Machine learning has been successfully applied to drug combination prediction in recent years. However, in some situations, the class imbalance problem still shows highly negative impacts on the modeling process, which cannot be directly handled by traditional methods. In addition, the interpretability of models is another key point for biological and medical experts. In this study, a clustering-based oblique decision tree (MOCT) algorithm is proposed to extract interpretable knowledge for the multi-class datasets. It firstly clusters samples of different classes, and then a proper feature subspace is generated to split data and forms a nonleaf node. Unlike traditional decision trees, our MOCT only grows one none-leaf node in each layer to generate a concise tree structure. Datasets of drug combinations were collected from three cell lines with three classes (Additive, Antagonism, and Synergy) in experiments, and the results show that our MOCT algorithm is superior to other methods with better interpretability.

For the interaction prediction task in the biomedical field, most machine learning algorithms overlook the relationships between entities within a pair by treating their features independently. To address this issue, this paper proposes a novel Cluster-based Paired Decision Tree model (CPDT), which pairs synonymous features of entity pairs to form paired feature spaces for simultaneous processing. It employs an adaptive grid-based clustering algorithm to partition these spaces in an axis-parallel manner, constructing interpretable decision boundaries. Moreover, the clustering algorithm leverages the probability density function to accommodate various data distributions in paired feature spaces, enhancing the effectiveness of sample partitioning. Experimental results demonstrate that CPDT performs well in two interaction prediction tasks: Drug Combination and Synthetic Lethality predictions. Furthermore, CPDT yields simple and interpretable decision rules that uncover potential patterns in biomedical interaction prediction. It also identifies molecules with medical significance, suggesting promising applications in the biomedical domain.

The interpretable machine learning method is important in drug discovery. Unlike traditional ensemble learning methods, this paper proposes an interpretable algorithm based on Bayesian rule extraction to obtain reliable and explainable results for hepatotoxicity prediction. To extract information from different types of omics data, our algorithm employs a multi-view learning strategy to enhance performance. Specifically, a random forest is trained in each view, and then the Bayesian rule extraction algorithm is designed to select an optimal rule subset, controlling the size and accuracy of the ruleset through probabilities. These rule sets are integrated through multi-view voting to get the final decisions. The performance of our algorithm is tested on the hepatotoxicity dataset, demonstrating that compared to traditional machine learning algorithms and rule-based algorithms, our approach maintains excellent performance while achieving high interpretability in most cases. Our python source code and the related Supplementary Materials are available at: github.com/MLDMXM2017/MV-BRS.

Speech emotion recognition (SER) plays a vital role in improving the interactions between humans and machines by inferring human emotion and affective states from speech signals. Whereas recent works primarily focus on mining spatiotemporal information from hand-crafted features, we explore how to model the temporal patterns of speech emotions from dynamic temporal scales. Towards that goal, we introduce a novel temporal emotional modeling approach for SER, termed Temporal-aware bI-direction Multi-scale Network (TIM-Net), which learns multi-scale contextual affective representations from various time scales. Specifically, TIM-Net first employs temporal-aware blocks to learn temporal affective representation, then integrates complementary information from the past and the future to enrich contextual representations, and finally fuses multiple time scale features for better adaptation to the emotional variation. Extensive experimental results on six benchmark SER datasets demonstrate the superior performance of TIM-Net, gaining 2.34% and 2.61% improvements of the average UAR and WAR over the second-best on each corpus. The source code is available at https://github.com/Jiaxin-Ye/TIM-Net_SER.

INTRODUCTION: There are countless possibilities for drug combinations, which makes it expensive and time-consuming to rely solely on clinical trials to determine the effects of each possible drug combination. In order to screen out the most effective drug combinations more quickly, scholars began to apply machine learning to drug combination prediction. However, most of them are of low interpretability. Consequently, even though they can sometimes produce high prediction accuracy, experts in the medical and biological fields can still not fully rely on their judgments because of the lack of knowledge about the decision-making process. RELATED WORK: Decision trees and their ensemble algorithms are considered to be suitable methods for pharmaceutical applications due to their excellent performance and good interpretability. We review existing decision trees or decision tree ensemble algorithms in the medical field and point out their shortcomings. METHOD: This study proposes a decision stump (DS)-based solution to extract interpretable knowledge from data sets. In this method, a set of DSs is first generated to selectively form a decision tree (DST). Different from the traditional decision tree, our algorithm not only enables a partial exchange of information between base classifiers by introducing a stump exchange method but also uses a modified Gini index to evaluate stump performance so that the generation of each node is evaluated by a global view to maintain high generalization ability. Furthermore, these trees are combined to construct an ensemble of DST (EDST). EXPERIMENT: The two-drug combination data sets are collected from two cell lines with three classes (additive, antagonistic and synergistic effects) to test our method. Experimental results show that both our DST and EDST perform better than other methods. Besides, the rules generated by our methods are more compact and more accurate than other rule-based algorithms. Finally, we also analyze the extracted knowledge by the model in the field of bioinformatics. CONCLUSION: The novel decision tree ensemble model can effectively predict the effect of drug combination datasets and easily obtain the decision-making process.

