Identification of associations between lncRNA and drug resistance based on deep learning and attention mechanism

Deep IDA: a deep learning approach for integrative discriminant analysis of multi-omics data with feature ranking-an application to COVID-19

Motivation

Many diseases are complex heterogeneous conditions that affect multiple organs in the body and depend on the interplay between several factors that include molecular and environmental factors, requiring a holistic approach to better understand disease pathobiology. Most existing methods for integrating data from multiple sources and classifying individuals into one of multiple classes or disease groups have mainly focused on linear relationships despite the complexity of these relationships. On the other hand, methods for nonlinear association and classification studies are limited in their ability to identify variables to aid in our understanding of the complexity of the disease or can be applied to only two data types.

Results

We propose Deep Integrative Discriminant Analysis (IDA), a deep learning method to learn complex nonlinear transformations of two or more views such that resulting projections have maximum association and maximum separation. Further, we propose a feature ranking approach based on ensemble learning for interpretable results. We test Deep IDA on both simulated data and two large real-world datasets, including RNA sequencing, metabolomics, and proteomics data pertaining to COVID-19 severity. We identified signatures that better discriminated COVID-19 patient groups, and related to neurological conditions, cancer, and metabolic diseases, corroborating current research findings and heightening the need to study the post sequelae effects of COVID-19 to devise effective treatments and to improve patient care.

Availability and implementation

Our algorithms are implemented in PyTorch and available at: https://github.com/JiuzhouW/DeepIDA.

Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends

Next-generation cancer and oncology research needs to take full advantage of the multimodal structured, or graph, information, with the graph data types ranging from molecular structures to spatially resolved imaging and digital pathology, biological networks, and knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on large multimodal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. We then identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise guidelines for cancer and oncology researchers or physician-scientists, asking the question of whether they should adopt the GNN methodology in their research pipelines.

Non-Coding RNAs and the Development of Chemoresistance to Docetaxel in Prostate Cancer: Regulatory Interactions and Approaches Based on Machine Learning Methods

Chemotherapy based on taxane-class drugs is the gold standard for treating advanced stages of various oncological diseases. However, despite the favorable response trends, most patients eventually develop resistance to this therapy. Drug resistance is the result of a combination of different events in the tumor cells under the influence of the drug, a comprehensive understanding of which has yet to be determined. In this review, we examine the role of the major classes of non-coding RNAs in the development of chemoresistance in the case of prostate cancer, one of the most common and socially significant types of cancer in men worldwide. We will focus on recent findings from experimental studies regarding the prognostic potential of the identified non-coding RNAs. Additionally, we will explore novel approaches based on machine learning to study these regulatory molecules, including their role in the development of drug resistance.

Defining new biomarkers for overcoming therapeutical resistance in cervical cancer using lncRNA

Despite improvements in cervical cancer diagnosis and treatment, the prognosis for cervical cancer patients remains dismal due to the development of drug resistance, metastasis, and invasion resulting leading to treatment failure. Long non-coding RNAs (lncRNAs), a class of RNA transcripts have been reported in mediating carcinogenesis as well as drug, and radio-resistance in tumor cells. These lncRNAs regulate various cancer hallmarks and contribute to the development of therapeutic resistance. They regulates multiple signalling pathways, recruits polycomb group, function as miRNA sponge and scaffolds. Additionally, lncRNAs can act as oncogenes or tumor suppressors in cervical cancer. This comprehensive review outlines the biogenesis of lncRNA and its role in cancer development. It delves into the mechanisms through which various lncRNAs mediate chemoresistance and radioresistance in cervical cancer. By shedding into the light of mechanism, this review will also aids researchers in understanding lncRNAs as biomarkers and latest advancements in clinically targeting them with the help of Artificial Intelligence for overcoming chemoresistance and radioresistance, thereby improving cervical cancer treatment.

DNA and RNA Molecules as a Foundation of Therapy Strategies for Treatment of Cardiovascular Diseases

There has always been a tendency of medicine to take an individualised approach to treating patients, but the most significant advances were achieved through the methods of molecular biology, where the nucleic acids are in the limelight. Decades of research of molecular biology resulted in setting medicine on a completely new platform. The most significant current research is related to the possibilities that DNA and RNA analyses can offer in terms of more precise diagnostics and more subtle stratification of patients in order to identify patients for specific therapy treatments. Additionally, principles of structure and functioning of nucleic acids have become a motive for creating entirely new therapy strategies and an innovative generation of drugs. All this also applies to cardiovascular diseases (CVDs) which are the leading cause of mortality in developed countries. This review considers the most up-to-date achievements related to the use of translatory potential of DNA and RNA in treatment of cardiovascular diseases, and considers the challenges and prospects in this field. The foundations which allow the use of translatory potential are also presented. The first part of this review focuses on the potential of the DNA variants which impact conventional therapies and on the DNA variants which are starting points for designing new pharmacotherapeutics. The second part of this review considers the translatory potential of non-coding RNA molecules which can be used to formulate new generations of therapeutics for CVDs.

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.