Artificial targeting of the NEIL1 DNA glycosylase to the mitochondria.

The human NEIL1 DNA glycosylase is one of 11 mammalian glycosylases that initiate base excision repair. While substrate preference, catalytic mechanism, and structural information of NEIL1's ordered residues are available, limited information on its subcellular localization, compounded by relatively low endogenous expression levels, have impeded our understanding of NEIL1. Here, we employed a previously developed computational framework to optimize the mitochondrial localization signal of NEIL1, enabling the visualization of its specific targeting to the mitochondrion via confocal microscopy.

Utilizing Machine Learning Techniques to Predict the Efficacy of Aerobic Exercise Intervention on Young Hypertensive Patients Based on Cardiopulmonary Exercise Testing.

Recently, the incidence of hypertension has significantly increased among young adults. While aerobic exercise intervention (AEI) has long been recognized as an effective treatment, individual differences in response to AEI can seriously influence clinicians' decisions. In particular, only a few studies have been conducted to predict the efficacy of AEI on lowering blood pressure (BP) in young hypertensive patients.

Machine Learning Techniques in Exploring MicroRNA Gene Discovery, Targets, and Functions.

In recent years, the role of miRNAs in post-transcriptional gene regulation has provided new insights into the understanding of several types of cancers and neurological disorders. Although miRNA research has gathered great momentum since its discovery, traditional biological methods for finding miRNA genes and targets continue to remain a huge challenge due to the laborious tasks and extensive time involved. Fortunately, advances in computational methods have yielded considerable improvements in miRNA studies.

Effective Removal of Noisy Data Via Batch Effect Processing.

In order to have faith in the analysis of data, a key factor is to have confidence that the data is reliable. In the case of microRNA, reliability includes understanding the collection methods, ensuring that the analysis is appropriate, and ensuring that the data itself is accurate. A key element in ensuring data accuracy is the removal of noise.

Exploiting multi-layered vector spaces for signal peptide detection.

Analysing and classifying sequences based on similarities and differences is a mathematical problem of escalating relevance and importance in many scientific disciplines. One of the primary challenges in applying machine learning algorithms to sequential data, such as biological sequences, is the extraction and representation of significant features from the data. To address this problem, we have recently developed a representation, entitled Multi-Layered Vector Spaces (MLVS), which is a simple mathematical model that maps sequences into a set of MLVS.

Exploitation of 3D stereotactic surface projection for predictive modelling of Alzheimer's disease.

Alzheimer's Disease (AD) is one major cause of dementia. Previous studies have indicated that the use of features derived from Positron Emission Tomography (PET) scans lead to more accurate and earlier diagnosis of AD, compared to the traditional approaches that use a combination of clinical assessments. In this study, we compare Naive Bayes (NB) with variations of Support Vector Machines (SVMs) for the automatic diagnosis of AD.