Automatic BIRCH thresholding with features transformation for hierarchical breast cancer clustering

Breast cancer is one of the most common diseases diagnosed in women over the world. The balanced iterative reducing and clustering using hierarchies (BIRCH) has been widely used in many applications. However, clustering the patient records and selecting an optimal threshold for the hierarchical clusters still a challenging task. In addition, the existing BIRCH is sensitive to the order of data records and influenced by many numerical and functional parameters. Therefore, this paper proposes a unique BIRCH-based algorithm for breast cancer clustering. We aim at transforming the medical records using the breast screening features into sub-clusters to group the subject cases into malignant or benign clusters. The basic BIRCH clustering is firstly fed by a set of normalized features then we automate the threshold initialization to enhance the tree-based sub-clustering procedure. Additionally, we present a thorough analysis on the performance impact of tuning BIRCH with various relevant linkage functions and similarity measures. Two datasets of the standard breast cancer wisconsin (BCW) benchmarking collection are used to evaluate our algorithm. The experimental results show a clustering accuracy of 97.7% in 0.0004 seconds only, thereby confirming the efficiency of the proposed method in clustering the patient records and making timely decisions

From tools and databases to clinically relevant applications in miRNA research

While especially early research focused on the small portion of the human genome that encodes proteins, it became apparent that molecules responsible for many key functions were also encoded in the remaining regions. Originally, non-coding RNAs, i.e., molecules that are not translated into proteins, were thought to be composed of only two classes (ribosomal RNAs and transfer RNAs). However, starting from the early 1980s many other non-coding RNA classes were discovered. In the past two decades, small non-coding RNAs (sncRNAs) and in particular microRNAs (miRNAs), have become essential molecules in biological and biomedical research. In this thesis, five aspects of miRNA research have been addressed. Starting from the development of advanced computational software to analyze miRNA data (1), an in-depth understanding of human and non-human miRNAs was generated and databases hosting this knowledge were created (2). In addition, the effects of technological advances were evaluated (3). We also contributed to the understanding on how miRNAs act in an orchestrated manner to target human genes (4). Finally, based on the insights gained from the tools and resources of the mentioned aspects we evaluated the suitability of miRNAs as biomarkers (5). With the establishment of next-generation sequencing, the primary goal of this thesis was the creation of an advanced bioinformatics analysis pipeline for high-throughput miRNA sequencing data, primarily focused on human. Consequently, miRMaster, a web-based software solution to analyze hundreds sequencing samples within few hours was implemented. The tool was implemented in a way that it could support different sequencing technologies and library preparation techniques. This flexibility allowed miRMaster to build a consequent user-base, resulting in over 120,000 processed samples and 1,5 billion processed reads, as of July 2021, and therefore laid out the basis for the second goal of this thesis. Indeed, the implementation of a feature allowing users to share their uploaded data contributed strongly to the generation of a detailed annotation of the human small non-coding transcriptome. This annotation was integrated into a new miRNA database, miRCarta, modelling thousands of miRNA candidates and corresponding read expression profiles. A subset of these candidates was then evaluated in the context of different diseases and validated. The thereby gained knowledge was subsequently used to validate additional miRNA candidates and to generate an estimate of the number of miRNAs in human. The large collection of samples, gathered over many years with miRMaster was also integrated into a web server evaluating miRNA arm shifts