Computational approaches leveraging integrated connections of multi-omic data toward clinical applications

In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing cellular activity. The complexity of the biological processes arises from the interactions between omic entities such as genes, proteins, and metabolites. Therefore, multi-omic data integration is crucial but challenging. The impact of the molecular alterations in multi-omic data is not local in the neighborhood of the altered gene or protein; rather, the impact diffuses in the network and changes the functionality of multiple signaling pathways and regulation of the gene expression. Additionally, multi-omic data is high-dimensional and has background noise. Several integrative approaches have been developed to accurately interpret the multi-omic datasets, including machine learning, network-based methods, and their combination. In this review, we overview the most recent integrative approaches and tools with a focus on network-based methods. We then discuss these approaches according to their specific applications, from disease-network and biomarker identification to patient stratification, drug discovery, and repurposing.


Automated biological data acquisition and integration using machine learning techniques
Çarkacıoğlu, Levent; Atalay, Mehmet Volkan; Department of Computer Engineering (2009)
Since the initial genome sequencing projects along with the recent advances on technology, molecular biology and large scale transcriptome analysis result in data accumulation at a large scale. These data have been provided in different platforms and come from different laboratories therefore, there is a need for compilation and comprehensive analysis. In this thesis, we addressed the automatization of biological data acquisition and integration from these non-uniform data using machine learning techniques....
Comparative statistical microarray analysis of yeast data under heat shock stress
Varol, Duygu; Purutçuoğlu Gazi, Vilda; Yılmaz, Remziye; Department of Statistics (2014)
The microarray technology is one of the widely used experimental methods in biological and biochemical sciences. By this innovation, a number of genes can be analyzed simultaneously by means of statistical methods. Hereby in this study we analyze a new one-channel microarray dataset that is measured to investigate the changes in heat shock stress of yeast. The data that are generated in the Molecular Biology and Biotechnology R-D Center at the Middle East Technical University has not been evaluated yet in d...
Inference of Gene Regulatory Networks Via Multiple Data Sources and a Recommendation Method
Ozsoy, Makbule Gulcin; Polat, Faruk; Alhajj, Reda (2015-11-12)
Gene regulatory networks (GRNs) are composed of biological components, including genes, proteins and metabolites, and their interactions. In general, computational methods are used to infer the connections among these components. However, computational methods should take into account the general features of the GRNs, which are sparseness, scale-free topology, modularity and structure of the inferred networks. In this work, observing the common aspects between recommendation systems and GRNs, we decided to ...
Using Adaptive Neuro-Fuzzy Inference System for Classification of Microarray Gene Expression Cancer Profiles
Haznedar, Bülent; Arslan, Mustafa Turan; Kalınlı, Adem (2018-05-01)
Microarray is a technology that enables simultaneously analysis of thousands of genes in DNA structure depending on the advances in biochemistry. With this technology, it has become possible to diagnose and treat heredity diseases by analyzing thousands of gene expression levels. This study proposes an artificial intelligence method, Adaptive neuro-fuzzy inference system (ANFIS), to classify cancer gene expression profiles. The findings obtained with the proposed ANFIS approach are compared with the results...
Evaluating the effects of rescaling parameters in large-scale genomic simulations
Kıratlı, Ozan; Birand Özsoy, Ayşegül Ceren; Department of Biology (2016)
Computer simulations are widely used in many subdisciplines of biological sciences, which evolutionary biology. Large-scale genomic simulations, where several kb (kilo base) to several Mb (megabase) genomes are modeled, are being increasingly used. These simulations require high computing power. There are some methods proposed in the literature to decrease the time and memory demand of these simulations. This study is concentrated on one of those methods, where both the number of generation, and the number ...
Citation Formats
H. C. Demirel and N. Tunçbağ, “Computational approaches leveraging integrated connections of multi-omic data toward clinical applications,” MOLECULAR OMICS, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: