Exploring natural products potential: A similarity-based target prediction tool for natural products.

Natural products are invaluable resources in drug discovery due to their substantial structural diversity. However, predicting their interactions with druggable protein targets remains a challenge, primarily due to the limited availability of bioactivity data. This study introduces CTAPred (Compound-Target Activity Prediction), an open-source command-line tool designed to predict potential protein targets for natural products.

Impact of Mobile Games-Aided Neurorehabilitation: A Systematic Literature Review.

Neurological rehabilitation is a physician-supervised programme for individuals with nervous system diseases, injuries or disorders. Neurological rehabilitation, also known as neurorehabilitation, is part of the rehabilitation process that improves function, reduces severity and enhances a patient's well-being. Because neurological injuries occur in the brain, spine and nerves, affecting multiple body parts including organs, blood vessels, muscles and bones, rehabilitation requires a multidisciplinary approach.

Big Brain Data Initiatives in Universiti Sains Malaysia: Data Stewardship to Data Repository and Data Sharing.

The sharing of open-access neuroimaging data has increased significantly during the last few years. Sharing neuroimaging data is crucial to accelerating scientific advancement, particularly in the field of neuroscience. A number of big initiatives that will increase the amount of available neuroimaging data are currently in development.

Comparative Studies on Resampling Techniques in Machine Learning and Deep Learning Models for Drug-Target Interaction Prediction.

The prediction of drug-target interactions (DTIs) is a vital step in drug discovery. The success of machine learning and deep learning methods in accurately predicting DTIs plays a huge role in drug discovery. However, when dealing with learning algorithms, the datasets used are usually highly dimensional and extremely imbalanced.

DeepNC: a framework for drug-target interaction prediction with graph neural networks.

The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on this stage. Widely practiced deep learning algorithms such as convolutional neural networks and recurrent neural networks are commonly employed in DTI prediction projects.

Turbo Similarity Searching: Effect of Partial Ranking and Fusion Rules on ChEMBL Database.

Turbo Similarity Searching (TSS) is the simplest and most recent chemical similarity searching (SS) approach, which improves the effectiveness of SS by performing a multi-target searching. TSS has four important elements, namely structural representation, similarity coefficient, number of nearest neighbours (NNs), and fusion rule, and any changes in these elements could affect the TSS results. A previous study suggested the advantage of using large numbers of reference compounds with small fractions of the database structures to obtain a better recall in group fusion.

Big Brain Data Initiative in Universiti Sains Malaysia: Challenges in Brain Mapping for Malaysia.

Universiti Sains Malaysia has started the Big Brain Data Initiative project since the last two years as brain mapping techniques have proven to be important in understanding the molecular, cellular and functional mechanisms of the brain. This Big Brain Data Initiative can be a platform for neurophysicians and neurosurgeons, psychiatrists, psychologists, cognitive neuroscientists, neurotechnologists and other researchers to improve brain mapping techniques. Data collection from a cohort of multiracial population in Malaysia is important for present and future research and finding cure for neurological and mental illness.