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Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understanding of multi-omics seems to provide a resource for integration in interdisciplinary biology since they altogether can draw the comprehensive picture of an organism's developmental and disease biology in cancers. Such large scale multi-omics data can be obtained from public consortium like The Cancer Genome Atlas (TCGA) and several other platforms. Integrating these multi-omics data from varied platforms is still challenging due to high noise and sensitivity of the platforms used. Currently, a robust integrative predictive model to estimate gene expression from these genetic and epigenetic data is lacking. In this study, we have developed a deep learning-based predictive model using Deep Denoising Auto-encoder (DDAE) and Multi-layer Perceptron (MLP) that can quantitatively capture how genetic and epigenetic alterations correlate with directionality of gene expression for liver hepatocellular carcinoma (LIHC). The DDAE used in the study has been trained to extract significant features from the input omics data to estimate the gene expression. These features have then been used for back-propagation learning by the multilayer perceptron for the task of regression and classification. We have benchmarked the proposed model against state-of-the-art regression models. Finally, the deep learning-based integration model has been evaluated for its disease classification capability, where an accuracy of 95.1% has been obtained.

The research on heterometallic 3d-4f polyoxometalates has made immense progress over the last decade with a larger number of structures being reported every year until the present day. The formation of 3d-4f POMs has been considered a challenge as both the 3d and 4f ions compete for coordination to the lacunary POMs ligands. It was seen that mostly amorphous precipitate was obtained instead of crystalline material. Hence, as a result, the synthetic parameters such as pH, temperature, solvent, time of reaction has to be precisely controlled in order to isolate the desired product. This review focuses on the heterometallic 3d-4f polyoxometalates reported in the last decade mainly after 2011, when the field of 3d-4f heterometallic polyoxometalates was considered an incipient field. The structural variety exhibited by this kind is diverse with a large number of structures including dimers, trimers, tetramers which can be divided mainly into four types of polyanions. These are pure 3d-4f polyanions, 3d substituted anions with 4f linkers, 4f substituted anions with 3d linkers, and POMs with both 3d and 4f as linkers. The reason for the desire to create such complexes is mainly due to the interesting magnetic properties exhibited by them. Introduction of 4f ions into the paramagnetic 3d cluster enhances the already present magnetic properties and allows the formation of single-molecule/ion magnets (SMMs or SIMs). Many of the complexes show slow magnetic relaxation which is ideal for SMMs/SIMs. A few of the complexes displaying such properties are discussed in the review. Apart from that, the catalytic properties exhibited by such 3d-4f complexes have also been discussed.