Colorectal cancer (CRC), one of the leading causes of mortality, is challenging to diagnose. By using metagenomic analysis with machine learning methods, this can be done in a non-invasive manner. In this research, a neural network has been trained on relative pathway abundance data, a way to measure the functional potential of a microbiome, in order to find biomarkers for colorectal cancer. The accuracy achieved by the neural network is 57%. The most important features used by the model are compared to established biomarkers in literature. Besides overlapping pathways, this research also found new potential biomarkers for CRC.

The efficacy of endovascular therapy in large vessel occlusion (LVO) of the anterior circulation is dependent to a high degree on the selection of patients who are likely to benefit from this procedure. To this end, functional outcome prediction based on clinical parameters is an active area of research. In the preoperative screening of LVO patients, CT-Angiography (CTA) imaging is commonly acquired. We compare the functional outcome prediction performance of multiple deep learning based classifiers with multiple conventional methods, including the clinically validated MR PREDICTS decision tool. Using a dataset composed of 1929 preprocedural CTA images combined with clinical data, we compare a clinical baseline model with an imaging based pipeline and a combined pipeline. For the imaging model backbone we train various state-of-the-art architectures (Med3D, Vision Transformer, Voxel Transformer). These models are used to predict dichotomized modified Rankin Scale score 90 days after mechanical thrombectomy. Binary classifier outcomes are quantified using Area-Under the receiver operating characteristic Curve (AUC). The activation maps of the best performing image based model are further investigated using the GradCAM++ posthoc visualization method. Combining clinical features with information extracted from CTA images does not significantly improve the performance of functional outcome prediction methods compared to the baseline model. The information extracted from the images does not seem to be complementary to the clinical features. This multimodal technique can however replace radiologically derived biomarkers, as its performance is non-inferior.

There is mounting evidence indicating a relation- ship between the gut microbiome composition and the development of mental diseases but the mech- anisms remain unclear. Shotgun sequenced data from 90 schizophrenic patients and 81 sex, age, weight, and location matched controls was used for three machine learning models: Logistic Re- gression, Random Forests, and XGBoost. The 20 most relevant species in the decision mak- ing of each classifier was retained and the over- lap between models recorded. There is a total 19 overlapping species between the models’ top 20 most relevant species, with 10 species over- lapping on all three models. Bifidobacterium bi- fidum, Akkermansia muciniphila, Eubacterium sir- aeum, Alistipes finegoldii, Intestinibacter bartlet- tii, Bifidobacterium pseudocatenulatum, and Strep- tococcus thermophilus are of particular interest as they are reported as enriched in schizophrenia sam- ples in existing literatures. Phoceicola vulgatus has been found to play a significant role in the classi- fiers decisions and is enriched in healthy samples in the literature. One species, Ruthenibacterium lactatiformans, and one co-abundant gene group, Eubacterium sp. CAG:180, consistently ranked as the most important features across all three classi- fiers, despite the absence of reporting in existing literature. This study could be expanded by using genus-level data. Further research should be done to validate the species mentioned above as potential biomarkers for schizophrenia.