Despite the development of computer vision techniques, the micro-expression (ME) recognition task still remains a great challenge because MEs have very low intensity and short duration. However, the ME recognition is of great significance since it provides important clues for real affective states detection. This paper proposes a novel Block Division Convolutional Network (BDCNN) with the implicit deep features augmentation. In detail, BDCNN learns from four optical flow features computed by the onset and apex frames of each video. It innovatively divides each image into a set of small blocks in the deep learning model, then the convolution and pooling operations are performed on these small blocks in sequence. To handle the small sample size problem in the micro-expression data, this study uses the improved implicit semantic data augmentation algorithm in the deep features space. Experiments are conducted on three publicly available databases, viz, CASME II, SMIC, and SAMM. Experimental results show that our model outperforms the state-of-the-art methods by attaining the accuracy of 84.32% and F1-score of 82.13% on the 3-class datasets, and the accuracy of 81.82% and F1-score of 75.46% on the 5-class datasets, respectively. Our source code is publicly available for non-commercial or research use athttps://github.com/MLDMXM2017/BDCNN.

Hepatotoxicity prediction is key to diseases with the high mortality rate. However, most of the algorithms used by now are black box in nature and lack of clear interpretability. This paper proposes a genetic algorithm-based interpretable algorithm based on rules extracted from a random forest. To take advantages from different types of omics data and molecular representations gathered from various datasets, our algorithm utilizes multiple distinct features to form a multi-view learning strategy. In detail, the genetic algorithm is designed to select optimal rules from each view, which are then used to form the ensemble of multi-view rule sets. The experiments are carried out to verify the performance of our algorithm on the hepatotoxicity data. The results confirm that our algorithm can gain high accuracy in most cases with more compact and shorter rules, compared with the original random forest or other rule-based algorithms. Our python source code and the related Supplementary Materials are available at: github.com/MLDMXM2017/MVR-GA.

Speech Emotion Recognition (SER) has become a growing focus of research in human-computer interaction. An essential challenge in SER is to extract common attributes from different speakers or languages, especially when a specific source corpus has to be trained to recognize the unknown data coming from another speech corpus. To address this challenge, a Capsule Network (CapsNet) and Transfer Learning based Mixed Task Net (CTL-MTNet) are proposed to deal with both the single-corpus and cross-corpus SER tasks simultaneously in this paper. For the single-corpus task, the combination of Convolution-Pooling and Attention CapsNet module (CPAC) is designed by embedding the self-attention mechanism to the CapsNet, guiding the module to focus on the important features that can be fed into different capsules. The extracted high-level features by CPAC provide sufficient discriminative ability. Furthermore, to handle the cross-corpus task, CTL-MTNet employs a Corpus Adaptation Adversarial Module (CAAM) by combining CPAC with Margin Disparity Discrepancy (MDD), which can learn the domain-invariant emotion representations through extracting the strong emotion commonness. Experiments including ablation studies and visualizations on both single- and cross-corpus tasks using four well-known SER datasets in different languages are conducted for performance evaluation and comparison. The results indicate that in both tasks the CTL-MTNet showed better performance in all cases compared to a number of state-of-the-art methods. The source code and the supplementary materials are available at: https://github.com/MLDMXM2017/CTLMTNet.

The wireless sensor network (WSN) has recently become an intensive research focus due to its potential applications in many areas. In this paper, we propose a new and efficient genetic algorithm (GA) to the optimal placement of sensors in a grid area with obstacles and preferences to minimize the number of sensors. A new sensor detection model is also introduced in this paper. Experiments show that our algorithm is able to achieve better results than previous heuristic algorithms.

The widespread deployment of WDM optical networks posts lots of new challenges for network designers. Traffic grooming is one of the most common problems. Efficient grooming of traffic can effectively reduce the overall cost of the network. But unfortunately, traffic grooming problems have been shown to be NP-hard. Therefore, new heuristics must be devised to tackle them. Among these approaches, metaheuristics are among the most promising ones. In this paper, we present a thorough and comprehensive survey on various metaheuristic approaches to the grooming of traffic in both static and dynamic patterns in WDM optical networks. Some future challenges and research directions are also discussed in this paper.

Terminal assignment (TA) is important in increasing the telecommunication networks' capacity and reducing the cost of it. We propose a tabu search (TS) approach to solve the problem with non-standard cost functions. A greedy decoding approach is used to generate the initial solution and then an effective and unique search approach is proposed to produce the neighborhood, which exchange one of the terminals in each concentrator to improve the quality of solution. Simulation results with the proposed TS approach are compared with those using genetic and greedy algorithms. Computer simulations show that our approach achieves very good results in solving this problem.