Predicting drug-target interactions with multi-label classification and label partitioning

Identifying drug-target interactions is crucial for drug discovery. Despite modern technologies used in drug screening, experimental identification of drug-target interactions is an extremely demanding task. Predicting drug-target interactions in silico can thereby facilitate drug discovery as well as drug repositioning. Various machine learning models have been developed over the years to predict such interactions. Multi-output learning models in particular have drawn the attention of the scientific community due to their high predictive performance and computational efficiency. These models are based on the assumption that all the labels are correlated with each other. However, this assumption is too optimistic. Here, we address drug-target interaction prediction as a multi-label classification task that is combined with label partitioning. We show that building multi-output learning models over groups (clusters) of labels often leads to superior results. The performed experiments confirm the efficiency of the proposed framework.status: Published onlin

Transferring Experience in Reinforcement Learning through Task Decomposition

Abstract Transfer learning refers to the process of conveying experience from a simple task to another more complex (and related) task in order to reduce the amount of time that is required to learn the latter task. Typically, in a transfer learning procedure the agent learns a behavior in a source task, and it uses the gained knowledge in order to speed up the learning process in a target task. Reinforcement Learning algorithms are time expensive when they learn from scratch, especially in complex domains, and transfer learning comprises a suitable solution to speed up the training process. In this work we propose a method that decomposes the target task in several instances of the source task and uses them to extract an adviced action for the target task. We evaluate the efficacy of the proposed approach in the robotic soccer Keepaway domain. The results demonstrate that the proposed method helps to reduce the training time of the target task

Local Imbalance based Ensemble for Predicting Interactions between Novel Drugs and Targets

status: Published onlin

Transferring Experience in Reinforcement Learning through Task Decomposition (Extended Abstract)

ABSTRACT Transfer learning refers to the process of conveying experience from a simple task to another more complex (and related) task in order to reduce the amount of time that is required to learn the latter task. Typically, in a transfer learning procedure the agent learns a behavior in a source task, and it uses the gained knowledge in order to speed up the learning process in a target task. Reinforcement Learning algorithms are time expensive when they learn from scratch, especially in complex domains, and transfer learning comprises a suitable solution to speed up the training process. In this work we propose a method that decomposes the target task in several instances of the source task and uses them to extract an advised action for the target task. We evaluate the efficacy of the proposed approach in the robotic soccer Keepaway domain. The results demonstrate that the proposed method helps to reduce the training time of the target task

Hepatitis B markers and vaccination-induced protection rate among Albanian pregnant women in Greece

Hepatitis B has long been a serious public health problem both in Greece and in Albania. In the February 2009 issue of World Journal of Gastroenterology, Resuli et al presented the interesting epidemiological data concerning hepatitis B virus infection in Albania. The results of this study were discussed and several data from our similar research were provided