indeed associated with the lower prediction uncertainty. In addition, we discuss implications of the similarity principle in machine learning.

The study explores the integration of mathematical modelling and machine learning to understand tumour-macrophage interactions in the tumour microenvironment. It details mathematical models based on biochemistry and physics for predicting tumour dynamics, highlighting the role of macrophages. Machine learning, particularly unsupervised and supervised techniques like K-means clustering, logistic regression, and support vector machines, are implemented to analyse simulation data. The thesis's integration of K-means clustering reveals distinct tumour behaviour patterns through the classification of tumour cells based on their microenvironmental interactions. This segmentation is crucial for understanding tumour heterogeneity and its implications for treatment. Additionally, the application of logistic regression provides insights into the probability of macrophage polarization states in the tumour microenvironment. This statistical model underscores the significant factors influencing macrophage behaviour and their consequent impact on tumour progression. These analytical approaches enhance the understanding of the complex dynamics within the tumour microenvironment, contributing to more effective tumour study strategies. The study presents a comprehensive analysis of tumour growth, macrophage polarization, and their impact on cancer treatment and prognosis. Ethical considerations and future directions focus on enhancing model accuracy and integrating experimental data for improved cancer diagnosis and treatment strategies. The thesis concludes with the potential of this hybrid approach in advancing cancer biology and therapeutic approaches. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p><p>There are other digital material (eg film, image or audio files) or models/artifacts that belongs to the thesis and need to be archived.</p>

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Machine learning has gained much attention and extended to the field of drug discovery. However, due to the uncertainties of the dataset, predictions should be quantitatively analyzed. Conformal prediction is a powerful method for quantifying these uncertainties, generating a predefined confidence level and a corresponding interval within which the true target is anticipated to fall. This paper aims to explore the effects of different chemical representations of SMILES structures for training (chemical descriptors, Morgan fingerprints), machine learning algorithms (k-nearest neighbor, support vector machine, random forest, extreme gradient boosting, and artificial neural network), and different normalization methods (k-nearest neighbor, Mondrian regression) in influencing the conformal prediction results. We find that Morgan fingerprint outperforms chemical descriptors, Mondrian regression outperforms knearest neighbor for one or several values of coverage, and the mean, median, and standard deviation of the output interval. None of the investigated machine learning methods extremely outperforms the other methods. Conformal predictive system, an alternative form of conformal prediction was also investigated to explore its usefulness in drug discovery.

It is well known that genetic variation and ability to adapt is crucial for the survival of anypopulation. Whether it be about a natural population’s ability to respond to changes in itsenvironment, or a population of livestock’s ability to produce more milk, genetic variation is akey element. Effectively small populations have an increased risk of extinction caused byreduced ability to adapt or respond to selection. Small populations are also more affected bygenetic drift, which can cause deleterious mutations to fixate, reducing the populations’ fitnesspossibly to the point where it is unable to survive. Models describing changes in allelefrequencies in a population under selection can be used to study a population’s response toselection. A limitation to such models is they often assume infinite population size and neglectthe effects of genetic drift, making them unable to implement when working with effectivelysmall populations.Here, an individual-based model of a quantitative trait affected by selection, mutation andgenetic drift is used to study the adaptive potential of effectively small populations. In a series ofsimulations, changes in the trait are explored under directional selection and stabilizingselection with adaptation to one, and several repeated shifts in optimum. Results of simulationinclude that populations under strong directional selection, such as breeding, potentially risklosing all adaptive potential. Results also suggest that effects of strong directional selectionmight be irreversible, even if the strong selective pressure is removed.

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Listeria monocytogenes and Campylobacter spp. are bacteria that sometimes can cause severe illness in humans. Both can be found as contaminants in food that has been produced, stored or prepared improperly, which is why it is important to ensure that the handling of food is done correctly. The National Food Agency (Livsmedelsverket) is the Swedish authority responsible for food safety. One important task is to, in collaboration with other authorities, track and prevent food-related disease outbreaks. For this purpose bacterial samples are regularly collected from border control, at food production facilities and retail as well as from suspected food items and drinking water during outbreaks, and epidemiological analyses are employed to determine the type of bacteria present and whether they can be linked to a common source. One part of these epidemiological analyses involve bioinformatic analyses of the bacterial DNA. This includes determination of sequence type and serotype, as well as calculations of similarities between samples. Such analyses require data processing in several different steps which are usually performed by a bioinformatician using different computer programs. Currently the National Food Agency outsources most of these analyses to other authorities and companies, and the purpose of this project was to develop a pipeline that would allow for these analyses to be performed in-house. The result was a pipeline named BacIL - Bacterial Identification and Linkage which has been developed to automatically perform sequence typing, serotyping and SNP-analysis of Listeria monocytogenes as well as sequence typing and SNP-analysis of Campylobacter jejuni, C. coli and C. lari. The result of the SNP-analysisis is used to create clusters which can be used to identify related samples. The pipeline decreases the number of programs that have to be manually started from more than ten to two.