In recent years, the advent of new experimental methodologies for studying the high complexity of the human genome and proteome has led to the generation of an increasing amount of digital information, hence bioinformatics, which harnesses computer science, biology, and chemistry, playing a mandatory role for the analysis of the produced datasets. The emerging technology of Artificial Intelligence (AI), including Machine Learning (ML) and Artificial Neural Networks (ANNs), is nowadays at the core of biomedical research and has already paved the way for significant breakthroughs in both biological and medical sciences. AI and computer science have transformed traditional medicine into modern biomedicine, thus promising a new era in systems biology that will enhance drug discovery strategies and facilitate clinical practice. The current review defines the main categories of AI and thoroughly describes the fundamental principles of the widely used ML, ANNs and DL approaches. Furthermore, we aim to underline the determinant role of AI-based methods in various biological research fields, such as proteomics and drug design techniques, and finally, investigate the implication of AI in everyday clinical practice and healthcare systems. Finally, this review also highlights the challenges and future directions of AI in Modern Biomedical study.

Deciphering cancer etiopathogenesis has proven to be an especially challenging task since the mechanisms that drive tumor development and progression are far from simple. An astonishing amount of research has revealed a wide spectrum of defects, including genomic abnormalities, epigenomic alterations, disturbance of gene transcription, as well as post-translational protein modifications, which cooperatively promote carcinogenesis. These findings suggest that the adoption of a multidimensional approach can provide a much more precise and comprehensive picture of the tumor landscape, hence serving as a powerful tool in cancer research and precision oncology. The introduction of next- and third-generation sequencing technologies paved the way for the decoding of genetic information and the elucidation of cancer-related cellular compounds and mechanisms. In the present review, we discuss the current and emerging applications of both generations of sequencing technologies, also referred to as massive parallel sequencing (MPS), in the fields of cancer genomics, transcriptomics and proteomics, as well as in the progressing realms of epi-omics. Finally, we provide a brief insight into the expanding scope of sequencing applications in personalized cancer medicine and pharmacogenomics.

Although a plethora of DNA modifications have been extensively investigated in the last decade, recent breakthroughs in molecular biology, including high throughput sequencing techniques, have enabled the identification of post-transcriptional marks that decorate RNAs; hence, epitranscriptomics has arisen. This recent scientific field aims to decode the regulatory layer of the transcriptome and set the ground for the detection of modifications in ribose nucleotides. Until now, more than 170 RNA modifications have been reported in diverse types of RNA that contribute to various biological processes, such as RNA biogenesis, stability, and transcriptional and translational accuracy. However, dysfunctions in the RNA-modifying enzymes that regulate their dynamic level can lead to human diseases and cancer. The present review aims to highlight the epitranscriptomic landscape in human RNAs and match the catalytic proteins with the deposition or deletion of a specific mark. In the current review, the most abundant RNA modifications, such as N6-methyladenosine (m6A), N5-methylcytosine (m5C), pseudouridine (Ψ) and inosine (I), are thoroughly described, their functional and regulatory roles are discussed and their contributions to cellular homeostasis are stated. Ultimately, the involvement of the RNA modifications and their writers, erasers, and readers in human diseases and cancer is also discussed.

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Breast Cancer Gene 1 (BRCA1) is a tumour suppressor protein that modulates multiple biological processes including genomic stability and DNA damage repair. Although the main BRCA1 protein is well characterized, further proteomics studies have already identified additional BRCA1 isoforms with lower molecular weights. However, the accurate nucleotide sequence determination of their corresponding mRNAs is still a barrier, mainly due to the increased mRNA length of BRCA1 (~5.5 kb) and the limitations of the already implemented sequencing approaches. In the present study, we designed and employed a multiplexed hybrid sequencing approach (Hybrid-seq), based on nanopore and semi-conductor sequencing, aiming to detect BRCA1 alternative transcripts in a panel of human cancer and non-cancerous cell lines. The implementation of the described Hybrid-seq approach led to the generation of highly accurate long sequencing reads that enabled the identification of a wide spectrum of BRCA1 splice variants ( BRCA1 sv.7 – sv.52), thus deciphering the transcriptional landscape of the human BRCA1 gene. In addition, demultiplexing of the sequencing data unveiled the expression profile and abundance of the described BRCA1 mRNAs in breast, ovarian, prostate, colorectal, lung and brain cancer as well as in non-cancerous human cell lines. Finally, in silico analysis supports that multiple detected mRNAs harbour open reading frames, being highly expected to encode putative protein isoforms with conserved domains, thus providing new insights into the complex roles of BRCA1 in genomic stability and DNA damage repair.