bioinformatics using machine learning

BACKGROUND: Despite the recognized roles of Sialic acid-binding Ig-like lectins (SIGLECs) in endocytosis and immune regulation across cancers, their molecular intricacies in colon adenocarcinoma (COAD) are underexplored. Meanwhile, the complicated interactions between different SIGLECs are also crucial but open questions. METHODS: We investigate the correlation between SIGLECs and various properties, including cancer status, prognosis, clinical features, functional enrichment, immune cell abundances, immune checkpoints, pathways, etc...

Background: Acute myeloid leukemia (AML) is the most common form of leukemia among adults and is characterized by uncontrolled proliferation and clonal expansion of hematopoietic cells. There has been a significant improvement in the treatment of younger patients, however, prognosis in the elderly AML patients remains poor. Methods: We used computational methods and machine learning (ML) techniques to identify and explore the differential high-risk genes (DHRGs) in AML. The DHRGs were explored through multiple in silico approaches including genomic and functional analysis, survival analysis, immune infiltration, miRNA co-expression and stemness features analyses to reveal their prognostic importance in AML...

MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. Bioinformatic tools could improve efficiency of miRNA research, while current bioinformatic tools are in lack of sufficient accuracy. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in the bioinformatical tools...

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains a serious threat to health, with limited effective therapeutic options, especially due to advanced stage at diagnosis and its inherent resistance to chemotherapy, making it one of the leading causes of cancer-related deaths worldwide. The lack of clear treatment directions underscores the urgent need for innovative approaches to address and manage this deadly condition. In this research, we repurpose drugs with potential anti-cancer activity using machine learning (ML)...

OBJECTIVE: To screen for the key characteristic genes of the psoriasis vulgaris (PV) patients with different Traditional Chinese Medicine (TCM) syndromes, including blood-heat syndrome (BHS), blood stasis syndrome (BSS), and blood-dryness syndrome (BDS), through bioinformatics and machine learning and to provide a scientific basis for the clinical diagnosis and treatment of PV of different TCM syndrome types. METHODS: The GSE192867 dataset was downloaded from Gene Expression Omnibus (GEO)...

OBJECTIVE: To identify inflamm-aging related biomarkers in osteoarthritis (OA). METHODS: Microarray gene profiles of young and aging OA patients were obtained from the Gene Expression Omnibus (GEO) database and aging-related genes (ARGs) were obtained from the Human Aging Genome Resource (HAGR) database. The differentially expressed genes of young OA and older OA patients were screened and then intersected with ARGs to obtain the aging-related genes of OA. Enrichment analysis was performed to reveal the potential mechanisms of aging-related markers in OA...

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model demonstrates remarkable performance, achieving an AUC of 0.856 and 0.823 on the PepBCL Set_1 and Set_2 datasets, respectively...

BACKGROUND: Classification of binary data arises naturally in many clinical applications, such as patient risk stratification through ICD codes. One of the key practical challenges in data classification using machine learning is to avoid overfitting. Overfitting in supervised learning primarily occurs when a model learns random variations from noisy labels in training data rather than the underlying patterns. While traditional methods such as regularization and early stopping have demonstrated effectiveness in interpolation tasks, addressing overfitting in the classification of binary data, in which predictions always amount to extrapolation, demands extrapolation-enhanced strategies...

Acute pancreatitis (AP) is a common systemic inflammatory disease resulting from the activation of trypsinogen by various incentives in ICU. The annual incidence rate is approximately 30 out of 100,000. Some patients may progress to severe acute pancreatitis, with a mortality rate of up to 40%. Therefore, the goal of this article is to explore the key genes for effective diagnosis and treatment of AP. The analysis data for this study were merged from two GEO datasets. 1357 DEGs were used for functional enrichment and cMAP analysis, aiming to reveal the pathogenic genes and potential mechanisms of AP, as well as potential drugs for treating AP...

Early diagnosis of peri-implantitis (PI) is crucial to understand its pathological progression and prevention. This study is committed to investigating the signature genes, relevant signaling pathways and their associations with immune cells in PI. We analyzed differentially expressed genes (DEGs) from a PI dataset in the gene expression omnibus database. Functional enrichment analysis was conducted for these DEGs. Weighted Gene Co-expression Network Analysis was used to identify specific modules. Least absolute shrinkage and selection operator and support vector machine recursive feature elimination were ultimately applied to identify the signature genes...

