Integrative Machine Learning Models for Discovery and Validation of Biological Knowledge

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The advent of next generation sequencing has revolutionized the way the genome, transcriptome and exome are studied, typically producing huge amounts of data to be analyzed. In this regard, machine learning has proposed a paramount of successful methods applied to knowledge discovery, retrieval and prediction of biological phenomena. Deep learning, in particular, has been successfully applied to extract knowledge in a wide range of applications in biology, medicine, data and network security, text mining, and computer vision, just to mention a few. There are, however, some challenges and limitations in the available data and the current approaches, as well as some obstacles yet to overcome, including lack of annotation of different variables, lack of large-scale and labelled training data, variety of variables, variations of formats across different datasets, and lack of sample-specificity in the knowledge extracted. This research program aims to develop integrative machine learning systems used to extract relevant biological knowledge from multiple variables, multiple datasets with large numbers of samples, and different biological indicators including "-omics", text and graphics from the literature. The integrative model will involve multiple datasets, multi-omics and multiple variables of different types of diseases. In the different sub-projects, we are planning to apply multi-modal, multi-task and transfer (deep and shallow) machine learning approaches that integrate different types of data. The approaches to be developed will integrate different schemes of representational deep leaning that utilize different forms of training such as adversary, convolutional and recurrent networks, with or without memory. Development of integrative machine learning approaches have not emerged significantly in multi-omics data, and hence, incorporating textual data, integrated with molecular measurements of different forms is a promising avenue for developing novel approaches, which can be then used in other fields as well, such as data security, networking and additive manufacturing, just to mention a few. The lack of reliable algorithms for integrating and disambiguating inconsistent data are crucial, as they are for missing variables - thus, integrative semi-supervised approaches are crucial in this regard. The methods to be developed will be used by other researchers in discovering biological knowledge from large datasets via sharing publications and a system that will be deployed in standard bioinformatics and open source platforms. In addition, it is expected that under this research program one postdoctoral fellow, three PhD students, and several Master's and undergraduate students will be trained, gaining key skills in big data analytics and software development of tools and platforms.