Cancer represents a huge challenge in the medical world, necessitating early detection methods to improve treatment outcomes. The field of fragmentomics emerged as a promising option towards developing efficient non-invasive cancer diagnosis tools. By analysing the differences between the cfDNA fragments from blood samples of healthy patients and patients with cancer, this study aims to determine the most important fragmentomics features for cancer detection. The methods present in this work involve extracting features from the cfDNA fragments available in the experimental dataset, applying a pipeline of feature selection techniques that removes the redundant features, training and evaluating a logistic regression and random forest classifiers to differentiate between healthy and diseased samples, and finally extracting the feature weights from the trained models to understand which features contributed the most to the classification task. Filter-based variance thresholding and Correlation-based Feature Selection (CFS) were employed to refine the dataset. Independent t-test and the Mann-Whitney U test are used to calculate the relationship between the cancer and healthy samples. The Pearson correlation coefficient calculates the correlation between each pair of features. The classification performance of the two proposed models is assessed using the train/test split and the nested cross-validation techniques. The evaluation reveals that logistic regression constantly outperforms the random forest and that removing the redundant features increases the performance of both classifiers. Certain genomic bins, mostly on chromosomes 1, 7 and 8, contain significant features for the classification task. These findings suggest that understanding the importance of the fragmentomics features can lead to improved diagnostic tools such as cancer detection based on blood tests.

Detecting cancer at an initial stage could change the course of the disease's development. A non-invasive examination consists of the liquid biopsy of blood, revealing biomarkers that could provide information about the existence of a tumour or not in the organism. The research touches upon the relevance of DNA fragments, precisely the length of fragments, in the detection of cancer. An in-depth interpretation of the fragment length distribution for predicting the state of a patient as being healthy or sick with cancer was approached. The distribution was explored from four perspectives: the complete fragment length distribution, the size range from 90 to 150 bp, important lengths selected by the feature extraction methods and the Fourier Transform of the initial data. These were input in three machine learning models. Using the fragment lengths between 93 and 98 produced accuracy and AUC scores of over 0.85 for all supervised classification models. Processing the data with the Fourier Transform and using the amplitude of spectrums as features in the Random Forest model resulted in an AUC of 0.99.

Recent research has indicated attributes of cell-free DNA (cfDNA) called fragmentomics
as a promising method for late stage cancer detection in a non-invasive manner. The pri-
mary objective of this research is to uncover hidden patterns and interactions that could
enhance the accuracy and sensitivity of blood-based cancer diagnostics. This study explores
the complementarity between three fragmentomics features; fragment length distribution,
and nucleotide fragment end sequence diversity and nucleosome positioning for four dif-
ferent sample groups; breast cancer, colorectal cancer, lung cancer and healthy controls.
Various machine learning techniques such as linear regression were employed to quantify
any complementary relationships between the features

In recent years, a new way of cancer diagnostics has emerged, the analysis of DNA fragments circulating in the blood of cancer patients known as fragmentomics. This DNA, known as cell-free DNA (cfDNA), is an easily available biomarker for cell types. Deducing the tissue origin of cfDNA can reveal anomalies in cell death caused by diseases. That holds great potential in cancer detection and monitoring. The process of establishing the cell composition of a blood sample is called cell deconvolution. This research paper focuses on the comparison of two methods of cell deconvolution. The first one UXM, solves this problem by employing a reference-based technique using a methylation atlas. The second one reference-free cfSort, utilizes a Deep Learning Neural Network to perform the sample analysis. In the paper, however, a simpler architecture was trained due to the difficulties in reproduction. Experiments have been conducted to assess the sensitivity of both methods. Experiments consisted of 5 major cell types together mixed with white blood cell DNA fragments to assess the sensitivity of each method. Furthermore, different metrics such as Pearson's correlation coefficient have been used to determine the accuracy of both methods. In the end, UXM outperformed cfSort in most metrics, including Pearson's correlation coefficient, indicating its superior accuracy in deconvolution tasks. However, cfSort showed potential for higher prediction accuracy with further development and better documentation. The findings highlight the strengths and limitations of both methods. This study suggests that while UXM is currently more reliable, future improvements in cfSort could make it a viable alternative. Continued research is recommended to enhance the accuracy and transparency of these methods, ensuring their effectiveness in real-world healthcare applications.

Cancer is one of the leading causes of death. To reduce the amount of deaths caused by cancer, a number of different screening methods are used to detect cancer in an earlier stage, to improve sur vival rates when treating patients with cancer. Cur rent screening methods are often invasive, costly and not very accurate. Therefore, new methods are being sought that aim to be cheaper, less in vasive and provide more accurate results. One of these methods is fragmentomics. Multiple methods have been proposed to use fragmentomics analy sis in the context of screening for cancer, includ ing using the short/long ratio as well as investigat ing the nucleotides at the ends of the fragments. Across previous works using fragmentomics anal ysis to predict cancer, different pre-proccessing steps are used, with limited explanation why the pre-processing methods were chosen. Research into the effects of pre-processing steps used when using fragmentomics analysis is lacking. Two main pre-processing steps in the field are correct ing GC-bias and filtering on MAPQ. Here we in vestigated the impact of three GC-correction meth ods by applying the correction method and then analyzing the resulting fragmentation profiles us ing short/long fragment ratios. Furthermore, three different MAPQ filtering thresholds were studied. This showed that Deeptools correction of the GC bias lowered performance, with the accuracy drop ping from 77.8% to 69.4%. Applying LOESS cor rection using all fragments at the same time re sulted in an accuracy of 83.3%, while applying LOESS correction using the short and long frag ments separately resulted in an accuracy of 91.7%. The impact of filtering the data based on mapping quality was determined by comparing the results of analysing all fragments, analyzing only fragments with mapping quality 5, 20 or 30. This showed that not filtering by mapping quality has a big impact on the profiles of cancer samples, with a KS-test statistic of 0.08 for MAPQ 5 and MAPQ 20 and larger differences in correlations between healthy and cancer samples. The performance of classi fication was much higher when not filtering, with an accuracy of 97.3%, which dropped whenever the filtering threshold was raised, bottoming out at 62.7% for a threshold of MAPQ 30. Due to limita tions with the study, the combined pre-processing of not filtering on MAPQ and using the LOESS separate correction were not studied.

Cell-free DNA (cfDNA) are DNA fragments originating from dying cells that enter the plasma. Uncontrolled cell death, for example caused by cancer, induces an elevated concentration of cfDNA. As a result, determining the cell type origins of cfDNA can provide information about an individual's health. This research looks into how to increase the sensitivity of a methylation-based cell type deconvolution method. We do this by adapting an existing method, CelFiE, which uses the methylation values of individual CpG sites to estimate cell type proportions. Our new method, named CelFEER, instead differentiates cell types by the average methylation values of individual reads. We additionally improved the originally reported performance of CelFiE by using a new approach for finding marker regions in the genome that are differentially methylated between cell types. This approach compares the methylation values over 500 bp regions instead of at single CpG sites and solely takes hypomethylated regions into account. We show that CelFEER estimates cell type proportions with a higher correlation than CelFiE on simulated mixtures of cfDNA. Moreover, we found that it can find a significant difference between skeletal muscle cfDNA in ALS patients (n=4) and a control group (n=4).

Variational Auto-Encoders are a class of machine learning models that have been used in varying context, such as cancer research. Earlier research has shown that initialization plays a crucial part in training these models, since it can increase performance. Therefore, this paper studies the effect initialization methods on VAEs. This research shows that if using only one hidden layer, Uniform methods and Xavier methods perform best depending on the VAE model, where the standard VAE shows the most sensitivity to these methods. But, if using more hidden layers, the uniform method performs significantly worse than a method that uses the number of inputs of the layer such as the default implementation of PyTorch, Xavier Normal or XavierUniform. However, after enough epochs in all other models these initialization methods converge.

Personalized treatment methods for a complex disease such as cancer benefit from using multiple data modalities from a patient's cancer cells. Multiple modalities allow for analysis of dependencies between complex biological processes and downstream tasks, such as drug response and/or expected survival rate. To this end, it is important to gain an understanding of the relationships between modalities in tumor cells. Multimodal Variational Auto-Encoders (MVAEs) are a combination of generative models trained on different sets of data modalities. In this research, the ability of MVAEs to capture common information between different data views from the same tumor cells is assessed. MVAE models discussed here are a Mixture-of-Experts (MoE) and a Product-of-Experts (PoE) approach to combining the generative model posterior distributions into a single common latent space. The performance assessment is done by: i) comparing the loss of information when reconstructing the training data to MOFA+, a linear method for combining multimodal data, and ii) measuring if one modality of a tumor cell can generate another modality, based on characteristics of the latent space learned by the MVAE. Biological data modalities considered are RNA-seq, gene-level copy number and DNA methylation (DNAme), gathered by The Cancer Genome Atlas. It is found that PoE reconstructs data from all data types with a higher accuracy compared to MoE and MOFA+. The mean squared error of PoE's average reconstruction loss is about a quarter of MOFA+'s, and less than a seventh of the MoE's average reconstruction loss. In terms of predicting modalities from other modalities, the PoE again outperforms MoE on all cross-modal predictions. Additionally, it can be concluded that both models have higher losses in their prediction of DNAme from other modalities, indicating a lesser correlation between this data type and the others.

Cancer has been known as a deadly and complex disease to tackle. By applying machine learning algorithms we hope to improve personalized treatment for cancer patients. These machine learning algorithms are trying to learn a (latent) representation of the input. The problem is that this representation is hard to interpret and to observe the actual connections between the input and the output. That is why these algorithms are considered to behave like a black-box. In this research, a benchmark is conducted to measure how sensitive these algorithms are to changes in their hyper-parameters. The focus of this experiment are different types of variational auto encoders. We will measure how sensitive they are to changes in their: latent space dimension, learning rate and type of optimizer. The models will be trained on a dataset that contains the RNA gene-expression of different types of cancer tissues. To conclude that the optimizer may play the most important role performance wise for VAE models. Using the optimizer Adam and RMSprop results overall in lower reconstruction loss and overall in a more consistent performance.

Using RNA sequence data for predicting patient properties is fairly common by now. In this paper, Variational Auto-Encoders (VAEs) are used to assist in this process. VAEs are a type of neural network seeking to encode data into a smaller dimension called latent space. These latent features are then used to do downstream task analysis such as cancer types, survival time and cancer stages, with the help of a MLP classifier. Furthermore, the training process itself is also analyzed with the usage of UMaps. The purpose of this paper is to compare different VAE models on their effectiveness in providing training data used for the predictions. The predictions mostly consist of guessing when using any of the latent spaces, constructed by the VAE models, as input data for the MLP classifier. The NoVAE model is the only model with slightly better performance when it comes to mean accuracy and standard deviation. The guessing issue is further analyzed with the help of UMaps. The VAEs are able to classify the input data during the training process, but when faced with new data, this end up not being the case. Both the learning rate and β term yield interesting results regarding the modification of the input data and variational property respectively. A lower learning rate leads to better classification, but this is due it deviation less from the original input data. When using a small β term with the β-VAE, the output is similar to that of the VanillaVAE. Meaning the VanillaVAE does not perform better than a regular autoencoder.

This study presents a comparison of different VariationalAutoencoder(VAE) models to see which VAE models arebetter at finding disentangled representations. Specificallytheir ability to encode biological processes into distinct la-tent dimensions. The biological processes that will be lookedat are the cell cycle and differentiation state. The cell cycleis expressed as a S- and G2M-Score and the differentiationstate is expressed as a number that quantifies the develop-ment time of the cells. First the models will be trained, afterthat the models will be evaluated. The evaluation is doneby checking the latent dimensions for a correlation with thetwo aforementioned biological processes. From this it be-came quite clear that VAE and DIP-VAE performed theworst out of the four models tested. On the other handβ-VAE andβ-TCVAE performed by far the best.

