Network-based approaches to explore complex biological systems towards network medicine

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes

mintRULS: Prediction of miRNA-mRNA Target Site Interactions Using Regularized Least Square Method

Identification of miRNA-mRNA interactions is critical to understand the new paradigms in gene regulation. Existing methods show suboptimal performance owing to inappropriate feature selection and limited integration of intuitive biological features of both miRNAs and mRNAs. The present regularized least square-based method, mintRULS, employs features of miRNAs and their target sites using pairwise similarity metrics based on free energy, sequence and repeat identities, and target site accessibility to predict miRNA-target site interactions. We hypothesized that miRNAs sharing similar structural and functional features are more likely to target the same mRNA, and conversely, mRNAs with similar features can be targeted by the same miRNA. Our prediction model achieved an impressive AUC of 0.93 and 0.92 in LOOCV and LmiTOCV settings, respectively. In comparison, other popular tools such as miRDB, TargetScan, MBSTAR, RPmirDIP, and STarMir scored AUCs at 0.73, 0.77, 0.55, 0.84, and 0.67, respectively, in LOOCV setting. Similarly, mintRULS outperformed other methods using metrics such as accuracy, sensitivity, specificity, and MCC. Our method also demonstrated high accuracy when validated against experimentally derived data from condition- and cell-specific studies and expression studies of miRNAs and target genes, both in human and mouse

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

Nucleotide Complementarity Features in the Design of Effective Artificial miRNAs

L'importance du miARN dans la régulation des gènes a bien été établie. Cependant, le mécanisme précis du processus de reconnaissance des cibles n'est toujours pas complètement compris. Parmi les facteurs connus, la complémentarité en nucléotides, l'accessibilité des sites cibles, la concentration en espèces d'ARN et la coopérativité des sites ont été jugées importantes. En utilisant ces règles connues, nous avons précédemment conçu des miARN artificiels qui inhibent la croissance des cellules cancéreuses en réprimant l'expression de plusieurs gènes. De telles séquences guides ont été délivrées dans les cellules sous forme de shARN. Le VIH étant un virus à ARN, nous avons conçu et testé des ARN guides qui inhibent sa réplication en ciblant directement le génome viral et les facteurs cellulaires nécessaires au virus dans le cadre de mon premier projet. En utilisant une version mise à jour du programme de conception, mirBooking, nous devenons capables de prédire l'effet de concentration des espèces à ARN avec plus de précision. Les séquences guides conçues fournissaient aux cellules une résistance efficace à l'infection virale, égale ou meilleure que celles ciblant directement le génome viral par une complémentarité quasi-parfaite. Cependant, les niveaux de répression des facteurs viraux et cellulaires ne pouvaient pas être prédits avec précision. Afin de mieux comprendre les règles de reconnaissance des cibles miARN, les règles de couplage des bases au-delà du « seed » ont été approfondies dans mon deuxième projet. En concevant des séquences guides correspondant partiellement à la cible et en analysant le schéma de répression, nous avons établi un modèle unificateur de reconnaissance de cible par miARN via la protéine Ago2. Il montre qu'une fois que le « seed » est appariée avec l'ARN cible, la formation d'un duplex d'ARN est interrompue au niveau de la partie centrale du brin guide mais reprend plus loin en aval de la partie centrale en suivant un ordre distinct. L'implémentation des règles découvertes dans un programme informatique, MicroAlign, a permis d'améliorer la conception de miARN artificiels efficaces. Dans cette étude, nous avons non seulement confirmé la contribution des nucléotides non-germes à l'efficacité des miARN, mais également défini de manière quantitative la manière dont ils fonctionnent. Le point de vue actuellement répandu selon lequel les miARN peuvent cibler efficacement tous les gènes de manière égale, avec uniquement des correspondances de semences, peut nécessiter un réexamenThe importance of miRNA in gene regulation has been well established; however, the precise mechanism of its target recognition process is still not completely understood. Among the known factors, nucleotide complementarity, accessibility of the target sites, and the concentration of the RNA species, and site cooperativity were deemed important. Using these known rules, we previously designed artificial miRNAs that inhibit cancer cell growth by repressing the expression of multiple genes. Such guide sequences were delivered into the cells in the form of shRNAs. HIV is an RNA virus. We designed and tested guide RNAs that inhibit its replication by directly targeting the viral genome and cellular factors that the virus requires in my first project. Using an updated version of the design program, mirBooking, we become capable to predict the concentration effect of RNA species more accurately. Designed guide sequences provided cells with effective resistance against viral infection. The protection was equal or better than those that target the viral genome directly via near-perfect complementarity. However, the repression levels of the viral and cellular factors could not be precisely predicted. In order to gain further insights on the rules of miRNA target recognition, the rules of base pairing beyond the seed was further investigated in my second project. By designing guide sequences that partially match the target and analysing the repression pattern, we established a unifying model of miRNA target recognition via Ago2 protein. It shows that once the seed is base-paired with the target RNA, the formation of an RNA duplex is interrupted at the central portion of the guide strand but resumes further downstream of the central portion following a distinct order. The implementation of the discovered rules in a computer program, MicroAlign, enhanced the design of efficient artificial miRNAs. In this study, we not only confirmed the contribution of non-seed nucleotides to the efficiency of miRNAs, but also quantitatively defined the way through which they work. The currently popular view that miRNAs can effectively target all genes equally with only seed matches may require careful re-examination