and switches, miRSwitch. Finally, we published an updated version of miRMaster, expanding its scope to other species and adding additional downstream analysis capabilities. The second goal of this thesis was further pursued by investigating the distribution of miRNAs across different human tissues and body fluids, as well as the variability of miRNA profiles over the four seasons of the year. Furthermore, small non-coding RNAs in zoo animals were examined and a tissue atlas of small non-coding RNAs for mice was generated. The third goal, the assessment of technological advances, was addressed by evaluating the new combinatorial probe-anchor synthesis-based sequencing technology published by BGI, analyzing the effect of RNA integrity on sequencing data, analyzing low-input library preparation protocols, and comparing template-switch based library preparation protocols to ligation-based ones. In addition, an antibody-based labeling sequencing chemistry, CoolMPS, was investigated. Deriving an understanding of the orchestrated regulation by miRNAs, the fourth goal of this thesis, was pursued in a first step by the implementation of a web server visualizing miRNA-gene interaction networks, miRTargetLink. Subsequently, miRPathDB, a database incorporating pathways affected by miRNAs and their targets was implemented, as well as miEAA 2.0, a web server offering quick miRNA set enrichment analyses in over 130,000 categories spanning 10 different species. In addition, miRSNPdb, a database evaluating the effects of single nucleotide polymorphisms and variants in miRNAs or in their target genes was created. Finally, the fifth goal of the thesis, the evaluation of the suitability of miRNAs as biomarkers for human diseases was tackled by investigating the expression profiles of miRNAs with machine learning. An Alzheimer's disease cohort with over 400 individuals was analyzed, as well as another neurodegenerative disease cohort with multiple time points of Parkinson's disease patients and healthy controls. Furthermore, a lung cancer cohort covering 3,000 individuals was examined to evaluate the suitability of an early detection test. In addition, we evaluated the expression profile changes induced by aging on a cohort of 1,334 healthy individuals and over 3,000 diseased patients. Altogether, the herein described tools, databases and research papers present valuable advances and insights into the miRNA research field and have been used and cited by the research community over 2,000 times as of July 2021.Während insbesondere die frühe Genetik-Forschung sich auf den kleinen Teil des menschlichen Genoms konzentrierte, der für Proteine kodiert, wurde deutlich, dass auch in den übrigen Regionen Moleküle kodiert werden, die für viele wichtige Funktionen verantwortlich sind. Ursprünglich ging man davon aus, dass nicht codierende RNAs, d. h. Moleküle, die nicht in Proteine übersetzt werden, nur aus zwei Klassen bestehen (ribosomale RNAs und Transfer-RNAs). Seit den frühen 1980er Jahren wurden jedoch viele andere nicht-kodierende RNA-Klassen entdeckt. In den letzten zwei Jahrzehnten sind kleine nichtcodierende RNAs (sncRNAs) und insbesondere microRNAs (miRNAs) zu wichtigen Molekülen in der biologischen und biomedizinischen Forschung geworden. In dieser Arbeit werden fünf Aspekte der miRNA-Forschung behandelt. Ausgehend von der Entwicklung fortschrittlicher Computersoftware zur Analyse von miRNA-Daten (1) wurde ein tiefgreifendes Verständnis menschlicher und nicht-menschlicher miRNAs entwickelt und Datenbanken mit diesem Wissen erstellt (2). Darüber hinaus wurden die Auswirkungen des technologischen Fortschritts bewertet (3). Wir haben auch dazu beigetragen, zu verstehen, wie miRNAs koordiniert agieren, um menschliche Gene zu regulieren (4). Schließlich bewerteten wir anhand der Erkenntnisse, die wir mit den Tools und Ressourcen der genannten Aspekte gewonnen hatten, die Eignung von miRNAs als Biomarker (5). Mit der Etablierung der Sequenzierung der nächsten Generation war das primäre Ziel dieser Arbeit die Schaffung einer fortschrittlichen bioinformatischen Analysepipeline für Hochdurchsatz-MiRNA-Sequenzierungsdaten, die sich in erster Linie auf den Menschen konzentriert. Daher wurde