This paper compares the performance of two phonetic notations, IPA and ASJPcode, with the alphabetical notation for word-level language identification. Two machine learning models, a Multilayer Percerptron and a Logistic Regression model, are used to classify words using each of the three notations. With both models the IPA notation outperforms the other two notations.

Rhyming words are one of the most important features in poems. They add rhythm to a poem, and poets use this literary device to portray emotion and meaning to their readers. Thus, detecting rhyming words will aid in adding emotions and enhancing readability when generating poems. Previous studies have been done on the topic of poem generation. However, those works did not put too much emphasis on the rhyme detector. Thus, this research will solely focus on rhyme detection and its evaluation. The aim of this research is to determine the most accurate way of detecting whether two English words rhyme. English rhyming words will be detected using combinations of features. Five features are used: edit distance, hamming distance, jaccard similarity, longest common substring, and vowel and consonant weights. We also experiment with two methods of retrieving phonemes: using the entire phoneme translation, and using part of the phoneme translation. We find that using only hamming distance and jaccard similarity with part of the phoneme translation, we can already obtain an accuracy of 90.05% with a log loss of 0.25 when trained on a balanced dataset. The reason for this remains unclear because there is no clear separation between the two classes.

In recent years the advent of multi-omic techniques have shown great promise in the field of oncology. In light of these advancements, this thesis focuses on the use of multiple data types to find methylation markers around transcription start site regions for colorectal cancer in the cell-free DNA (cfDNA) domain. It combines several methods of finding these markers, based on publicly available data obtained from solid tissue biopsies. These methods are both based on a single data type, as well as integrating multiple different data types. The resulting selections of methylation markers are then tested for significance on two independent datasets of cfDNA samples. The selections produced are tested on these datasets for their significance in distinguishing colorectal cancer samples from healthy samples. On one of these datasets, the selections are also tested for being differentially methylated between a group of patients with recurring tumors versus non-recurring tumors. The results on these two different datasets vary, showing that the methods of selecting potential methylation markers are capable of doing so on one platform, but that these results cannot be validated on another.

Text classification has a wide range of usage such as extracting the sentiment out of a product review, analyzing the topic of a document and spam detection. In this research, the text classification task is to predict from which TV-show a given line is. The skip-gram model, originally used to train the Word2Vec sentence embeddings [Mikolov et al, 2013], is adapted to determine the likelihood of occurrence of a sentence in a TV-show. Based on this feature, a classifier is built to perform the task of this research. The results of the cross-validation show that it reaches an accuracy of 58% when running on the transcript data of 3 shows and 43% on 4 shows, while the accuracies of random guessing are supposed to be 33% and 25%. The difference between the neural networks and the skip-gram model becomes smaller when more shows are added to evaluate the model. Among each 5 fold cross-validation of the two models, the best results appear in the midmost iterations.

Using transcripts of the TV-series FRIENDS, this paper explores the problem of predicting the location in which a sentence was said. The research focuses on using feature extraction on the sentences, and training a logistic regression model on those features. Specifically looking at the differences in performance between using ELMo and TF-IDF for this feature extraction, achieving an accuracy rate of 58\% and 67\% respectively on a binary classification. The paper also explores the effect of several data cleaning techniques on the results.

Git repository containing the source code used in the paper - https://github.com/David-Happel/scene-location-NLP

Word embeddings are useful for various applications, such as sentiment classification (Tang et al., 2014), word translation (Xing, Wang, Liu, & Lin, 2015) and résumé parsing (Nasser, Sreejith, & Irshad, 2018). Previous research has determined that word embeddings contain gender bias, which can be problematic in certain applications such as résumé parsing. This research has addressed the question whether gender bias is present in word embeddings of different languages. Gender bias has been measured on word embedding of 26 different lan- guages with the help of the Word Embedding Association Test by Caliskan, Bryson, and Narayanan (2017). The results show that most of the tested languages seem to have bias towards male, while a few languages seem to have a bias towards female. This result is in line with previous literature.