Anelloviruses and Cancer.

Anelloviruses are among the most prevalent viruses within the human virome, detected in over 90% of healthy individuals. Despite their ubiquity, the role of anelloviruses in human health remains elusive. This review examines the potential associations of anelloviruses torque teno virus (TTV), torque teno midi virus (TTMDV), and torque teno mini virus (TTMV) with various cancers, highlighting the mixed conclusions from current epidemiological studies.

miRNA in Machine-Learning-Based Diagnostics of Oral Cancer.

Background: MicroRNAs (miRNAs) are crucial regulators of gene expression, playing significant roles in various cellular processes, including cancer pathogenesis. Traditional cancer diagnostic methods, such as biopsies and histopathological analyses, while effective, are invasive, costly, and require specialized skills. With the rising global incidence of cancer, there is a pressing need for more accessible and less invasive diagnostic alternatives.

piRNA in Machine-Learning-Based Diagnostics of Colorectal Cancer.

Objective biomarkers are crucial for early diagnosis to promote treatment and raise survival rates for diseases. With the smallest non-coding RNAs-piwi-RNAs (piRNAs)-and their transcripts, we sought to identify if these piRNAs could be used as biomarkers for colorectal cancer (CRC). Using previously published data from serum samples of patients with CRC, 13 differently expressed piRNAs were selected as potential biomarkers.

Implementation of Machine Learning-Based System for Early Diagnosis of Feline Mammary Carcinomas through Blood Metabolite Profiling.

Feline mammary carcinoma (FMC) is a prevalent and fatal carcinoma that predominantly affects unspayed female cats. FMC is the third most common carcinoma in cats but is still underrepresented in research. Current diagnosis methods include physical examinations, imaging tests, and fine-needle aspiration.

Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Machine Learning and Blood-Based miRNAs.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major cancers due to a lack of symptoms in early stages, early detection tools, and optimal therapies for late-stage patients. Thus, effective and non-invasive diagnostic tests are greatly needed. Recently, circulating miRNAs have been reported to be altered in PDAC.

Colorectal Cancer Detection via Metabolites and Machine Learning.

Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patient's body.

Using Machine Learning and miRNA for the Diagnosis of Esophageal Cancer.

Background: Esophageal cancer (EC) remains a global health challenge, often diagnosed at advanced stages, leading to high mortality rates. Current diagnostic tools for EC are limited in their efficacy. This study aims to harness the potential of microRNAs (miRNAs) as novel, noninvasive diagnostic biomarkers for EC.

Parkinson's Disease Diagnosis Using miRNA Biomarkers and Deep Learning.

Background: The current standard for Parkinson's disease (PD) diagnosis is often imprecise and expensive. However, the dysregulation patterns of microRNA (miRNA) hold potential as a reliable and effective non-invasive diagnosis of PD.

Methods: We use data mining to elucidate new miRNA biomarkers and then develop a machine-learning (ML) model to diagnose PD based on these biomarkers.

Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics.

The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning (ML), the model was developed. We identified seven metabolic pathways related to TC: Pyrimidine metabolism, Tyrosine metabolism, Glycine, serine, and threonine metabolism, Pantothenate and CoA biosynthesis, Arginine biosynthesis, Phenylalanine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis.

Laryngeal cancer diagnosis via miRNA-based decision tree model.

Purpose: Laryngeal cancer (LC) is the most common head and neck cancer, which often goes undiagnosed due to the inaccessible nature of current diagnosis methods in some parts of the world. Many recent studies have shown that microRNAs (miRNAs) are crucial biomarkers for a variety of cancers.

Methods: In this study, we create a decision tree model for the diagnosis of laryngeal cancer using a created series of miRNA attributes, such as sequence-based characteristics, predicted miRNA target genes, and gene pathways.

Role of Senataxin in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive, uncurable neurodegenerative disorder characterized by the degradation of motor neurons leading to muscle impairment, failure, and death. Senataxin, encoded by the SETX gene, is a human helicase protein whose mutations have been linked with ALS onset, particularly in its juvenile ALS4 form. Using senataxin's yeast homolog Sen1 as a model for study, it is suggested that senataxin's N-terminus interacts with RNA polymerase II, whilst its C-terminus engages in helicase activity.

Search of inhibitors of aldose reductase for treatment of diabetic cataracts using machine learning.

Purpose: Patients with diabetes mellitus have an elevated chance of developing cataracts, a degenerative vision-impairing condition often needing surgery. The process of the reduction of glucose to sorbitol in the lens of the human eye that causes cataracts is managed by the Aldose Reductase Enzyme (AR), and it is been found that AR inhibitors may mitigate the onset of diabetic cataracts. There exists a large pool of natural and synthetic AR inhibitors that can prevent diabetic complications, and the development of a machine-learning (ML) prediction model may bring new AR inhibitors with better characteristics into clinical use.

Two-Stage Deep-Learning Classifier for Diagnostics of Lung Cancer Using Metabolites.

