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Finding biological markers for Parkinson's disease: Using machine learning to analyse metagenomic data

Koning, Marilotte (author)

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...

bachelor thesis 2023

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Identifying biological markers in the gut microbiome associated with celiac disease using machine learning

Persianov, Petr (author)

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...

bachelor thesis 2023

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Finding Biomarkers for Type 2 Diabetes

Das, Aratrika (author)

Type 2 Diabetes is a very prevalent disease in current times and leads to significant adverse effects. Recently, there has been a growing interest in the association of the human gut microbiome with respect to chronic diseases like Type 2 Diabetes with the aim to identify biomarkers. In this study, we researched the effect of different machine...

bachelor thesis 2023

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Finding biological markers for the prediction of colorectal cancer: Using machine learning methods to identify functional biomarkers in the human gut microbiome

Sloof, Jos (author)

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...

bachelor thesis 2023

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Finding Biomarkers for Schizophrenia: Can Machine Learning algorithms identify schizophrenia-related biomarkers within metagenomic data derived from the human gut microbiome?

Bastow, Timothy (author)

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,...

bachelor thesis 2023

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Using metagenomic Hi-C data to discover broad host-range plasmids conferring antimicrobial resistance

Dorrestijn, Eljo (author)

Horizontal gene transfer (HGT) trough plasmids is one of the main contributors to the rapid increase of antimicrobial resistance (AMR). Studying wastewater from wastewater treatment plants (WWTPs) allows us new insights into HGT as bacteria from different sources come together. Currently the analysis of HGT is limited, as plasmids cannot be...

master thesis 2023

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Reference-free biomarker mining in metagenomic data using language embedding

Agrawal, Isha (author)

Metagenomic Next-Generation Sequencing (mNGS) presents a promising avenue to generate massive volume of sequence reads in a short period of time. This has opened opportunities for disease diagnosis based on individual variations and mutations by considering the microbiome profile of each patient. However, the effective use of this data requires...

master thesis 2023

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Host- Microbiome Omics Integration for Cancer Analysis and Diagnostics: Investigating the added value of integrating microbial and host omics information for cancer diagnostics using prediction models

d' Abreu de Paulo, Gedeon (author)

Cancer is one of the leading causes of death in the world. While there have been many studies investigating the development and progression of cancer in human tissues using host omics data or microbial data, there is a lack of research combining both types of data, even though both modalities have been shown to affect cancer morphology and...

master thesis 2023

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Explainable AI for metabolic engineering

van Graft, Tijmen (author)

Metabolic engineering is an important field in biotechnology, aimed at optimizing cellular processes to produce desired compounds. In this thesis, we focus on predicting the metabolome from the proteome, as understanding this relationship is crucial for understanding cellular metabolism. We investigate the usage of additional biological...

master thesis 2023

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Evaluation of natural language processing embeddings in protein function prediction for bacteria

Cosma, Bianca (author)

<b>Motivation: </b>The development of automated protein function prediction models is essential in closing the gap between the large amount of protein sequence data available and the fraction of validly annotated data. Recent approaches to function prediction rely on unsupervised deep learning models, through which protein sequences are...

bachelor thesis 2022

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Batch correction of taxonomic data of the human gut microbiome for generalization of case-control classification

van der Toorn, Eric (author)

Next-Generation Sequencing (NGS) has made it possible to perform metagenomic sequencing of environmental microbiome samples. Colorectal cancer (CRC) benefits from early detection, and many studies find correlations between disease presence and abundance of species in samples of the microbiome. However, these studies are hard to reproduce and...

master thesis 2022

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The Effect of Color Spaces and Spectra on a Strawberry Prediction Model

Rosenberg, Jaron (author)

The purpose of this research is to reduce food waste by monitoring the ripening process of strawberries in order to optimize the harvesting time. To improve the moment of harvest, we need to know the ripeness of a strawberry. Using data from different color ranges and spaces we should be able to predict the ripeness of a strawberry on a 1-10...

bachelor thesis 2022

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Using pre-trained convolutional neural networks to predict maturity levels of strawberries

Buitenweg, Jurriaan (author)

To reduce food waste, the strawberry harvesting process should be optimized. In the modern era, computer vision can provide huge amounts of help. This paper focuses on optimizing pre-trained convolutional neural networks (CNN) to determine the maturity level of strawberries on a 1-10 scale. Here, 1 means unripe and 10 means overripe. Maturity...

bachelor thesis 2022

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Detecting strawberries using different Convolutional Neural Networks

Bechtold, Jeroen (author)

This paper tries to combat the food waste of strawberries during the harvesting steps.<br/>An automatic pipeline must be established to combat this food waste.<br/>One of the steps needed in this pipeline is detecting strawberries in images.<br/>Therefore, this paper aims to find out which Convolutional Neural Network (CNN) can be best used to...

bachelor thesis 2022

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Measuring the size of strawberries using binocular photos

van Melis, Kris (author)

To reduce food waste, it is important to know what strawberries to prioritise for harvesting. Size is an important quality attribute for strawberry. In order to know the size, the depth of the strawberry in the image must been known. To estimate the depth, stereovision gets utilized using binocular images. Since classic stereovision methods are...

bachelor thesis 2022

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Benchmarking of viral quasispecies assembly algorithms

Narkhede, Rucha (author)

Viral quasispecies refers to viral populations that comprises of numerous viral strains closely related to each other due to within-host evolution or co-infection. The reconstruction of viral strain-specific genomes using sequencing reads is referred to as viral quasispecies assembly, and it is also crucial to determine the relative abundances...

master thesis 2022

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Explainable artificial intelligence in forensics: realistic explanations for number of contributor predictions of DNA profiles

Veldhuis, Marthe (author)

Using machine learning to determine the number of contributors (NOC) in short tandem repeat (STR) mixture DNA profiles has been shown to obtain good accuracy. However, the models used so far are not transparent to users as they only output a prediction without any reasoning for that conclusion. Therefore, we leverage techniques from the field of...

master thesis 2021

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A plug-in infrastructure for the CodeFeedr project

Kuijpers, Jos (author), Quist, Joris (author), Zorgdrager, Wouter (author)

CodeFeedr is a research project at the software engineering division of the Delft University of Technology in collaboration with the Software Improvement Group. The research focuses on a software infrastructure which serves software practitioners in utilizing data-driven decision making. Currently, frameworks like Apache Flink are capable of...

bachelor thesis 2018

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A Bayesian approach to haplotype-aware de novo genome assembly for polyploid organisms

van Dijk, Lucas (author)

Capturing all genetic variation within a polyploid organism is a challenge. Most current de novo assemblers have no notion of the concept “ploidy”. Consequently, when assembling the genome of diploid or higher ploidy organisms, the assembler mixes reads coming from either chromosome copy and builds a single DNA sequence representing an arbitrary...

master thesis 2017

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Population graph of Mycobacterium Tuberculosis

Mouzos, L. (author)

In recent years, cheap sequencing has resulted in hundreds or thousands of individual genomes available for many species. Therefore, it is logical to rethink the concept of the reference genome; as consisting of a population of genomes rather than being a single one. The term pan-genome describes a population of genomes of the same species or a...

master thesis 2017