For decades, great strides have been made in the field of immunometabolism. A plethora of evidence ranging from basic mechanisms to clinical transformation has gradually embarked on immunometabolism t Show more
For decades, great strides have been made in the field of immunometabolism. A plethora of evidence ranging from basic mechanisms to clinical transformation has gradually embarked on immunometabolism to the center stage of innate and adaptive immunomodulation. Given this, we focus on changes in immunometabolism, a converging series of biochemical events that alters immune cell function, propose the immune roles played by diversified metabolic derivatives and enzymes, emphasize the key metabolism-related checkpoints in distinct immune cell types, and discuss the ongoing and upcoming realities of clinical treatment. It is expected that future research will reduce the current limitations of immunotherapy and provide a positive hand in immune responses to exert a broader therapeutic role. Show less
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation Show more
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict. Here, one model is used for feature manipulation and the other one for classification. Using the first method FRnet-Encode, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-Predict, to identify interaction probability employing those features. We have tested our method on four gold standard datasets extensively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three out of four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores. The source codes and implementation details of our methods are available from https://github.com/farshidrayhanuiu/FRnet-DTI/ and also readily available to use as an web application from http://farshidrayhan.pythonanywhere.com/FRnet-DTI/ . Show less