We developed a machine-learning system for the selective diagnostics of adenocarcinoma (AD), squamous cell carcinoma (SQ), and small-cell carcinoma lung (SC) cancers based on their metabolomic profiles. The system is organized as two-stage binary classifiers. The best accuracy for classification is 92%.

Synergism in actions of HBV with aflatoxin in cancer development.

Aflatoxin B1 (AFB1) is a fungal metabolite found in animal feeds and human foods. It is one of the most toxic and carcinogenic of aflatoxins and is classified as a Group 1 carcinogen. Dietary exposure to AFB1 and infection with chronic Hepatitis B Virus (HBV) make up two of the major risk factors for hepatocellular carcinoma (HCC).

Targeting of insulin receptor endocytosis as a treatment to insulin resistance.

Background: Insulin resistance is the decreased effectiveness of insulin receptor function during signaling of glucose uptake. Insulin receptors are regulated by endocytosis, a process that removes receptors from the cell surface to be marked for degradation or for re-use.

Objectives: Our goal was to discover insulin-resistance-related genes that play key roles in endocytosis which could serve as potential biological targets to enhance insulin sensitivity.

Biological functions of the Olig gene family in brain cancer and therapeutic targeting.

The genes encode members of the basic helix-loop-helix (bHLH) family of transcription factors. Olig1, Olig2, and Olig3 are expressed in both the developing and mature central nervous system (CNS) and regulate cellular specification and differentiation. Over the past decade extensive studies have established functional roles of and in development as well as in cancer.

Small Molecule Decoys of Aggregation for Elimination of Aβ-Peptide Toxicity.

Several lines of evidence suggest that a characteristic of the neuropathology of Alzheimer's disease (AD) is the aggregation of the amyloid beta peptides (Aβ), fragments of the human amyloid precursor protein (hAPP). The dominating species are the Aβ40 and Aβ42 fragments with 40 and 42 amino acids, respectively. Aβ initially forms soluble oligomers that continue to expand to protofibrils, suggestively the neurotoxic intermediates, and thereafter turn into insoluble fibrils that are markers of the disease.

Diagnostics of ovarian cancer via metabolite analysis and machine learning.

Ovarian cancer (OC) is the second most common cancer of the female reproductive system. Due to the asymptomatic nature of early stages of OC and an increasingly poor prognosis in later stages, methods of screening for OC are much desired. Furthermore, screening and diagnosis processes, in order to justify use on asymptomatic patients, must be convenient and non-invasive.

Repurposing of drugs for combined treatment of COVID-19 cytokine storm using machine learning.

Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) induced cytokine storm is the major cause of COVID-19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines production. This approach provided very limited success, since there are multiple proteins involved in the complex cell signaling disease mechanisms.

Structure-selected RBM immunogens prime polyclonal memory responses that neutralize SARS-CoV-2 variants of concern.

Successful control of the COVID-19 pandemic depends on vaccines that prevent transmission. The full-length Spike protein is highly immunogenic but the majority of antibodies do not target the virus: ACE2 interface. In an effort to affect the quality of the antibody response focusing it to the receptor-binding motif (RBM) we generated a series of conformationally-constrained immunogens by inserting solvent-exposed RBM amino acid residues into hypervariable loops of an immunoglobulin molecule.

The role of BAG3 in dilated cardiomyopathy and its association with Charcot-Marie-Tooth disease type 2.

Bcl2-associated athanogene 3 (BAG3) is a multifunctional cochaperone responsible for protein quality control within cells. BAG3 interacts with chaperones HSPB8 and Hsp70 to transport misfolded proteins to the Microtubule Organizing Center (MTOC) and degrade them in autophagosomes in a process known as Chaperone Assisted Selective Autophagy (CASA). Mutations in the second conserved IPV motif of BAG3 are known to cause Dilated Cardiomyopathy (DCM) by inhibiting adequate removal of non-native proteins.

Machine-learning-based virtual screening to repurpose drugs for treatment of Candida albicans infection.

Background: Approximately 30% of Candida genus isolates are resistant to all currently available antifungal drugs and it is highly important to develop new treatments. Additionally, many current drugs are toxic and cause unwanted side effects. 1,3-beta-glucan synthase is an essential enzyme that builds the cell walls of Candida.

Potential SARS-CoV-2 nonstructural proteins inhibitors: drugs repurposing with drug-target networks and deep learning.

Background: In the current COVID-19 pandemic, with an absence of approved drugs and widely accessible vaccines, repurposing existing drugs is vital to quickly developing a treatment for the disease.

Methods: In this study, we used a dataset consisting of sequences of viral proteins and chemical structures of pharmaceutical drugs for known drug-target interactions (DTIs) and artificially generated non-interacting DTIs to train a binary classifier with the ability to predict new DTIs. Random Forest (RF), deep neural network (DNN), and convolutional neural networks (CNN) were tested.