Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection

Lopez-Rincon A, Martinez-Archundia M, Martinez-Ruiz GU, Schönhuth A, Tonda A. Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection. BMC Bioinformatics. 2019;20(1): 480

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Using gene and microRNA expression in the human airway for lung cancer diagnosis

Lung cancer surpasses all other causes of cancer-related deaths worldwide. Gene-expression microarrays have shown that differences in the cytologically normal bronchial airway can distinguish between patients with and without lung cancer. In research reported here, we have used microRNA expression in bronchial epithelium and gene expression in nasal epithelium to advance biological understanding of the lung-cancer "field of injury" and develop new biomarkers for lung cancer diagnosis. MicroRNAs are known to mediate the airway response to tobacco smoke exposure but their role in the lung-cancer-associated field of injury was previously unknown. Microarrays can measure microRNA expression; however, they are probe-based and limited to detecting annotated microRNAs. MicroRNA sequencing, on the other hand, allows the identification of novel microRNAs that may play important biological roles. We have used microRNA sequencing to discover novel microRNAs in the bronchial epithelium. One of the predicted microRNAs, now known as miR-4423, is associated with lung cancer and airway development. This finding demonstrates for the first time a microRNA expression change associated with the lung-cancer field of injury and microRNA mediation of gene expression changes within that field. The National Lung Screening Trial showed that screening high-risk smokers using CT scans decreases lung-cancer-associated mortality. Nodules were detected in over 20% of participants; however, the overwhelming majority of screening-detected nodules were non-malignant. We therefore need biomarkers to determine which screening-detected nodules are benign and do not require further invasive testing. Given that the lung-cancer-associated field of injury extends to the bronchial epithelium, our group hypothesized that the field of injury may extend farther up in the airway. Using gene expression microarrays, we have identified a nasal epithelium gene-expression signature associated with lung cancer. Using samples from the bronchial epithelium and the nasal epithelium, we have established that there is a common lung-cancer-associated gene-expression signature throughout the airway. In addition, we have developed a nasal epithelium gene-expression biomarker for lung cancer together with a clinico-genomic classifier that includes both clinical factors and gene expression. Our data suggests that gene expression profiling in nasal epithelium might serve as a non-invasive approach for lung cancer diagnosis and screenin

Machine learning-based ensemble recursive feature selection of circulating miRNAs for cancer tumor classification

Circulating microRNAs (miRNA) are small noncoding RNA molecules that can be detected in bodily fluids without the need for major invasive procedures on patients. miRNAs have shown great promise as biomarkers for tumors to both assess their presence and to predict their type and subtype. Recently, thanks to the availability of miRNAs datasets, machine learning techniques have been successfully applied to tumor classification. The results, however, are difficult to assess and interpret by medical experts because the algorithms exploit information from thousands of miRNAs. In this work, we propose a novel technique that aims at reducing the necessary information to the smallest possible set of circulating miRNAs. The dimensionality reduction achieved reflects a very important first step in a potential, clinically actionable, circulating miRNA-based precision medicine pipeline. While it is currently under discussion whether this first step can be taken, we demonstrate here that it is possible to perform classification tasks by exploiting a recursive feature elimination procedure that integrates a heterogeneous ensemble of high-quality, state-of-the-art classifiers on circulating miRNAs. Heterogeneous ensembles can compensate inherent biases of classifiers by using different classification algorithms. Selectin

Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

A miRNA-Target Prediction Case Study

Giansanti, V., Castelli, M., Beretta, S., & Merelli, I. (2019). Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study. In V. V. Krzhizhanovskaya, M. H. Lees, P. M. A. Sloot, J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, ... R. Lam (Eds.), Computational Science – ICCS 2019: 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part III (pp. 31-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11538 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22744-9_3MicroRNAs (miRNAs) are small non-coding RNAs with a key role in the post-transcriptional gene expression regularization, thanks to their ability to link with the target mRNA through the complementary base pairing mechanism. Given their role, it is important to identify their targets and, to this purpose, different tools were proposed to solve this problem. However, their results can be very different, so the community is now moving toward the deployment of integration tools, which should be able to perform better than the single ones. As Machine and Deep Learning algorithms are now in their popular years, we developed different classifiers from both areas to verify their ability to recognize possible miRNA-mRNA interactions and evaluated their performance, showing the potentialities and the limits that those algorithms have in this field. Here, we apply two deep learning classifiers and three different machine learning models to two different miRNA-mRNA datasets, of predictions from 3 different tools: TargetScan, miRanda, and RNAhybrid. Although an experimental validation of the results is needed to better confirm the predictions, deep learning techniques achieved the best performance when the evaluation scores are taken into account.authorsversionpublishe

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality