Motivation Mitochondria are an essential organelle in most eukaryotes. They not only play an important role in energy metabolism but also take part in many critical cytopathological processes. Abnormal mitochondria can trigger a series of human diseases, such as Parkinson's disease, multifactor disorder and Type-II diabetes. Protein submitochondrial localization enables the understanding of protein function in studying disease pathogenesis and drug design. Results We proposed a new method, SubMito-XGBoost, for protein submitochondrial localization prediction. Three steps are included: (i) the g-gap dipeptide composition (g-gap DC), pseudo-amino acid composition (PseAAC), auto-correlation function (ACF) and Bi-gram position-specific scoring matrix (Bi-gram PSSM) are employed to extract protein sequence features, (ii) Synthetic Minority Oversampling Technique (SMOTE) is used to balance samples, and the ReliefF algorithm is applied for feature selection and (iii) the obtained feature vectors are fed into XGBoost to predict protein submitochondrial locations. SubMito-XGBoost has obtained satisfactory prediction results by the leave-one-out-cross-validation (LOOCV) compared with existing methods. The prediction accuracies of the SubMito-XGBoost method on the two training datasets M317 and M983 were 97.7% and 98.9%, which are 2.8–12.5% and 3.8–9.9% higher than other methods, respectively. The prediction accuracy of the independent test set M495 was 94.8%, which is significantly better than the existing studies. The proposed method also achieves satisfactory predictive performance on plant and non-plant protein submitochondrial datasets. SubMito-XGBoost also plays an important role in new drug design for the treatment of related diseases. Availability and implementation The source codes and data are publicly available at https://github.com/QUST-AIBBDRC/SubMito-XGBoost/. Supplementary information Supplementary data are available at Bioinformatics online.

Most Simultaneous Localization and Mapping (SLAM) methods assume that environments are static. Such a strong assumption limits the application of most visual SLAM systems. The dynamic objects will cause many wrong data associations during the SLAM process. To address this problem, a novel visual SLAM method that follows the pipeline of feature-based methods called DM-SLAM is proposed in this paper. DM-SLAM combines an instance segmentation network with optical flow information to improve the location accuracy in dynamic environments, which supports monocular, stereo, and RGB-D sensors. It consists of four modules: semantic segmentation, ego-motion estimation, dynamic point detection and a feature-based SLAM framework. The semantic segmentation module obtains pixel-wise segmentation results of potentially dynamic objects, and the ego-motion estimation module calculates the initial pose. In the third module, two different strategies are presented to detect dynamic feature points for RGB-D/stereo and monocular cases. In the first case, the feature points with depth information are reprojected to the current frame. The reprojection offset vectors are used to distinguish the dynamic points. In the other case, we utilize the epipolar constraint to accomplish this task. Furthermore, the static feature points left are fed into the fourth module. The experimental results on the public TUM and KITTI datasets demonstrate that DM-SLAM outperforms the standard visual SLAM baselines in terms of accuracy in highly dynamic environments.

This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm. The results show that model interpolation, though simple, achieves the best results on all the open test sets where the test data is very different from the training data. The tree-based boosting algorithm achieves the best performance on most of the closed test sets where the test data and the training data are similar, but its performance drops significantly on the open test sets due to the instability of trees. Several methods are explored to improve the robustness of the algorithm, with limited success.

The Golgi apparatus is a key organelle for protein synthesis in eukaryotic cell. Any dysfunction of Golgi-resident proteins can lead to different diseases, especially neurodegenerative and inherited diseases, such as diabetes, cancer, and cystic fibrosis, and so on. Therefore, the accurate classification of Golgi-resident proteins may contribute to drug development and further to drug therapy. This paper presents a novel Golgiresident protein types prediction method called Golgi-XGBoost. First, the feature vectors of protein sequence are extracted by fusing pseudo-amino acid composition (PseAAC), dipeptide composition (DC), pseudoposition specific scoring matrix (PsePSSM) and encoding based on grouped weight (EBGW). Secondly, the conditional covariance minimization (CCM) is used to reduce the dimension of the feature vectors. Then, we adopt the synthetic minority over sampling technique (SMOTE) to balance the samples. Finally, the optimal feature vectors are input into the extreme gradient boosting (XGBoost) classifier to predict the type of Golgi-resident protein. The overall prediction accuracy is 92.1% on training set via jackknife test, which achieves better performance than other state-of-the-art methods. The accuracy of independent testing dataset is 86.5%. And the results show that this paper provides a new method for predicting the type of Golgi-resident protein. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/Golgi-XGBoost/.