Protein Remote Homology Detection Based on an Ensemble Learning Approach

Protein remote homology detection is one of the central problems in bioinformatics. Although some computational methods have been proposed, the problem is still far from being solved. In this paper, an ensemble classifier for protein remote homology detection, called SVM-Ensemble, was proposed with a weighted voting strategy. SVM-Ensemble combined three basic classifiers based on different feature spaces, including Kmer, ACC, and SC-PseAAC. These features consider the characteristics of proteins from various perspectives, incorporating both the sequence composition and the sequence-order information along the protein sequences. Experimental results on a widely used benchmark dataset showed that the proposed SVM-Ensemble can obviously improve the predictive performance for the protein remote homology detection. Moreover, it achieved the best performance and outperformed other state-of-the-art methods

Efficacy of Co-administration of Liuwei Dihuang Pills and Ginkgo Biloba Tablets on Albuminuria in Type 2 Diabetes: A 24-Month, Multicenter, Double-Blind, Placebo-Controlled, Randomized Clinical Trial

Purpose: We investigated the effects of Traditional Chinese Medicine (TCM) on the occurrence and progression of albuminuria in patients with type 2 diabetes.Methods: In this randomized, double-blind, multicenter, controlled trial, we enrolled 600 type 2 diabetes without diabetic nephropathy (DN) or with early-stage DN. Patients were randomly assigned (1:1) to receive Liuwei Dihuang Pills (LWDH) (1.5 g daily) and Ginkgo biloba Tablets (24 mg daily) orally or matching placebos for 24 months. The primary endpoint was the change in urinary albumin/creatinine ratio (UACR) from baseline to 24 months.Results: There were 431 patients having UACR data at baseline and 24 months following-up in both groups. Changes of UACR from baseline to follow-up were not affected in both groups: −1.61(−10.24, 7.17) mg/g in the TCM group and −0.73(−7.47, 6.75) mg/g in the control group. For patients with UACR ≥30 mg/g at baseline, LWDH and Ginkgo biloba significantly reduced the UACR value at 24 months [46.21(34.96, 58.96) vs. 20.78(9.62, 38.85), P < 0.05]. Moreover, the change of UACR from baseline to follow-up in the TCM group was significant higher than that in the control group [−25.50(−42.30, −9.56] vs. −20.61(−36.79, 4.31), P < 0.05].Conclusion: LWDH and Ginkgo biloba may attenuate deterioration of albuminuria in type 2 diabetes patients. These results suggest that TCM is a promising option of renoprotective agents for early stage of DN.Trial registration: The study was registered in the Chinese Clinical Trial Registry. (no. ChiCTR-TRC-07000037, chictr.org

Prediction of NB-UVB phototherapy treatment response of psoriasis patients using data mining

IEEE International Conference on Bioinformatics and Biomedicine, (BIBM-BHI 2017), Kansas, MO, USA, November 13-16, 2017NB-UVB Phototherapy is one of the most commontreatments administrated by dermatologists for psoriasis patients.Although in general, the treatment results in improving thecondition, it also can worsen it. If a model can predict thetreatment response before hand, the dermatologists can adjustthe treatment accordingly. In this paper, we use data miningtechniques and conduct four experiments. The best performanceof all four experiments was obtained by the stacked classifiermade of hyper parameter tuned Random Forest, kSVM and ANNbase learners, learned using L1-Regularized Logistic Regressionsuper learner.Science Foundation IrelandInsight Research Centre2017-12-14 JG: NB this is not the same paper as this: https://doi.org/10.1109/ICKEA.2017.816990

Recombination Hotspot/Coldspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach

Recombination presents a nonuniform distribution across the genome. Genomic regions that present relatively higher frequencies of recombination are called hotspots while those with relatively lower frequencies of recombination are recombination coldspots. Therefore, the identification of hotspots/coldspots could provide useful information for the study of the mechanism of recombination. In this study, a new computational predictor called SVM-EL was proposed to identify hotspots/coldspots across the yeast genome. It combined Support Vector Machines (SVMs) and Ensemble Learning (EL) based on three features including basic kmer (Kmer), dinucleotide-based auto-cross covariance (DACC), and pseudo dinucleotide composition (PseDNC). These features are able to incorporate the nucleic acid composition and their order information into the predictor. The proposed SVM-EL achieves an accuracy of 82.89% on a widely used benchmark dataset, which outperforms some related methods

Use of Data Mining Techniques to Predict Short Term Adverse Events Occurrence in NB-UVB Phototherapy Treatments

The prediction of short term adverse events occurrence in phototherapy treatment is important for the dermatologists who administrate phototherapy to adjust the treatment and standardize the clinical outcomes. Recently, a modeling technique which can detect the potential short term adverse events occurrence in phototherapy treatments is required for clinicians. Based on data mining, this study tends to explore the significant features and the class distribution of training data for the short term adverse events occurrence prediction in NB-UVB phototherapy treatments. The experimental results highlight that acceptable prediction accuracy can be achieved by using the significant features and the performance of the classifiers can be significantly improved by sampling 40% of negative class samples in training data, hyper parameter tuning of classifiers and use of stacked classifiers in creating prediction models.Science Foundation IrelandInsight Research CentreJournal site: http://www.ijmlc.org