Survival analysis is a statistical method used to predict when an event will occur. Machine learning survival models have been used in many cancer studies. However, machine learning models may not always be interpretable. The current lack of research for explainable survival analysis for urothelial cancer prompted this study. This study offers an insight into the generalizability and explainability of machine learning models for urothelial cancer. We also determine how we can make the models interpretable in the presence of collinearity. In this study, we compared the performance of the models; Rank Linear Support Vector Machine (SVM), Rank Kernel SVM, Coxnet, Random Survival Forest (RSF), and Gradient Boosting (Gboost). We used the Memorial Sloan Kettering (MSK) and The Cancer Genome Atlas (TCGA) datasets. We used gene expression variables and clinical variables to train our models. We evaluated these models based on the C-index. We used Permutation Feature Importance (PFI), a model-agnostic method, to explain our models and used Principal Component Analysis (PCA) to deal with collinearity. We determined that the best linear model was Rank Linear SVM (C-index = 0.58) and the best non-linear model was RSF (C-index = 0.63). Using PFI showed that some of the top-most important genes were expressed in urothelial cancer, one of them even being a prognostic marker. With PCA, we were able to deal with collinearity, and the performance using PCA was comparable to models not using it. PFI with PCA showed that processes exhibited in the top genes were prevalent in cancer.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor function loss and potential mental and behavioral changes. The identification of biomarkers in the gut microbiota of PD patients can significantly aid in fast and accurate diagnosis. This study investigates the application of machine learning (ML) models, including Logistic Regression (LR), Random Forest (RF), and Support Vector Machines (SVM), to discover biomarkers in the gut metagenomic data of PD patients. The ML models were optimized using various feature selection techniques, and a comparative analysis of the most influential species in sample discrimination was conducted to verify potential PD-associated biomarkers. The results demonstrate that all three ML models exhibit moderate performance, indicating their limited discriminatory power. However, the comparison of significant species across different classifiers demonstrates substantial overlap and indicates PD-associated species that align with existing literature findings. These outcomes provide promising evidence that LR, RF, and SVM classifiers can effectively identify biomarkers for PD. However, confounding analysis on a small subset of the dataset failed to identify meaningful PD-associated species. Therefore, caution is advised when interpreting the findings of ML model, considering factors such as classifier performance, dataset limitations, potential biases, influence of feature selection methods, and inherent model differences. We validate the potential usefulness of ML approaches for biomarker discovery and highlight areas for further investigation into building a sufficiently accurate ML model for PD diagnosis.

Celiac disease is a genetic autoimmune disorder caused by a negative reaction to gluten associated with alterations in the gut microbiome. This study explored the potential of machine learning models and feature selection methods in identifying biomarkers for celiac disease using gut microbiome data. The performance of several machine learning models was evaluated, and the impact of different feature selection methods, including MRMR, ANOVA, and information gain, was examined. The findings revealed comparable performance among the models without feature selection. However, the choice of feature selection method had varying effects on model performance, with logistic regression and support vector machines being more sensitive than random forest and XGBoost models. Notably, several identified bacteria species, such as Bacteroides eggerthii, Parabacteroides johnsonii, Faecalibacterium prausnitzii, and Ruminococcus_D bicirculans, have been previously associated with celiac disease, reinforcing their potential as biomarkers.

In the problem of video summarization, the goal is to select a subset of the input frames conveying the most important information of the input video. The collection of data proves to be a challenging task. In part because there exists a disagreement among human annotators on what segments of a video should be considered important for a summary. In this study we analyse a new dataset created with the goal of increasing agreement between the human annotators. The dataset has been created with the use of a novel annotation method, which uses existing action localization labels for segmenting the videos. We train a supervised and an unsupervised deep learning framework on popularly used benchmark datasets and the new dataset. Experimental results show the effectiveness of this novel summary annotation method in improving the agreement between annotators. Analysis reveals some issues with the evaluation of the deep learning framework.

There is growing research on automated video summarization following the rise of video content. However, the subjectivity of the task itself is still an issue to address. This subjectivity stems from the fact that there can be different summaries for the same video depending on which parts one considers important. Supervised models especially suffer from this problem as they need informative labels to learn from. As a result, upon evaluation, supervised models appear to perform worse than unsupervised models. This inspired our research on whether action localization can aid the video summarization process. To investigate this issue, this paper will answer the question of how well VASNet, a supervised video summarization model, can predict summaries for videos in an action localization dataset. This involves investigating whether action localization can produce well-correlated human-generated summaries and how it affects the quality of predicted summaries. Our findings reveal that there is a positive indication that action localization can aid in producing more well-correlated human summaries. In addition, we have observed that upon comparison with several video summarization models, VASNet has performed well and that in general, supervised models appear to outperform unsupervised ones when trained with an action localization dataset.