OBJECTIVE: To explor the potential mechanisms of ferroptosis involvement in non-obstructive azoospermia based on bioinformatics and machine learning methods. METHODS: To obtain disease-related datasets and ferroptosis-related genes, we utilized the GEO database and FerrDb database, respectively. Using the R software, the disease dataset was subjected to normalization, differential analysis, and GO and KEGG enrichment analysis. The differentially expressed genes from the disease dataset were then intersected with the ferroptosis-related genes to identify common genes...

INTRODUCTION: The CLDN18 gene, encoding claudin 18.1 and claudin 18.2, is a key component of tight junction strands in epithelial cells that form a paracellular barrier that is critical in Stomach Adenocarcinoma (STAD). METHODS: Our study included 1,095 patients with proven STAD, 415 from The Cancer Genome Atlas (TCGA) cohort and 680 from the Gene Expression Omnibus database. We applied various analyses, including gene set enrichment analysis, pathway analysis, and in vitro drug screening to evaluate survival, immune cells, and genes and gene sets associated with cancer progression, based on CLDN18 expression levels...

BACKGROUND: Osteoarthritis and lipid metabolism are strongly associated, although the precise targets and regulatory mechanisms are unknown. METHODS: Osteoarthritis gene expression profiles were acquired from the GEO database, while lipid metabolism-related genes (LMRGs) were sourced from the MigSB database. An intersection was conducted between these datasets to extract gene expression for subsequent differential analysis. Following this, functional analyses were performed on the differentially expressed genes (DEGs)...

Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants of unknown significance (VUS). Bioinformatic analyses can successfully reveal rare variants and variants with clear associations with disease-related phenotypes. These studies have had a significant impact on how clinical genetic screens are interpreted and how patients are stratified for treatment. There are few, if any, computational methods for variants comparable to biological activity predictions...

Spinach ( Spinacia oleracea ) is an important leafy crop possessing notable economic value and health benefits. Current genomic resources include reference genomes and genome-wide association studies. However, the worldwide genetic relationships and the migration history of the crop remained uncertain, and genome-wide association studies have produced extensive gene lists related to agronomic traits. Here, we re-analysed the sequenced genomes of 305 cultivated and wild spinach accessions to unveil the phylogeny and history of cultivated spinach and to explore genetic variation in relation to phenotypes...

A pathology-supported genetic testing (PSGT) framework was established in South Africa to improve access to precision medicine for patients with breast carcinomas. Nevertheless, the frequent identification of variants of uncertain significance (VUSs) with the use of genome-scale next-generation sequencing has created a bottleneck in the return of results to patients. This review highlights the importance of incorporating functional genomics into the PSGT framework as a proposed initiative. Here, we explore various model systems and experimental methods available for conducting functional studies in South Africa to enhance both variant classification and clinical interpretation...

Plant genomes include large numbers of transposable elements. One particular type of these elements is flanked by two Long Terminal Repeats (LTRs) and can translocate using RNA. Such elements are known as LTR-retrotransposons; they are the most abundant type of transposons in plant genomes. They have many important functions involving gene regulation and the rise of new genes and pseudo genes in response to severe stress. Additionally, LTR-retrotransposons have several applications in biotechnology. Due to the abundance and the importance of LTR-retrotransposons, multiple computational tools have been developed for their detection...

BACKGROUND: With the rapid increase in throughput of long-read sequencing technologies, recent studies have explored their potential for taxonomic classification by using alignment-based approaches to reduce the impact of higher sequencing error rates. While alignment-based methods are generally slower, k-mer-based taxonomic classifiers can overcome this limitation, potentially at the expense of lower sensitivity for strains and species that are not in the database. RESULTS: We present MetageNN, a memory-efficient long-read taxonomic classifier that is robust to sequencing errors and missing genomes...

Osteosarcoma (OS), known for its high recurrence and metastasis rates, poses a significant challenge in oncology. Our research investigates the role of programmed cell death (PCD) genes in OS and develops a prognostic model using advanced bioinformatics. We analyzed single-cell sequencing data from the Gene Expression Omnibus (GEO) database to identify subpopulations, distinguish malignant from non-malignant cells, assess cell cycle phases, and map PCD gene distribution. Additionally, we applied consistency clustering to bulk sequencing data from GEO and TARGET (Therapeutically Applicable Research to Generate Effective Treatments) databases, facilitating survival analysis across clusters with the Kaplan-Meier method...

BACKGROUND: Severe burns may alter the stability of the intestinal flora and affect the patient's recovery process. Understanding the characteristics of the gut microbiota in the acute phase of burns and their association with phenotype can help to accurately assess the progression of the disease and identify potential microbiota markers. METHODS: We established mouse models of partial thickness deep III degree burns and collected faecal samples for 16 S rRNA amplification and high throughput sequencing at two time points in the acute phase for independent bioinformatic analysis...