Protein Remote Homology Detection Based on an Ensemble Learning Approach

Protein remote homology detection is one of the central problems in bioinformatics. Although some computational methods have been proposed, the problem is still far from being solved. In this paper, an ensemble classifier for protein remote homology detection, called SVM-Ensemble, was proposed with a weighted voting strategy. SVM-Ensemble combined three basic classifiers based on different feature spaces, including Kmer, ACC, and SC-PseAAC. These features consider the characteristics of proteins from various perspectives, incorporating both the sequence composition and the sequence-order information along the protein sequences. Experimental results on a widely used benchmark dataset showed that the proposed SVM-Ensemble can obviously improve the predictive performance for the protein remote homology detection. Moreover, it achieved the best performance and outperformed other state-of-the-art methods

The Limb Movement Analysis of Rehabilitation Exercises using Wearable Inertial Sensors

38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Florida, United States of America, 16-20 August 2016Due to no supervision of a therapist in home based exercise programs, inertial sensor based feedback systems which can accurately assess movement repetitions are urgently required. The synchronicity and the degrees of freedom both show that one movement might resemble another movement signal which is mixed in with another not precisely defined movement. Therefore, the data and feature selections are important for movement analysis. This paper explores the data and feature selection for the limb movement analysis of rehabilitation exercises. The results highlight that the classification accuracy is very sensitive to the mount location of the sensors. The results show that the use of 2 or 3 sensor units, the combination of acceleration and gyroscope data, and the feature sets combined by the statistical feature set with another type of feature, can significantly improve the classification accuracy rates. The results illustrate that acceleration data is more effective than gyroscope data for most of the movement analysis

Automatic Classification of Knee Rehabilitation Exercises Using a Single Inertial Sensor: a Case Study

IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 4-7 March 2018, Las Vegas, Nevada, USAInertial measurement units have the ability to accurately record the acceleration and angular velocity of human limb segments during discrete joint movements. These movements are commonly used in exercise rehabilitation programmes following orthopaedic surgery such as total knee replacement. This provides the potential for a biofeedback system with data mining technique for patients undertaking exercises at home without physician supervision. We propose to use machine learning techniques to automatically analyse inertial measurement unit data collected during these exercises, and then assess whether each repetition of the exercise was executed correctly or not. Our approach consists of two main phases: signal segmentation, and segment classification. Accurate pre-processing and feature extraction are paramount topics in order for the technique to work. In this paper, we present a classification method for unsupervised rehabilitation exercises, based on a segmentation process that extracts repetitions from a longer signal activity. The results obtained from experimental datasets of both clinical and healthy subjects, for a set of 4 knee exercises commonly used in rehabilitation, are very promising.Science Foundation Irelan

Hypoxia inducible factor-1α is an important regulator of macrophage biology

Hypoxia-inducible factor-1 (HIF-1), a heterodimeric transcription factor composed of the α and β subunits, regulates cellular adaptive responses to hypoxia. Macrophages, which are derived from monocytes, function as antigen-presenting cells that activate various immune responses. HIF-1α regulates the immune response, viability, migration, phenotypic plasticity, and metabolism of macrophages. Specifically, macrophage-derived HIF-1α can prevent excessive pro-inflammatory responses by attenuating the transcriptional activity of nuclear factor-kappa B in vivo and in vitro. HIF-1α modulates macrophage migration by inducing the release of various chemokines and providing necessary energy. HIF-1α promotes macrophage M1 polarization by targeting glucose metabolism. Additionally, HIF-1α induces the upregulation of glycolysis-related enzymes and intermediates of the tricarboxylic acid cycle and pentose phosphate pathway. HIF-1α promotes macrophage apoptosis, necroptosis and reduces autophagy. The current review highlights the mechanisms associated with the regulation of HIF-1α stabilization in macrophages as well as the role of HIF-1α in modulating the physiological functions of macrophages

A multi-period framework for coordinated dispatch of plug-in electric vehicles

Coordinated dispatch of plug-in electric vehicles (PEVs) with renewable energies has been proposed in recent years. However, it is difficult to achieve effective PEV dispatch with a win-win result, which not only optimizes power system operation, but also satisfies the requirements of PEV owners. In this paper, a multi-period PEV dispatch framework, combining day-ahead dispatch with real-time dispatch, is proposed. On the one hand, the day-ahead dispatch is used to make full use of wind power and minimize the fluctuation of total power in the distribution system, and schedule the charging/discharging power of PEV stations for each period. On the other hand, the real-time dispatch arranges individual PEVs to meet the charging/discharging power demands of PEV stations given by the day-ahead dispatch. To reduce the dimensions of the resulting large-scale, non-convex problem, PEVs are clustered according to their travel information. An interval optimization model is introduced to obtain the problem solution of the day-ahead dispatch. For the real-time dispatch, a priority-ordering method is developed to satisfy the requirements of PEV owners with fast response. Numerical studies demonstrate the effectiveness of the presented framework