In the current state of genomics and biomedical research the utilization of Artificial Intelligence (AI) Machine Learning (ML) and Deep Learning (DL) have emerged as paradigm shifters. While traditional NGS DNA and RNA sequencing analysis pipelines have been sound in decoding genetic information the sequencing data's volume and complexity have surged. There is a demand for more efficient and accurate methods of analysis. This has led to de Read More

Background: PIWI-interacting RNAs (piRNAs) and circular RNAs (circRNAs) are two kinds of non-coding RNAs (ncRNAs) that play important roles in epigenetic regulation transcriptional regulation post-transcriptional regulation of many biological processes. Although there exist various resources it is still challenging to select such resources for specific research projects on ncRNAs. Methods: In order to facilitate researchers in finding the appropriate bioinforma Read More

Introduction: More recent self-supervised deep language models such as Bidirectional Encoder Representations from Transformers (BERT) have performed the best on some language tasks by contextualizing word embeddings for a better dynamic representation. Their proteinspecific versions such as ProtBERT generated dynamic protein sequence embeddings which resulted in better performance for several bioinformatics tasks. Besides a number of diff Read More

Background: With new variants of COVID-19 causing challenges we need to focus on integrating multiple deep-learning frameworks to develop intelligent healthcare systems for early detection and diagnosis. Objective: This article suggests three hybrid deep learning models namely CNN-LSTM CNN-Bi- LSTM and CNN-GRU to address the pressing need for an intelligent healthcare system. These models are designed to capture spatial and temporal patterns i Read More

Background: This study addresses the need for analyzing viral genome sequences and understanding their genetic relationships. The focus is on introducing a novel natural graph approach as a solution. Objective: The objective of this study is to demonstrate the effectiveness and advantages of the proposed natural graph approach in clustering viral genome sequences into distinct clades subtypes or districts. Additionally the aim is to explore its interpretability Read More

Background: Comparing directed networks using the alignment-free technique offers the advantage of detecting topologically similar regions that are independent of the network size or node identity. Objective: We propose a novel method to compare directed networks by decomposing the network into small modules the so-called network subgraph approach which is distinct from the network motif approach because it does not depend on null mo Read More

In the era of genomics fueled by advanced technologies and analytical tools metabolomics has become a vital component in biomedical research. Its significance spans various domains encompassing biomarker identification uncovering underlying mechanisms and pathways as well as the exploration of new drug targets and precision medicine. This article presents a comprehensive overview of the latest developments in metabolomics techniques Read More

Background: Microbe-disease associations are integral to understanding complex diseases and their screening procedures. Objective: While numerous computational methods have been developed to detect these associations their performance remains limited due to inadequate utilization of weighted inherent similarities and microbial taxonomy hierarchy. To address this limitation we have introduced WTHMDA (weighted taxonomic heterogeneo Read More

Background: One of the problems in drug discovery that can be solved by artificial intelligence is toxicity prediction. In drug-induced immune thrombocytopenia toxicity can arise in patients after five to ten days by significant bleeding caused by drugdependent antibodies. In clinical trials when this condition occurs all the drugs consumed by patients should be stopped although sometimes this is not possible especially for older patients who ar Read More