Identifying the potential miRNA biomarkers based on multi-view networks and reinforcement learning for diseases

Comprehensive review of reinforcement learning in lung cancer diagnosis and treatment: Taxonomy, challenges and recommendations

Lung cancer (LuC) is one of the leading causes of death in the world, and due to the complex mechanisms and widespread metastasis, diagnosis and treatment are challenging. In recent years, the application of reinforcement learning (RL) techniques as a new tool to improve LuC diagnosis and treatment has been dramatically expanded. These techniques can potentially increase the accuracy of diagnosis and optimize treatment processes by learning from limited data and improving clinical decisions. However, RL in LuC diagnosis and treatment faces challenges such as limited access to clinical data, the complexity of algorithms, and the need for technical expertise for proper implementation. Our systematic review article aims to evaluate the latest developments in applications and challenges of using RL techniques in LuC diagnosis and treatment. The findings showed that RL has increased the accuracy of identifying disease trends by 37 % and enhancing treatment decisions by 23 %. Also, using this approach reduces data processing time by 17 % and streamlining treatment processes by 12 %. Ultimately, analyzing the current challenges and offering recommendations to researchers could help develop new strategies for improving the diagnosis and treatment of LuC.

Machine learning for catalysing the integration of noncoding RNA in research and clinical practice

The human transcriptome predominantly consists of noncoding RNAs (ncRNAs), transcripts that do not encode proteins. The noncoding transcriptome governs a multitude of pathophysiological processes, offering a rich source of next-generation biomarkers. Toward achieving a holistic view of disease, the integration of these transcripts with clinical records and additional data from omic technologies ("multiomic" strategies) has motivated the adoption of artificial intelligence (AI) approaches. Given their intricate biological complexity, machine learning (ML) techniques are becoming a key component of ncRNA-based research. This article presents an overview of the potential and challenges associated with employing AI/ML-driven approaches to identify clinically relevant ncRNA biomarkers and to decipher ncRNA-associated pathogenetic mechanisms. Methodological and conceptual constraints are discussed, along with an exploration of ethical considerations inherent to AI applications for healthcare and research. The ultimate goal is to provide a comprehensive examination of the multifaceted landscape of this innovative field and its clinical implications.

Analyzing omics data based on sample network

Identifying valuable features from complex omics data is of great significance for disease diagnosis study. This paper proposes a new feature selection algorithm based on sample network (FS-SN) to mine important information from omics data. The sample network is constructed according to the sample neighbor relationship at the molecular (feature) expression level, and the distinguishing ability of the feature is evaluated based on the topology of the sample network. The sample network established on a feature with a strong discriminating ability tends to have many edges between the same group samples and few edges between the different group samples. At the same time, FS-SN removes redundant features according to the gravitational interaction between features. To show the validation of FS-SN, it was compared on ten public datasets with ERGS, mRMR, ReliefF, ATSD-DN, and INDEED which are efficient in omics data analysis. Experimental results show that FS-SN performed better than the compared methods in accuracy, sensitivity and specificity in most cases. Hence, FS-SN making use of the topology of the sample network is effective for analyzing omics data, it can identify key features that reflect the occurrence and development of diseases, and reveal the underlying biological mechanism.