The Nottingham Prognostics Index (NPI) is a prognostics measure that predicts operable primary breast cancer survival. NPI value calculated based on the size of tumor, number lymph nodes, and tumor grade. Next-generation sequencing advancements have led to measuring different biological indicators called multi-omics data. availability data triggered challenge integrating analyzing these various measures understand progression diseases. High-dimensional embedding techniques are incorporated...

Studying breast cancer survivability among different patients who received various treatment therapies may help us understand the relationship between and of based on genetic expression. In this work, we present a classification system that predicts whether given patient underwent through hormone therapy, radiotherapy, or surgery will survive beyond five years after treatment. Our classifier is tree-based hierarchical approach groups classes. Each node in tree associated with therapy subset...

Circadian rhythms are daily physiological oscillations driven by the circadian clock: a 24-hour transcriptional timekeeper that regulates hormones, inflammation, and metabolism. known to be important for health, but whether their loss contributes colorectal cancer is not known. We tested nonredundant clock gene Bmal1 in intestinal homeostasis tumorigenesis, using Apcmin model of cancer.Bmal1 mutant, epithelium-conditional photoperiod (day/night cycle) disrupted mice bearing allele were...

Prostate cancer is one of the most common types among Canadian men. Next-generation sequencing using RNA-Seq provides large amounts data that may reveal novel and informative biomarkers. We introduce a method uses machine learning techniques to identify transcripts correlate with prostate development progression. have isolated potential serve as prognostic indicators tremendous value in guiding treatment decisions. Analysis normal versus malignant sets indicates differential expression genes...

Multi-omics data integration facilitates collecting richer understanding and perceptions than separate omics data. Various promising integrative approaches have been utilized to analyze multi-omics for biomedical applications, including disease prediction subtypes, biomarker prediction, others.In this paper, we introduce a method that is constructed using the combination of gene similarity network (GSN) based on uniform manifold approximation projection (UMAP) convolutional neural networks...

Colorectal cancer (CRC) is one of the most common and lethal diseases among all types cancer, metabolites play a significant role in development this complex disease. This study aimed to identify potential biomarkers targets diagnosis treatment CRC using high-throughput metabolomics. Metabolite data extracted from feces patients healthy volunteers were normalized with median normalization Pareto scale for multivariate analysis. Univariate ROC analysis, t-test, analysis fold changes (FCs)...

Diabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis DR and precise treatment strategies based on explainable artificial intelligence (XAI) framework. The integrated clinical, biochemical, metabolomic biomarkers associated with following classes: non-DR (NDR), non-proliferative diabetic (NPDR), proliferative (PDR) in type 2 diabetes (T2D)...

Finding the tumor location in prostate is an essential pathological step for cancer diagnosis and treatment. The of - laterality can be unilateral (the affecting one side prostate), or bilateral on both sides. Nevertheless, overestimated underestimated by standard screening methods. In this work, a combination efficient machine learning methods feature selection classification are proposed to analyze gene activity select them as relevant biomarkers different samples.A data set that consists...

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating illness with significant global prevalence, affecting over 65 million individuals. It affects various systems, including the immune, neurological, gastrointestinal, circulatory systems. Studies have shown abnormalities in immune cell types, increased inflammatory cytokines, brain abnormalities. Further research needed to identify consistent biomarkers develop targeted therapies. This study uses...

Background: This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated bioinformatics techniques. Methods: Univariate multivariate analyses such as fold change, a receiver operating characteristic curve (ROC), Partial Least-Squares Discriminant Analysis (PLS-DA) were used biomarker metabolites that showed significant concentration in T2D patients. Three...

(1) Background:One of the most common cancers that affect North American men and worldwide is prostate cancer. The Gleason score a pathological grading system to examine potential aggressiveness disease in tissue. Advancements computing next-generation sequencing technology now allow us study genomic profiles patients association with their different scores more accurately effectively. (2) Methods: In this study, we used novel machine learning method analyse gene expression tumours scores,...

AimsThe multi-omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential for enhancing predictive models. The main motivation behind this study stems from imperative need to advance prognostic methodologies in cancer diagnosis, an area where precision is pivotal effective clinical decision-making. In context, present introduces innovative methodology that integrates copy number alteration (CNA), DNA methylation, and gene...

Identifying menopause-related breast cancer biomarkers is crucial for enhancing diagnosis, prognosis, and personalized treatment at that stage of the patient’s life. In this paper, we present a comprehensive framework extracting multiomics specifically related to incidence before after menopause. Our approach integrates DNA methylation, gene expression, copy number alteration data using systematic pipeline encompassing preprocessing handling class imbalance, dimensionality reduction,...

Background: Prostate cancer is complicated by a high level of unexplained variability in the aggressiveness newly diagnosed disease. Given that this one most prevalent cancers worldwide, finding biomarkers to effectively stratify risk patient populations vital next step improving survival rates and quality life after treatment. Materials Methods: In study, we selected dataset consisting 106 prostate samples, which represent various stages developed RNA-Seq technology. Our objective identify...

Next-generation sequencing technology generates a huge number of reads (short sequences), which contain vast amount genomic data. The process, however, comes with artifacts. Preprocessing sequences is mandatory for further downstream analysis. We present Zseq, linear method that identifies the most informative and reduces biased sequences, sequence duplications, ambiguous nucleotides. Zseq finds complexity by counting unique k-mers in each as its corresponding score also takes into account...

Clustering is a prominent method to identify similar patterns in large groups of data and can be beneficial the bioinformatics studies due this property. Classical methods such as k-means maximum likelihood consider mixture Gaussian probability density function (PDF) find clusters based on maximizing PDF. However, correlation among different existence noise make it difficult correctly detect correct number clusters. Furthermore, assumption distance for PDF not necessarily true real...

Many bioinformatics data sets have class-imbalanced data, where the number of samples in each class is not equal. Since most contain usual versus unusual cases, e.g. cancer normal or miRNAs other non-coding RNA, minority with least interesting that contains cases. The learning models based on standard classifiers, such as support vector machine (SVM), random forest and k-NN are usually biased towards majority class, which means classifier likely to predict from inaccurately. Thus, handling...

1) Background: One of the most common cancer that affects men worldwide and North American is prostate cancer. Gleason score a pathological grading system to examine potential aggressiveness disease in tissue. The advancement computing next-generation sequencing technology now allow us study genomic profiles patients association with their different more accurately effectively. 2) Methods: In this study, we used novel machine learning method analyze gene expression tumors scores, identify...

Breast cancer is a widespread type in females and accounts for lots of cases deaths the world. Identifying breast plays crucial role selecting best treatment. In this paper an optimized hierarchical model proposed to predict subtype. Suitable filter feature selection methods new hybrid are utilized our find discriminative genes. The multi-class problem handled using proper classifier at each step separate subtype from others. parameters achieve better performance. Our achieves 100% accuracy...