Active module identification in intracellular networks using a memetic algorithm with a new binary decoding scheme

BACKGROUND: Active modules are connected regions in biological network which show significant changes in expression over particular conditions. The identification of such modules is important since it may reveal the regulatory and signaling mechanisms that associate with a given cellular response. RESULTS: In this paper, we propose a novel active module identification algorithm based on a memetic algorithm. We propose a novel encoding/decoding scheme to ensure the connectedness of the identified active modules. Based on the scheme, we also design and incorporate a local search operator into the memetic algorithm to improve its performance. CONCLUSION: The effectiveness of proposed algorithm is validated on both small and large protein interaction networks

CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services

Click-through rate (CTR) prediction is a crucial task in the context of an online on-demand food delivery (OFD) platform for precisely estimating the probability of a user clicking on food items. Unlike universal e-commerce platforms such as Taobao and Amazon, user behaviors and interests on the OFD platform are more location and time-sensitive due to limited delivery ranges and regional commodity supplies. However, existing CTR prediction algorithms in OFD scenarios concentrate on capturing interest from historical behavior sequences, which fails to effectively model the complex spatiotemporal information within features, leading to poor performance. To address this challenge, this paper introduces the Contrastive Sres under different search states using three modules: contrastive spatiotemporal representation learning (CSRL), spatiotemporal preference extractor (StPE), and spatiotemporal information filter (StIF). CSRL utilizes a contrastive learning framework to generate a spatiotemporal activation representation (SAR) for the search action. StPE employs SAR to activate users' diverse preferences related to location and time from the historical behavior sequence field, using a multi-head attention mechanism. StIF incorporates SAR into a gating network to automatically capture important features with latent spatiotemporal effects. Extensive experiments conducted on two large-scale industrial datasets demonstrate the state-of-the-art performance of CSPM. Notably, CSPM has been successfully deployed in Alibaba's online OFD platform Ele.me, resulting in a significant 0.88% lift in CTR, which has substantial business implications

Generalization Bounds Derived IPM-Based Regularization for Domain Adaptation

Domain adaptation has received much attention as a major form of transfer learning. One issue that should be considered in domain adaptation is the gap between source domain and target domain. In order to improve the generalization ability of domain adaption methods, we proposed a framework for domain adaptation combining source and target data, with a new regularizer which takes generalization bounds into account. This regularization term considers integral probability metric (IPM) as the distance between the source domain and the target domain and thus can bound up the testing error of an existing predictor from the formula. Since the computation of IPM only involves two distributions, this generalization term is independent with specific classifiers. With popular learning models, the empirical risk minimization is expressed as a general convex optimization problem and thus can be solved effectively by existing tools. Empirical studies on synthetic data for regression and real-world data for classification show the effectiveness of this method

Link Prediction via Sparse Gaussian Graphical Model

Link prediction is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC) value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods

Multi-Path Routing Algorithm for Wireless Sensor Network Based on Semi-Supervised Learning

Multi-path transmission can well solve the data transmission reliability problems and life cycle problems caused by single-path transmission. However, the accuracy of the routing scheme generated by the existing multi-path routing algorithms is difficult to guarantee. In order to improve the accuracy of the multi-path routing scheme, this paper innovatively proposes a multi-path routing algorithm for a wireless sensor network (WSN) based on the evaluation. First, we design and implement the real-time evaluation algorithm based on semi-supervised learning (RESL). We prove that RESL is better in evaluation time and evaluation accuracy through comparative experiments. Then, we combine RESL to design and implement the multi-path routing algorithm for wireless sensor networks based on semi-supervised learning (MRSSL). Then, we prove that MRSSL has advantages in improving the accuracy of the multi-path routing scheme through comparative experiments

An efficient semi-supervised community detection framework in social networks.

Community detection is an important tasks across a number of research fields including social science, biology, and physics. In the real world, topology information alone is often inadequate to accurately find out community structure due to its sparsity and noise. The potential useful prior information such as pairwise constraints which contain must-link and cannot-link constraints can be obtained from domain knowledge in many applications. Thus, combining network topology with prior information to improve the community detection accuracy is promising. Previous methods mainly utilize the must-link constraints while cannot make full use of cannot-link constraints. In this paper, we propose a semi-supervised community detection framework which can effectively incorporate two types of pairwise constraints into the detection process. Particularly, must-link and cannot-link constraints are represented as positive and negative links, and we encode them by adding different graph regularization terms to penalize closeness of the nodes. Experiments on multiple real-world datasets show that the proposed framework significantly improves the accuracy of community detection

Two-Dimensional Extreme Learning Machine

Extreme learning machine (ELM) has achieved wide attention due to faster learning speed compared with conventional neural network models like support vector machine (SVM) and back-propagation (BP) networks. However, like many other methods, ELM is originally proposed to handle vector pattern while nonvector patterns in real applications need to be explored, such as image data. We propose the two-dimensional extreme learning machine (2DELM) based on the very natural idea to deal with matrix data directly. Unlike original ELM which handles vectors, 2DELM take the matrices as input features without vectorization. Empirical studies on several real image datasets show the efficiency and effectiveness of the algorithm

Two-Dimensional Extreme Learning Machine

Extreme learning machine (ELM) has achieved wide attention due to faster learning speed compared with conventional neural network models like support vector machine (SVM) and back-propagation (BP) networks. However, like many other methods, ELM is originally proposed to handle vector pattern while nonvector patterns in real applications need to be explored, such as image data. We propose the two-dimensional extreme learning machine (2DELM) based on the very natural idea to deal with matrix data directly. Unlike original ELM which handles vectors, 2DELM take the matrices as input features without vectorization. Empirical studies on several real image datasets show the efficiency and effectiveness of the algorithm