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A fundamental question in precision medicine is to quantitatively decode the genetic basis of complex human diseases, which will enable the development of predictive models of disease risks based on personal genome sequences. To account for the complex systems within different cellular contexts, large-scale regulatory networks are critical components to be integrated into the analysis. Based on the fast accumulation of multiomics and disease genetics data, advanced machine learning algorithms and efficient computational tools are becoming the driving force in predicting phenotypes from genotypes, identifying potential causal genetic variants, and revealing disease mechanisms. Here, we review the state-of-the-art methods for this topic and describe a computational pipeline that assembles a series of algorithms together to achieve improved disease genetics prediction through the delineation of regulatory circuitry step by step.With rapid advances in experimental instruments and protocols, imaging and sequencing data are being generated at an unprecedented rate contributing significantly to the current and coming big biomedical data. Meanwhile, unprecedented advances in computational infrastructure and analysis algorithms are realizing image-based digital diagnosis not only in radiology and cardiology but also oncology and other diseases. Machine learning methods, especially deep learning techniques, are already and broadly implemented in diverse technological and industrial sectors, but their applications in healthcare are just starting. Uniquely in biomedical research, a vast potential exists to integrate genomics data with histopathological imaging data. The integration has the potential to extend the pathologist's limits and boundaries, which may create breakthroughs in diagnosis, treatment, and monitoring at molecular and tissue levels. Moreover, the applications of genomics data are realizing the potential for personalized medicine, making diagnosis, treatment, monitoring, and prognosis more accurate. In this chapter, we discuss machine learning methods readily available for digital pathology applications, new prospects of integrating spatial genomics data on tissues with tissue morphology, and frontier approaches to combining genomics data with pathological imaging data. We present perspectives on how artificial intelligence can be synergized with molecular genomics and imaging to make breakthroughs in biomedical and translational research for computer-aided applications.Cancer produces complex cellular changes. Microarrays have become crucial to identifying genes involved in causing these changes; however, microarray data analysis is challenged by the high-dimensionality of data compared to the number of samples. This has contributed to inconsistent cancer biomarkers from various gene expression studies. Also, identification of crucial genes in cancer can be expedited through expression profiling of peripheral blood cells. We introduce a novel feature selection method for microarrays involving a two-step filtering process to select a minimum set of genes with greater consistency and relevance, and demonstrate that the selected gene set considerably enhances the diagnostic accuracy of cancer. The preliminary filtering (Bi-biological filter) involves building gene coexpression networks for cancer and healthy conditions using a topological overlap matrix (TOM) and finding cancer specific gene clusters using Spectral Clustering (SC). This is followed by a filtering step to extract a much-reduced set of crucial genes using best first search with support vector machine (BFS-SVM). Finally, artificial neural networks, SVM, and K-nearest neighbor classifiers are used to assess the predictive power of the selected genes as well as to select the most effective diagnostic system. The approach was applied to peripheral blood profiling for breast cancer where Bi-biological filter selected 415 biologically consistent genes, from which BFS-SVM extracted 13 highly cancer specific genes for breast cancer identification. ANN was the superior classifier with 93.2% classification accuracy, a 14% improvement over the study from which data were obtained for this study (Aaroe et al., Breast Cancer Res 12R7, 2010).Biology has become a data driven science largely due to the technological advances that have generated large volumes of data. To extract meaningful information from these data sets requires the use of sophisticated modeling approaches. Toward that, artificial neural network (ANN) based modeling is increasingly playing a very important role. The "black box" nature of ANNs acts as a barrier in providing biological interpretation of the model. Here, the basic steps toward building models for biological systems and interpreting them using calliper randomization approach to capture complex information are described.While the term artificial intelligence and the concept of deep learning are not new, recent advances in high-performance computing, the availability of large annotated data sets required for training, and novel frameworks for implementing deep neural networks have led to an unprecedented acceleration of the field of molecular (network) biology and pharmacogenomics. The need to align biological data to innovative machine learning has stimulated developments in both data integration (fusion) and knowledge representation, in the form of heterogeneous, multiplex, and biological networks or graphs. In this chapter we briefly introduce several popular neural network architectures used in deep learning, namely, the fully connected deep neural network, recurrent neural network, convolutional neural network, and the autoencoder. Deep learning predictors, classifiers, and generators utilized in modern feature extraction may well assist interpretability and thus imbue AI tools with increased explication, potentially adding insights and advancements in novel chemistry and biology discovery.The capability of learning representations from structures directly without using any predefined structure descriptor is an important feature distinguishing deep learning from other machine learning methods and makes the traditional feature selection and reduction procedures unnecessary. Cilofexor clinical trial In this chapter we briefly show how these technologies are applied for data integration (fusion) and analysis in drug discovery research covering these areas (1) application of convolutional neural networks to predict ligand-protein interactions; (2) application of deep learning in compound property and activity prediction; (3) de novo design through deep learning. We also (1) discuss some aspects of future development of deep learning in drug discovery/chemistry; (2) provide references to published information; (3) provide recently advocated recommendations on using artificial intelligence and deep learning in -omics research and drug discovery.