Protein-protein interactions (PPIs) are central to cellular signaling and regulation, and their dysregulation underlies many diseases. Predicting the impact of mutations on PPI stability, quantified as ΔΔG, is essential for understanding disease mechanisms and guiding protein engineering. Here, we first present MutPPI, a graph-based deep-learning model that encodes full-residue structural features of protein-protein complexes and employs a shared GIN-GAT feature extractor for wild-type and mutant complexes. MutPPI outperforms 12 existing methods on an antibody-antigen single-point mutation dataset (S645). By integrating evolutionary information from protein language models, we further develop MutPPI-plus, achieving enhanced predictive performance. Second, we proposed a mutation-path-based data augmentation strategy, which enriches input modalities and improves generalization of both MutPPI and MutPPI-plus. After data augmentation, MutPPI-plus demonstrates state-of-the-art performance on S645 and three additional multi-point mutation datasets (SM_ZEMu, SM595, SM1124), substantially surpassing DDMut-PPI. Our analyses highlight the benefits of the multimodal framework and the physically informed data augmentation method. Together, these results provide a versatile computational tool for accurate ΔΔG prediction, advancing rational protein design.

In the actual industrial production process, due to the uncertainties and disturbances caused by measurement errors, abnormal events and other factors, the single-value exact prediction models for key indicators usually fail to guarantee high prediction accuracy. The adoption of prediction interval regression can effectively improve this problem. In this paper, two twin extreme learning machines for interval regression, TELMR and K-TELMR, are proposed. Specifically, TELMR aims to find two non-parallel hyperplanes passing through the origin that approach the sample points from the upper and lower bounds, respectively. The regression error is minimized in each hyperplane without exceeding the sample points as much as possible, and slack variables and a penalty term are introduced to balance the underfitting and overfitting problems. K-TELMR is based on TELMR with kernel mapping instead of random mapping, which saves the step of optimizing the number of hidden layer neurons and makes the model further simplified and lightweight. Numerical computation results on several UCI regression problems and a practical industrial regression problem validate the effectiveness of the proposed models.

CD8+ T cells can recognize peptides presented by class I human leukocyte antigen (HLA-I) of nucleated cells. Exploring this immune mechanism is essential for identifying T-cell vaccine targets in cancer immunotherapy. Over the past decade, the wealth of data generated by experiments has spawned many computational approaches for predicting HLA-I binding, antigen presentation and T-cell immune responses. Nevertheless, existing HLA-I binding and antigen presentation prediction approaches suffer from low precision due to the absence of T-cell receptor (TCR) recognition. Direct modeling of T-cell immune responses is less effective as TCR recognition's mechanism still remains underexplored. Therefore, directly applying these existing methods to screen cancer neoantigens is still challenging. Here, we propose a novel immune epitope prediction method termed IEPAPI by effectively incorporating antigen presentation and immunogenicity. First, IEPAPI employs a transformer-based feature extraction block to acquire representations of peptides and HLA-I proteins. Second, IEPAPI integrates the prediction of antigen presentation prediction into the input of immunogenicity prediction branch to simulate the connection between the biological processes in the T-cell immune response. Quantitative comparison results on an independent antigen presentation test dataset exhibit that IEPAPI outperformed the current state-of-the-art approaches NetMHCpan4.1 and mhcflurry2.0 on 100 (25/25) and 76% (19/25) of the HLA subtypes, respectively. Furthermore, IEPAPI demonstrates the best precision on two independent neoantigen datasets when compared with existing approaches, suggesting that IEPAPI provides a vital tool for T-cell vaccine design.

The identification of gene regulatory networks (GRN) from gene expression time series data is a challenge and open problem in system biology. This paper considers the structure inference of GRN from the incomplete and noisy gene expression data, which is a not well-studied issue for GRN inference. In this paper, the dynamical behavior of the gene expression process is described by a stochastic nonlinear state-space model with unknown noise information. A variational Bayesian (VB) framework are proposed to estimate the parameters and gene expression levels simultaneously. One of the advantages of this method is that it can easily handle the missing observations by generating the prediction values. Considering the sparsity of GRN, the smoothed gene data are modeled by the extreme gradient boosting tree, and the regulatory interactions among genes are identified by the importance scores based on the tree model. The proposed method is tested on the artificial DREAM4 datasets and one real gene expression dataset of yeast. The comparative results show that the proposed method can effectively recover the regulatory interactions of GRN in the presence of missing observations and outperforms the existing methods for GRN identification.

This paper tackles a large-scale and uncertain scheduling problem of semiconductor wafer fabrication system. Two kinds of uncertainties are considered here, including the machine breakdowns and the fluctuations of processing times. Instead of using the traditional rigid schedule, we propose a novel concept of operation-group-based soft schedule which is used as the new decision variable of the studied problem. The inherent idea of this concept is to make some critical scheduling decisions at the beginning of the scheduling horizon, and allow the remaining decisions to be made during the execution of the initial soft schedule. Furthermore, an operation-group-based soft scheduling approach (OGSSA) is proposed to deal with the uncertainties, which contains an offline optimization layer and an online dispatching layer. In the offline optimization layer, a prediction-based decomposition scheduling method is proposed to generate a soft schedule, and a global scheduling objective prediction model is constructed to evaluate the soft schedule. Then, the initial soft schedule is released to the online dispatching layer, and an online heuristic rule is designed to decide in real time, which operations and when to process. The computational results on the practical production data demonstrate the effectiveness of OGSSA under uncertain production environments.

The flame image has important significance in combustion state recognition and judgment, which can be used effectively for control of energy consumption and exhaust emissions. Due to the harsh industrial environments, flame images are usually corrupted by transmission errors or coding issues, which makes the combustion state analysis very challenging. This paper proposes a novel flame combustion state analysis framework, which provides new insight into two crucial issues: corrupted flame image recovery and combustion state recognition. First, we propose Riemannian alternative optimization (RAO) with fast convergence and the global optimization ability to recover the corrupted flame image. More specifically, RAO constructs a low-rank factorization model and exploits the geometric nature of the flame image to perform the optimization on Riemannian manifolds. Second, we use Fisher discriminant analysis to exploit discriminative features of the recovered flame image and provide well-separated classes of the combustion state for recognition. The experiments show that the proposed framework recovers the corrupted flame image efficiently and achieves satisfying performance of combustion state recognition.

In this paper, an uncertain scheduling problem arising from the steelmaking-continuous casting (SCC) production system is investigated. For the practical SCC production system, it is difficult to obtain a schedule with better performance using traditional deterministic scheduling methods since there exists uncertainty in processing times. According to the analysis on characteristics of the uncertain SCC scheduling problem (SCCSP), we construct a soft-form schedule which includes slack ratios as characteristic indexes and the job sequence at the casting stage as key decision variables to cope with the uncertainty in processing times, and propose a two-phase soft optimization method to solve the uncertain SCCSP with the just-in-time and the waiting time objectives under the break probability. In the first phase, the continuous estimation distribution algorithm (EDA) with the ordinal optimization policy is proposed to optimize slack ratios under the chance constraint, in which the optimal computing budget allocation with constrained optimization is applied to reduce the computational burden. In the second phase, based on the above optimized characteristic indexes, the discrete EDA with a local search procedure is proposed to optimize the job sequence at the casting stage. Finally, computational experiments with various scales and noise levels are performed to validate the effectiveness of the proposed algorithm.

In the operational optimization and scheduling problems of actual industrial processes, such as iron and steel, and microelectronics, the operational indices and process parameters usually need to be predicted. However, for some input and output variables of these prediction models, there may exist a lot of uncertainties coming from themselves, the measurement error, the rough representation, and so on. In such cases, constructing a prediction interval (PI) for the output of the corresponding prediction model is very necessary. In this paper, two twin extreme learning machine (TELM) models for constructing PIs are proposed. First, we propose a regularized asymmetric least squares extreme learning machine (RALS-ELM) method, in which different weights of its squared error loss function are set according to whether the error of the model output is positive or negative in order that the above error can be differentiated in the parameter learning process, and Tikhonov regularization is introduced to reduce overfitting. Then, we propose an asymmetric Bayesian extreme learning machine (AB-ELM) method based on the Bayesian framework with the asymmetric Gaussian distribution (AB-ELM), in which the weights of its likelihood function are determined as the same method in RALS-ELM, and the type II maximum likelihood algorithm is derived to learn the parameters of AB-ELM. Based on RALS-ELM and AB-ELM, we use a pair of weights following the reciprocal relationship to obtain two nonparallel regressors, including a lower-bound regressor and an upper-bound regressor, respectively, which can be used for calculating the PIs. Finally, some discussions are given, about how to adjust the weights adaptively to meet the desired PI, how to use the proposed TELMs for nonlinear quantile regression, and so on. Results of numerical comparison on data from one synthetic regression problem, three University of California Irvine benchmark regression problems, and two actual industrial regression problems show the effectiveness of the proposed models.

Outliers often exist in the data for modeling in actual industrial processes. If these outliers are used as support vectors, the obtained Support Vector Regression function maybe unreliable. In this paper, we propose a new Robust Least Squares Support Vector Regression based on variational Bayesian (RB-LSSVR). The main idea of RB-LSSVR is to learn the parameters of LSSVR in Bayesian framework, but replace the Gaussian distribution with Student's t-distribution as the probability density function of residuals of the model output and real output, which makes the model more robust to outliers. In order to solve RB-LSSVR, the Student's t-distribution is written as a scale-mixture form and variational approximation is used to iteratively learn the parameters of RB-LSSVR. The hyperparameters of the Gamma distribution that can't be solved explicitly are optimized by using Newton method. And, by using variational Bayesian, the user-specified parameters selection is simplified in RB-LSSVR. The numerical results based on several benchmark regression problems and one actual industrial modeling problem show the proposed RB-LSSVR can handle outliers very well.