miRMaster, eine webbasierte Softwarelösung zur Analyse von Hunderten von Sequenzierproben innerhalb weniger Stunden, implementiert. Das Tool wurde so implementiert, dass es verschiedene Sequenzierungstechnologien und Bibliotheksvorbereitungstechniken unterstützen kann. Diese Flexibilität ermöglichte es miRMaster, eine konsequente Nutzerbasis aufzubauen, die im Juli 2021 über 120.000 verarbeitete Proben und 1,5 Milliarden verarbeitete Reads umfasste, womit die Grundlage für das zweite Ziel dieser Arbeit geschaffen wurde. Die Implementierung einer Funktion, die es den Nutzern ermöglicht, ihre hochgeladenen Daten mit anderen zu teilen, trug wesentlich zur Erstellung einer detaillierten Annotation des menschlichen kleinen nicht-kodierenden Transkriptoms bei. Diese Annotation wurde in eine neue miRNA-Datenbank, miRCarta, integriert, die Tausende von miRNA-Kandidaten und entsprechende Expressionsprofile abbildet. Eine Teilmenge dieser Kandidaten wurde dann im Zusammenhang mit verschiedenen Krankheiten bewertet und validiert. Die so gewonnenen Erkenntnisse wurden anschließend genutzt, um weitere miRNA-Kandidaten zu validieren und eine Schätzung der Anzahl der miRNAs im Menschen vorzunehmen. Die große Sammlung von Proben, die über viele Jahre mit miRMaster gesammelt wurde, wurde auch in einen Webserver integriert, der miRNA-Armverschiebungen und -Wechsel auswertet, miRSwitch. Schließlich haben wir eine aktualisierte Version von miRMaster veröffentlicht, die den Anwendungsbereich auf andere Spezies ausweitet und zusätzliche Downstream-Analysefunktionen hinzufügt. Das zweite Ziel dieser Arbeit wurde weiterverfolgt, indem die Verteilung von miRNAs in verschiedenen menschlichen Geweben und Körperflüssigkeiten sowie die Variabilität der miRNA-Profile über die vier Jahreszeiten hinweg untersucht wurde. Darüber hinaus wurden kleine nichtkodierende RNAs in Zootieren untersucht und ein Gewebeatlas der kleinen nichtkodierenden RNAs für Mäuse erstellt. Das dritte Ziel, die Einschätzung des technologischen Fortschritts, wurde angegangen, indem die neue kombinatorische Sonden-Anker-Synthese-basierte Sequenzierungstechnologie, die vom BGI veröffentlicht wurde, bewertet wurde, die Auswirkungen der RNA-Integrität auf die Sequenzierungsdaten analysiert wurden, Protokolle für die Bibliotheksvorbereitung mit geringem Input analysiert wurden und Protokolle für die Bibliotheksvorbereitung auf der Basis von Template-Switch mit solchen auf Ligationsbasis verglichen wurden. Darüber hinaus wurde eine auf Antikörpern basierende Labeling-Sequenzierungschemie, CoolMPS, untersucht. Das vierte Ziel dieser Arbeit, das Verständnis der orchestrierten Regulation durch miRNAs, wurde in einem ersten Schritt durch die Implementierung eines Webservers zur Visualisierung von miRNA-Gen-Interaktionsnetzwerken, miRTargetLink, verfolgt. Anschließend wurde miRPathDB implementiert, eine Datenbank, die von miRNAs und ihren Zielgenen beeinflusste Pfade enthält, sowie miEAA 2.0, ein Webserver, der schnelle miRNA-Anreicherungsanalysen in über 130.000 Kategorien aus 10 verschiedenen Spezies bietet. Darüber hinaus wurde miRSNPdb, eine Datenbank zur Bewertung der Auswirkungen von Einzelnukleotid-Polymorphismen und Varianten in miRNAs oder ihren Zielgenen, erstellt. Schließlich wurde das fünfte Ziel der Arbeit, die Bewertung der Eignung von miRNAs als Biomarker für menschliche Krankheiten, durch die Untersuchung der Expressionsprofile von miRNAs anhand von maschinellem Lernen angegangen. Eine Alzheimer-Kohorte mit über 400 Personen wurde analysiert, ebenso wie eine weitere neurodegenerative Krankheitskohorte mit Parkinson-Patienten an mehreren Zeitpunkten der Krankheit und gesunden Kontrollen. Außerdem wurde eine Lungenkrebskohorte mit 3.000 Personen untersucht, um die Eignung eines Früherkennungstests zu bewerten. Darüber hinaus haben wir die altersbedingten Veränderungen des Expressionsprofils bei einer Kohorte von 1.334 gesunden Personen und über 3.000 kranken Patienten untersucht. Insgesamt stellen die hier beschriebenen Tools, Datenbanken und Forschungsarbeiten wertvolle Fortschritte und Erkenntnisse auf dem Gebiet der miRNA-Forschung dar und wurden bis Juli 2021 von der Forschungsgemeinschaft über 2.000 Mal verwendet und zitiert

Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology

The key to success in machine learning is the use of effective data representations. The success of deep neural networks (DNNs) is based on their ability to utilize multiple neural network layers, and big data, to learn how to convert simple input representations into richer internal representations that are effective for learning. However, these internal representations are sub-symbolic and difficult to explain. In many scientific problems explainable models are required, and the input data is semantically complex and unsuitable for DNNs. This is true in the fundamental problem of understanding the mechanism of cancer drugs, which requires complex background knowledge about the functions of genes/proteins, their cells, and the molecular structure of the drugs. This background knowledge cannot be compactly expressed propositionally, and requires at least the expressive power of Datalog. Here we demonstrate the use of relational learning to generate new data descriptors in such semantically complex background knowledge. These new descriptors are effective: adding them to standard propositional learning methods significantly improves prediction accuracy. They are also explainable, and add to our understanding of cancer. Our approach can readily be expanded to include other complex forms of background knowledge, and combines the generality of relational learning with the efficiency of standard propositional learning

ExoPRIME technology for exosomal miRNA analysis and identification of oxidative DNA damage-induced miRNA regulatory network in human astrocytes

The high lipid content of the brain, coupled with its heavy oxygen dependence and relatively weak antioxidant system, makes it highly susceptible to oxidative DNA damage that contributes to neurodegeneration. This study assesses and compares the neurotoxic effects of proton and photon radiation on mitochondrial function and DNA repair capabilities of human astrocytes. Human astrocytes received either proton (0.5 Gy and 3 Gy), photon (0.5 Gy and 3 Gy), or sham-radiation treatment. The mRNA expression level of the human base-excision repair protein, 8-deoxyguanosine DNA glycosylase 1 (hOGG1) was determined via RT-qPCR. Radiation-induced changes in mitochondrial mass and oxidative activity were assessed using fluorescent imaging with MitoTracker™ Green FM and MitoTracker™ Orange CM-H2TMRos dyes, respectively. A significant increase in mitochondrial mass and levels of reactive oxygen species was observed after radiation treatment. This was accompanied by a decreased OGG1 mRNA expression. These results are indicative of a radiation-induced dose-dependent decrease in mitochondrial function, an increase in senescence and astrogliosis, and impairment of the DNA repair capabilities in healthy glial cells. Photon irradiation was associated with a more significant disruption in mitochondrial function and base-excision repair mechanisms in vitro in comparison to the same dose of proton treatment. This study further identifies specific ROS-responsive miRNAs that modulate the expression and activity of the DNA repair proteins in human astrocytes, which could lead to the development of targeted therapeutic strategies for neurological diseases. Oxidative DNA damage was established after treatment of human astrocytes with 10 μM sodium dichromate for 16 hours. Comet assay analysis indicated a significant increase in oxidized guanine lesions. PCR analysis confirmed that sodium dichromate reduced the mRNA expression levels of hOGG1. Small RNAseq was performed on an Ion Torrent™ system and the differentially expressed miRNAs were identified using Partek Flow® software. The biologically significant miRNAs were selected using miRNet 2.0. Oxidative-stressinduced DNA damage was associated with a significant decrease in miRNA expression: 231 downregulated miRNAs and 2 upregulated miRNAs (p \u3c 0.05; \u3e 2-fold). In addition to identifying multiple miRNA-mRNA pairs involved in DNA repair processes, this study uncovered two novel miRNA-mRNA pairs interactions: miR-1248:OGG1 and miR-103a- OGG1. Inhibition of miR-1248 and miR-103a via the transfection of their inhibitors restored the increased expression levels of hOGG1. Therefore, targeting the identified microRNAs could ameliorate the nuclear DNA damage caused by exposure to mutagens. The miRNA candidates identified in this study could serve as potential biomarkers and therapeutics for oxidative stress in the brain to reduce the incidence and improve the treatment of cancer and neurodegenerative disorders. In a parallel but closely related study, we report a direct, one-step exosome sampling technology, for selective capture of CD63+ exosome subpopulations using an immune-affinity protocol. The ExoPRIME microprobe provides a Precise Rapid Inexpensive Mild (non-invasive) and Efficient (i.e. PRIME) alternative to the conventional polymer precipitation-based methods by enriching a comparatively more homogenous exosome population. The tool consists of an inert Serin™ stainless steelz microneedle (300 μm in diameter × 30 mm in height), pre-coated with a thin-film polyelectrolyte layer that serves as a substrate for covalent bonding of biotin. An anti-CD63 steptavidin-conjugated antibody that selectively binds to the corresponding tetraspanin embedded in the lipid bilayer of exosomes was immobilized to the outer surface of the probe. The feasibility of the ExoPRIME technology was validated using two types of biological samples: conditioned astrocyte medium (CAM) and astrocyte-derived exosome suspension (EXO). The study investigated the impact of the temperature (4°C and 22°C) and incubation duration (2h and 16h) on the capture efficiency of the ExoPRIME tool. A fluorescence-based enzymatic assay for exosome quantification was used to assess the probe’s exosomes capture efficiency and the reproducibility of the technology. The low level of non-specific binding initially observed in non-functionalized microneedles was drastically minimized by blocking the ExoPRIME probe with 0.1% BSA. The ExoPRIME microprobe captured exponentially more exosomes than the non-functionalized microneedle that indicates enrichment of CD63-expressing exosomes. A major advantage provided by the ExoPRIME technology over existing platforms is its applicability over a broad dynamic range of temperature and incubation parameters without compromising the purity and viability of exosomal cargoes. The loading capacity of the probe increased after incubation for 16 h at 40C in exosome suspension (24Å~106 exosomes per probe) while the efficiency decreased 10 folds after 2 h at 40C (24Å~105 exosomes per probe). The increase in temperature had an impact on the stability of the reagents that contributed to a 2-fold efficiency reduction after incubation in exosome suspension for 16 h at 220C (12Å~106 exosomes per probe). However, the 2-hour roomtemperature incubation (2 h at 220C) of the ExoPRIME probe yielded an increased capture efficiency (12Å~106 exosomes per probe) when compared to the 2 h at 4°C incubation (24Å~105 exosomes per probe). These results suggest that lower temperatures with extended incubation times constitute the most optimal parameters that ensure high probe loading capacity. Another advantage of the ExoPRIME microprobe is that it captures antigen-specific subpopulation of exosomes directly from conditioned astrocyte medium (CAM), eliminating the requirements for additional filtration and pre-concentration, and thereby cutting down costs and handling time. Besides the relatively reduced number of enriched exosomes, the CAM results are consistent with the trend obtained for EXO incubations, a phenomenon that could be attributed to the presence of various extracellular proteins and cellular debris, which could mask antibodies and compete physically with exosomes for binding. The capabilities to integrate different incubation times, temperatures, and biofluid type thus present exosome researchers with the flexibility to choose the combined parameters that best suit their purpose, the desired factor in clinical and laboratory applications. The developed tool requires very low amounts of antibody, permits the use and reuse of minimal sample volumes (≤ 200 μL), can be multiplexed in arrays to diagnostically profile multiple exosome classes and is amenable to integration into a lab-on-a-chip platform to achieve parallel, high-throughput isolation in a [semi]-automated workstation. Moreover, this platform could provide direct exosomal analysis of biological fluids since it can elegantly interface with existing picomolar-range nucleic acid assays to provide a clinical diagnostic tool at the point of care and facilitate fundamental studies in exosomes functions

