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The human Wnt signaling pathway contains 57 genes communicating among themselves by 70 experimentally established associations, as given in the KEGG/PATHWAY database. It is responsible for a variety of crucial biological functions such as regulation of cell fate determination, proliferation, differentiation, migration, and apoptosis. Abnormal behavior of its members causes numerous types of human cancers, dramatic changes in bone mass density that lead to diseases such as osteoporosis-pseudo-glioma syndrome, Van-Buchem disease, skeletal malformation, autosomal dominant sclerosteosis, and osteoporosis type I syndromes. So far, single genes have been investigated for their disease-causing properties, and single diseases have been traced backwards to discover foul-play of the system pathways. Differential expression of the whole genome has been mapped by microarray. But how all the genes involved in a pathway affect each other in single/multiple disease state(s) and whether the presence of one disease state makes a person prone to another kind of disease(s) (i.e., co-morbidity among diseases associated with a certain important biological pathway) is still unknown. We have developed a human Wnt signaling pathway diseasome and analyzed it for finding answers to such questions. Data used in constructing the diseasome can be downloaded from the publicly accessible webserver http://www.isical.ac.in/-rajat/diseasome/index.php.

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Artificial intelligence and the Internet of Things (IoT) have resulted in more computationally demanding and time-sensitive applications. Given the limited processing power of current mobile computers, there is a need for on-demand computing resources with minimal latency. Edge computing has already made a significant contribution to mobile networks, enabling the distribution, scaling, and faster access of computational resources at network margins closer to users, especially in power-constrained mobile devices. Offloading tasks efficiently on the Mobile Edge Computing Server (MECS) is an important part of our proposed method. We propose a method of offloading multiple tasks for Mobile Edge Computing servers that require fixed memory capacities and low latency. We calculate the optimum cumulative intrinsic profit of the number of offloaded tasks efficiently using the Ant Colony Optimization (ACO) model, which is flexible and versatile in the context of real-time applications.