Figure - uploaded by Matteo Cereda
Content may be subject to copyright.
The table reports all learning approaches reported in the main text with respect to each section.

The table reports all learning approaches reported in the main text with respect to each section.

Source publication
Article
Full-text available
Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer....

Context in source publication

Context 1
... we explore the tight link between these approaches and learning strategies. All methods that we report are listed in Table 2 and summarized in Figure 3. Figure 3. Graphical summary of AI approaches (columns) applied to solve tasks (rows) presented in this review. Cells show the RNA-seq data type used for the analysis. ...

Similar publications

Article
Full-text available
Ovarian cancer is one of the leading causes of deaths among patients with gynecological malignancies worldwide. In order to identify prognostic markers for ovarian cancer, we performed RNA-sequencing and analyzed the transcriptome data from 51 patients who received conventional therapies for high-grade serous ovarian carcinoma (HGSC). Patients with...
Preprint
Full-text available
Renal cell carcinoma (RCC) is one of the most prevalent cancers. Long noncoding RNAs (LncRNAs) have been indicated as a mediator acted in tumorigenesis of RCC. However, the mechanism of LINC00460 on RCC is yet to be investigated. This study aimed to investigate the potential function of LINC00460 and underlying mechanism of RCC. We detected LINC004...

Citations

... Auslander et al. reviewed machine learning/deep learning approaches incorporated to establish bioinformatics and computational biology frameworks in the areas of molecular evolution, protein structure analysis, systems biology, and disease genomics [19]. Del Giudice et al. comprehensively reviewed machine learning/deep learning solutions for computational problems in bulk and single-cell RNA-sequencing data analysis [20]. Banegas-Luna et al. discussed the interpretability of machine learning/deep learning methods in cancer research [21]. ...
Article
Full-text available
In recent years, deep learning has emerged as a highly active research field, achieving great success in various machine learning areas, including image processing, speech recognition, and natural language processing, and now rapidly becoming a dominant tool in biomedicine [...]
... If linear separation is not applicable, the kernel approach can be applied to transform the training samples into a high-dimensional space. A separator is then used in learning to this space [42]. It stands out amongst other known classification approaches based on computational circumstances over their opponents. ...
... SVMs control non-linear decision margins of unpredictable intricacy. Linear SVMs are used for specific linear discriminant classifications [42]. Linear SVM applies as a maximum margin classifier when the datasets are linearly distinguishable. ...
Article
Background The world has been battling the continuous COVID-19 pandemic spread by the SARS-CoV-2 virus for last two years. The issue of viral disease prediction is constantly a matter of interest in virology and the study of disease transmission over the long years. Objective In this study, we aimed to implement genome association studies using RNA-Seq of COVID-19 and reveal highly expressed gene biomarkers and prediction based on the machine learning model of COVID-19 analysis to combat this pandemic. Method We collected RNA-Seq gene count data for both healthy (Control) and non-healthy (Treated) COVID-19 cases. In this experiment, a sequence of bioinformatics strategies and statistical techniques, such as fold-change and adjusted p-value, were processed to identify differentially expressed genes (DEGs). We filtered biomarker sets of high DEGs, moderate DEGs, and low DEGs using DESeq2, Limma Trend, and Limma Voom methods based on intersection and union operations and applied machine learning techniques to predict COVID-19. Result Through experimental analysis, 67 potential biomarkers were extracted, comprising 49 up-regulated and 18 down-regulated genes, using statistical techniques and a set-theory consensus strategy. We trained the machine learning models on 12 different biomarker sets and found that the SVM model performed better than the other classifiers with 99.07% classification accuracy for moderate DEGs. Conclusion Our study revealed that identified differentially expressed genes of the moderate DEGs biomarker set, |log2FC| ≥ 2 with adjusted p-value < 0.05, work significantly as input features to implement a machine learning model using a kernel-based SVM technique to predict COVID-19.
... AI approaches are commonly used to solve regression, classification, dimensionality reduction, and clustering tasks. AI algorithms can be employed to capture more detailed information on cell types, DEGs, biomarker expression patterns, lineage transition, and disease subtypes, as well as to predict clinical outcomes [83]. AI-enabled analysis of scRNA-Seq data, along with the visualisation of landmark genes, enables us to uncover the "where" for every "what", and offers a holistic understanding of gene expression at a single-cell resolution within a tissue microenvironment. ...
Article
Full-text available
Since the time when detection of gene expression in single cells by microarrays to the Next Generation Sequencing (NGS) enabled Single Cell Genomics (SCG), it has played a pivotal role to understand and elucidate the functional role of cellular heterogeneity. Along this journey to becoming a key player in the capture of the individuality of cells, SCG overcame many milestones, including scale, speed, sensitivity and sample costs (4S). There have been many important experimental and computational innovations in the efficient analysis and interpretation of SCG data. The increasing role of AI in SCG data analysis has further enhanced its applicability in building models for clinical intervention. Furthermore, SCG has been instrumental in the delineation of the role of cellular heterogeneity in specific diseases, including cancer and infectious diseases. The understanding of the role of differential immune responses in driving coronavirus disease-2019 (COVID-19) disease severity and clinical outcomes has been greatly aided by SCG. With many variants of concern (VOC) in sight, it would be of great importance to further understand the immune response specificity vis-a-vis the immune cell repertoire, the identification of novel cell types, and antibody response. Given the potential of SCG to play an integral part in the multi-omics approach to the study of the host–pathogen interaction and its outcomes, our review attempts to highlight its strengths, its implications for infectious disease biology, and its current limitations. We conclude that the application of SCG would be a critical step towards future pandemic preparedness.
Chapter
Precision oncology is a novel research field and approach to cancer care which leverages high-throughput sequencing technologies and bioinformatics pipelines to determine diagnosis, prognosis, and treatment of patients in a personalized manner. This chapter provides an overview of a typical precision oncology software platform, from raw data to patient reports. Standard and advanced analytical components are described and discussed, along with their strengths and limitations, in general and in the context of a precision oncology application for advanced cancer patients.
Preprint
Full-text available
A review of over 4000+ articles published in 2021 related to artificial intelligence in healthcare.A BrainX Community exclusive, annual publication which has trends, specialist editorials and categorized references readily available to provide insights into related 2021 publications. Cite as: Mathur P, Mishra S, Awasthi R, Cywinski J, et al. (2022). Artificial Intelligence in Healthcare: 2021 Year in Review. DOI: 10.13140/RG.2.2.25350.24645/1