A stew of mixed ingredients: Observational omics in the post-GWAS era

The past 20 years have seen extensive profiling of the DNA. Collectively, scientists all across the world have identified many places in the DNA, known as loci, that impact human traits such as disease state or immune function. However, interpreting the results from these studies, known as genome wide association studies (GWAS), has been challenging. This thesis studies several approaches for interpreting GWAS results, with a specific focus on our immune system given its important role in preventing and causing disease. This is done through the use of so called ‘omics’ technologies, that can study the role of thousands of genes, proteins and genetic variants at the same time. By doing this, maps can be constructed of which genes and proteins interact to impact human traits. The ultimate goal of this research is to provide a better understanding of the cascade between the DNA and human traits. The hope is that building a specific understanding of how the variation in the DNA leads to the development of human traits, such as disease, will ultimately aid the development of drugs for these diseases

Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

A knowledge graph to interpret clinical proteomics data

Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making

Literature on applied machine learning in metagenomic classification: A scoping review

Applied machine learning in bioinformatics is growing as computer science slowly invades all research spheres. With the arrival of modern next-generation DNA sequencing algorithms, metagenomics is becoming an increasingly interesting research field as it finds countless practical applications exploiting the vast amounts of generated data. This study aims to scope the scientific literature in the field of metagenomic classification in the time interval 2008–2019 and provide an evolutionary timeline of data processing and machine learning in this field. This study follows the scoping review methodology and PRISMA guidelines to identify and process the available literature. Natural Language Processing (NLP) is deployed to ensure efficient and exhaustive search of the literary corpus of three large digital libraries: IEEE, PubMed, and Springer. The search is based on keywords and properties looked up using the digital libraries’ search engines. The scoping review results reveal an increasing number of research papers related to metagenomic classification over the past decade. The research is mainly focused on metagenomic classifiers, identifying scope specific metrics for model evaluation, data set sanitization, and dimensionality reduction. Out of all of these subproblems, data preprocessing is the least researched with considerable potential for improvement

Machine Learning Approaches for Healthcare Analysis

Machine learning (ML)is a division of artificial intelligence that teaches computers how to discover difficult-to-distinguish patterns from huge or complex data sets and learn from previous cases by utilizing a range of statistical, probabilistic, data processing, and optimization methods. Nowadays, ML plays a vital role in many fields, such as finance, self-driving cars, image processing, medicine, and Speech recognition. In healthcare, ML has been used in applications such as the detection, prognosis, diagnosis, and treatment of diseases due to Its capability to handle large data. Moreover, ML has exceptional abilities to predict disease by uncovering patterns from medical datasets. Machine learning and deep learning are better suited for analyzing medical datasets than traditional methods because of the nature of these datasets. They are mostly large and complex heterogeneous data coming from different sources, requiring more efficient computational techniques to handle them. This dissertation presents several machine-learning techniques to tackle medical issues such as data imbalance, classification and upgrading tumor stages, and multi-omics integration. In the second chapter, we introduce a novel method to handle class-imbalanced dilemmas, a common issue in bioinformatics datasets. In class-imbalanced data, the number of samples in each class is unequal. Since most data sets contain usual versus unusual cases, e.g., cancer versus normal or miRNAs versus other noncoding RNA, the minority class with the least number of samples is the interesting class that contains the unusual cases. The learning models based on the standard classifiers, such as the support vector machine (SVM), random forest, and k-NN, are usually biased towards the majority class, which means that the classifier is most likely to predict the samples from the interesting class inaccurately. Thus, handling class-imbalanced datasets has gained researchers’ interest recently. A combination of proper feature selection, a cost-sensitive classifier, and ensembling based on the random forest method (BCECSC-RF) is proposed to handle the class-imbalanced data. Random class-balanced ensembles are built individually. Then, each ensemble is used as a training pool to classify the remaining out-bagged samples. Samples in each ensemble will be classified using a class-sensitive classifier incorporating random forest. The sample will be classified by selecting the most often class that has been voted for in all sample appearances in all the formed ensembles. A set of performance measurements, including a geometric measurement, suggests that the model can improve the classification of the minority class samples. In the third chapter, we introduce a novel study to predict the upgrading of the Gleason score on confirmatory magnetic resonance imaging-guided targeted biopsy (MRI-TB) of the prostate in candidates for active surveillance based on clinical features. MRI of the prostate is not accessible to many patients due to difficulty contacting patients, insurance denials, and African-American patients are disproportionately affected by barriers to MRI of the prostate during Active surveillance [6,7]. Modeling clinical variables with advanced methods, such as machine learning, could allow us to manage patients in resource-limited environments with limited technological access. Upgrading to significant prostate cancer on MRI-TB was defined as upgrading to G 3+4 (definition 1 - DF1) and 4+3 (DF2). For upgrading prediction, the AdaBoost model was highly predictive of upgrading DF1 (AUC 0.952), while for prediction of upgrading DF2, the Random Forest model had a lower but excellent prediction performance (AUC 0.947). In the fourth chapter, we introduce a multi-omics data integration method to analyze multi-omics data for biomedical applications, including disease prediction, disease subtypes, biomarker prediction, and others. Multi-omics data integration facilitates collecting richer understanding and perceptions than separate omics data. Our method is constructed using the combination of gene similarity network (GSN) based on Uniform Manifold Approximation and Projection (UMAP) and convolutional neural networks (CNNs). The method utilizes UMAP to embed gene expression, DNA methylation, and copy number alteration (CNA) to a lower dimension creating two-dimensional RGB images. Gene expression is used as a reference to construct the GSN and then integrate other omics data with the gene expression for better prediction. We used CNNs to predict the Gleason score levels of prostate cancer patients and the tumor stage in breast cancer patients. The results show that UMAP as an embedding technique can better integrate multi-omics maps into the prediction model than SO