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

Involvement of genes and non-coding RNAs in cancer: profiling using microarrays

MicroRNAs (miRNAs) are small noncoding RNAs (ncRNAs, RNAs that do not code for proteins) that regulate the expression of target genes. MiRNAs can act as tumor suppressor genes or oncogenes in human cancers. Moreover, a large fraction of genomic ultraconserved regions (UCRs) encode a particular set of ncRNAs whose expression is altered in human cancers. Bioinformatics studies are emerging as important tools to identify associations between miRNAs/ncRNAs and CAGRs (Cancer Associated Genomic Regions). ncRNA profiling, the use of highly parallel devices like microarrays for expression, public resources like mapping, expression, functional databases, and prediction algorithms have allowed the identification of specific signatures associated with diagnosis, prognosis and response to treatment of human tumors

Joint learning from multiple information sources for biological problems

Thanks to technological advancements, more and more biological data havebeen generated in recent years. Data availability offers unprecedented opportunities to look at the same problem from multiple aspects. It also unveils a more global view of the problem that takes into account the intricated inter-play between the involved molecules/entities. Nevertheless, biological datasets are biased, limited in quantity, and contain many false-positive samples. Such challenges often drastically downgrade the performance of a predictive model on unseen data and, thus, limit its applicability in real biological studies. Human learning is a multi-stage process in which we usually start with simple things. Through the accumulated knowledge over time, our cognition ability extends to more complex concepts. Children learn to speak simple words before being able to formulate sentences. Similarly, being able to speak correct sentences supports our learning to speak correct and meaningful paragraphs, etc. Generally, knowledge acquired from related learning tasks would help boost our learning capability in the current task. Motivated by such a phenomenon, in this thesis, we study supervised machine learning models for bioinformatics problems that can improve their performance through exploiting multiple related knowledge sources. More specifically, we concern with ways to enrich the supervised models’ knowledge base with publicly available related data to enhance the computational models’ prediction performance. Our work shares commonality with existing works in multimodal learning, multi-task learning, and transfer learning. Nevertheless, there are certain differences in some cases. Besides the proposed architectures, we present large-scale experiment setups with consensus evaluation metrics along with the creation and release of large datasets to showcase our approaches’ superiority. Moreover, we add case studies with detailed analyses in which we place no simplified assumptions to demonstrate the systems’ utilities in realistic application scenarios. Finally, we develop and make available an easy-to-use website for non-expert users to query the model’s generated prediction results to facilitate field experts’ assessments and adaptation. We believe that our work serves as one of the first steps in bridging the gap between “Computer Science” and “Biology” that will open a new era of fruitful collaboration between computer scientists and biological field experts

miRConnect: Identifying Effector Genes of miRNAs and miRNA Families in Cancer Cells

micro(mi)RNAs are small non-coding RNAs that negatively regulate expression of most mRNAs. They are powerful regulators of various differentiation stages, and the expression of genes that either negatively or positively correlate with expressed miRNAs is expected to hold information on the biological state of the cell and, hence, of the function of the expressed miRNAs. We have compared the large amount of available gene array data on the steady state system of the NCI60 cell lines to two different data sets containing information on the expression of 583 individual miRNAs. In addition, we have generated custom data sets containing expression information of 54 miRNA families sharing the same seed match. We have developed a novel strategy for correlating miRNAs with individual genes based on a summed Pearson Correlation Coefficient (sPCC) that mimics an in silico titration experiment. By focusing on the genes that correlate with the expression of miRNAs without necessarily being direct targets of miRNAs, we have clustered miRNAs into different functional groups. This has resulted in the identification of three novel miRNAs that are linked to the epithelial-to-mesenchymal transition (EMT) in addition to the known EMT regulators of the miR-200 miRNA family. In addition, an analysis of gene signatures associated with EMT, c-MYC activity, and ribosomal protein gene expression allowed us to assign different activities to each of the functional clusters of miRNAs. All correlation data are available via a web interface that allows investigators to identify genes whose expression correlates with the expression of single miRNAs or entire miRNA families. miRConnect.org will aid in identifying pathways regulated by miRNAs without requiring specific knowledge of miRNA targets

Multi-project and Multi-profile joint Non-negative Matrix Factorization for cancer omic datasets

Abstract Motivation The integration of multi-omic data using machine learning methods has been focused on solving relevant tasks such as predicting sensitivity to a drug or subtyping patients. Recent integration methods, such as joint Non-negative Matrix Factorization, have allowed researchers to exploit the information in the data to unravel the biological processes of multi-omic datasets. Results We present a novel method called Multi-project and Multi-profile joint Non-negative Matrix Factorization capable of integrating data from different sources, such as experimental and observational multi-omic data. The method can generate co-clusters between observations, predict profiles and relate latent variables. We applied the method to integrate low-grade glioma omic profiles from The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia projects. The method allowed us to find gene clusters mainly enriched in cancer-associated terms. We identified groups of patients and cell lines similar to each other by comparing biological processes. We predicted the drug profile for patients, and we identified genetic signatures for resistant and sensitive tumors to a specific drug.This work has been supported by the Ministry of Science, Technology and Innovation of Colombia grant No. 785. The European Research Council (ERC) Consolidator Grant 770827 and the Spanish State Research Agency AEI 10.13039/501100011033 grant number PID2019-105500GB-I00.Peer ReviewedPostprint (author's final draft

RNA structure analysis : algorithms and applications

In this doctoral thesis, efficient algorithms for aligning RNA secondary structures and mining unknown RNA motifs are presented. As the major contribution, a structure alignment algorithm, which combines both primary and secondary structure information, can find the optimal alignment between two given structures where one of them could be either a pattern structure of a known motif or a real query structure and the other be a subject structure. Motivated by widely used algorithms for RNA folding, the proposed algorithm decomposes an RNA secondary structure into a set of atomic structural components that can be further organized in a tree model to capture the structural particularities. The novel structure alignment algorithm is implemented using dynamic programming techniques coupled by position-independent scoring matrices. The algorithm can find the optimal global and local alignments between two RNA secondary structures at quadratic time complexity. When applied to searching a structure database, the algorithm can find similar RNA substructures and therefore can be used to identify functional RNA motifs. Extension of the algorithm has also been accomplished to deal with position-dependent scoring matrix in the purpose of aligning multiple structures. All algorithms have been implemented in a package under the name RSmatch and applied to searching mRNA UTR structure database and mining RNA motifs. The experimental results showed high efficiency and effectiveness of the proposed techniques