Attribute reduction is one basic issue in knowledge discovery of information systems. In this paper, based on the object oriented concept lattice and classical concept lattice, the approach of attribute reduction for formal contexts is investigated. We consider attribute reduction and attribute characteristics from the perspective of linear dependence of vectors. We first introduce the notion of context matrix and the operations of corresponding column vectors, then present some judgment theorems of attribute reduction for formal contexts. Furthermore, we propose a new method to reducing formal context and show corresponding reduction algorithms. Compared with previous reduction approaches which employ discernibility matrix and discernibility function to determine all reducts, the proposed approach is more simpler and easier to implement.

This paper considers minimizing makespan for a blocking flowshop scheduling problem, which has important application in a variety of modern industries. A constructive heuristic is first presented to generate a good initial solution by combining the existing profile fitting (PF) approach and Nawaz-Enscore-Ham (NEH) heuristic in an effective way. Then, a memetic algorithm (MA) is proposed including effective techniques like a heuristic-based initialization, a path-relinking-based crossover operator, a referenced local search, and a procedure to control the diversity of the population. Afterwards, the parameters and operators of the proposed MA are calibrated by means of a design of experiments approach. Finally, a comparative evaluation is carried out with the best performing algorithms presented for the blocking flowshop with makespan criterion, and with the adaptations of other state-of-the-art MAs originally designed for the regular flowshop problem. The results show that the proposed MA performs much better than the other algorithms. Ultimately, 75 out of 120 upper bounds provided by Ribas [“An iterated greedy algorithm for the flowshop scheduling with blocking”, OMEGA, vol. 39, pp. 293-301, 2011.] for Taillard flowshop benchmarks that are considered as blocking flowshop instances are further improved by the presented MA.

Job deterioration and learning co-exist in many realistic scheduling situations. This paper introduces a general scheduling model that considers the effects of position-dependent learning and time-dependent deterioration simultaneously. In the proposed model, the actual processing time of a job depends not only on the total processing time of the jobs already processed but also on its scheduled position. This paper focuses on the single-machine scheduling problems with the objectives of minimizing the makespan, total completion time, total weighted completion time, discounted total weighted completion time, and maximum lateness based on the proposed model, respectively. It shows that they are polynomially solvable and optimal under certain conditions. Additionally, it presents some approximation algorithms based on the optimal schedules for the corresponding single-machine scheduling problems and analyzes their worst case error bound.

Parameter prediction is an important data mining problem and has many applications. Considering the difficulty for the conventional parameter prediction methods to deal with numeric and categorical inputs, this paper proposes a new Adaptive-Network-based Fuzzy Inference System (ANFIS)-based parameter prediction method that can well tackle such inputs. First, it introduces a Firing-strength Transform Matrix (FTM) into the generation mechanism of firing strengths of fuzzy rules in standard ANFIS in order that the categorical inputs can be handled. Next, a new training algorithm of the structural parameters in the premise/consequent parts of fuzzy rules and FTM in the new ANFIS is proposed. Moreover, to reduce the number of structural parameters to be learned in the new ANFIS with high-dimensional inputs, this paper presents a fuzzy c-means method based on a binary tree linear division method for identifying the structure of the new ANFIS. Then, numerical comparisons are made, and the results show that the performance of the new ANFIS has significant advantages over that of the Multilayer-Perceptron (MLP)-based parameter prediction method. Finally, the proposed method is applied to predict the trim-beam numbers in an industrial textile scheduling process.

Genetic programming (GP) has been rarely applied to scheduling problems. In this paper the use of GP to learn single-machine predictive scheduling (PS) heuristics with stochastic breakdowns is investigated, where both tardiness and stability objectives in face of machine failures are considered. The proposed bi-tree structured representation scheme makes it possible to search sequencing and idle time inserting programs integratedly. Empirical results in different uncertain environments show that GP can evolve high quality PS heuristics effectively. The roles of inserted idle time are then analysed with respect to various weighting objectives. Finally some guides are supplied for PS design based on GP-evolved heuristics.

Ranking and comparing fuzzy numbers is an important part in many fuzzy optimization problems such as intelligent control and manufacturing system production line scheduling with uncertainty environments. In this paper, based on the level characteristic function and α -average of level cut sets of fuzzy number, we establish the IM α -metric method for measuring fuzzy number as a whole, and introduce the concept of ID α -difference that describes the reliability of IM α -metric value. Further, the basic properties and the separability of IM α -metric and ID α -difference are discussed. Finally, we give a mathematical model to solve fuzzy optimization problems by means of IM α -metric.