With the growing demand for face editing applications, face inpainting has become an increasingly important subfield within image inpainting research. While many existing methods use semantic segmentation guidance, they apply uniform weighting across all regions of the map. Since the missing areas of the image lack meaningful features, this uniform treatment provides insufficient guidance in missing areas, leading to unrealistic or structurally incoherent results. Moreover, these methods generally lack the capacity to adapt inpainting results to individual user preferences, thereby limiting their effectiveness in personalized face editing. To address these limitations, we propose SegPainter, a Mamba-based architecture for user-controllable face inpainting that enables customized restoration guided by user-defined semantic segmentation maps generated using the Image Segmentation Annotation Tool (ISAT) integrated with Meta's Segment Anything Model (SAM). Specifically, we propose Hard Mask Soft One-Hot Encoding (HMSOE) to adaptively weight regions in the segmentation map based on whether they correspond to known or missing areas of the masked image. This strategy amplifies semantic guidance in missing regions while attenuating it in known regions to avoid over-constraining existing content. We further introduce Semantic-Guided State Space Model (SG-SSM) to dynamically modulate the Mamba layer with semantic features, adapting guidance to the masked image. To enhance the quality of inpainting results, we also propose Tri-Scan Inspection (TSI), a scanning mechanism designed to capture both global and local dependencies while preserving spatial continuity and facial structure. Extensive experiments on the CelebAMask-HQ and FFHQ datasets demonstrate that our framework outperforms state-of-the-art methods, producing sharper and more semantically consistent face inpainting results.

Contrastive clustering performs clustering and data representation in a unified model, where instance- and cluster-level constrastive learning are conducted simultaneously. However, commonly-used data augmentation methods make contrastive mechanism effect but may cause representation learning getting stuck in domain-specific information, which further deteriorates clustering performance and limits generalization ability. To this end, we propose a new framework, named Generalized Contrastive Clustering with domain shifts modeling (GeCC), which can integrate diverse domain knowledge to improve the clustering performance. Specifically, we first design a cluster-guided domain shifts modeling module to synthesize a reference view with diverse domain information. Then, we introduce instance representation and cluster assignment contrastive modules with well-designed attention weights to guide the representation learning and clustering. In this way, our method can maximize the extraction of cluster-related information and avoid over-fitting domain-specific features. Experimental results on four benchmark datasets demonstrate that our proposed method consistently outperforms other state-of-the-art methods.

With a burgeoning number of artificial intelligence (AI) applications in various fields, biomolecular science has also given a big welcome to advanced AI techniques in recent years. In this broad field, scoring a protein-ligand binding structure to output the binding strength is a crucial problem that heavily relates to computational drug discovery. Aiming at this problem, we have proposed an efficient scoring framework using deep learning techniques. This framework describes a binding structure by a high-resolution atomic graph, places a focus on the inter-molecular interactions and learns the graph in a rational way. For a protein-ligand binding complex, the generated atomic graph reserves key information of the atoms (as graph nodes), and focuses on inter-molecular interactions (as graph edges) that can be identified by introducing multiple distance ranges to the atom pairs within the binding area. To provide more confidence in the predicted binding strengths, we have interpreted the deep learning model from the model level and in a post-hoc analysis. The proposed learning framework has been demonstrated to have competitive performance in scoring and screening tasks, which will prospectively promote the development of related fields further.

Multi-label ensemble can achieve superior performance on multi-label learning problems by integrating a number of base classifiers. In existing multi-label ensemble methods, the base classifiers are usually trained with the same original features; it is difficult for each base classifier to capture label-relevant or label subset-relevant information. Meanwhile, the manually designed integrating strategies cannot automatically distinguish the importance of the base classifiers, which also lack flexibility and scalability. In order to resolve these problems, this paper proposes a new multi-label ensemble framework, named Attention-aware Multi-label Ensemble with Label Subset-specIfic FeaTures (AME-LSIFT). It utilizes$c$-means clustering to produce Label Subset-specIfic FeaTures (LSIFT), constructs a neural network based model for each label subset, and integrates the base models with a dynamic and automatic attention-aware mechanism. Moreover, an objective function that considers both the label subset accuracy and ensemble accuracy is developed for training the proposed AME-LSIFT. Experiments conducted on ten benchmark datasets demonstrate the superior performance of the proposed method compared with state-of-the-art approaches.

Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free energy-based simulations and machine learning-based scoring functions can potentially provide accurate predictions. In this paper, we review these two classes of methods, following a number of thermodynamic cycles for the free energy-based simulations and a feature-representation taxonomy for the machine learning-based scoring functions. More recent deep learning-based predictions, where a hierarchy of feature representations are generally extracted, are also reviewed. Strengths and weaknesses of the two classes of methods, coupled with future directions for improvements, are comparatively discussed.

MOTIVATION: Reliable predictive models of protein-ligand binding affinity are required in many areas of biomedical research. Accurate prediction based on current descriptors or molecular fingerprints (FPs) remains a challenge. We develop novel interaction FPs (IFPs) to encode protein-ligand interactions and use them to improve the prediction. RESULTS: Proteo-chemometrics IFPs (PrtCmm IFPs) formed by combining extended connectivity fingerprints (ECFPs) with the proteo-chemometrics concept. Combining PrtCmm IFPs with machine-learning models led to efficient scoring models, which were validated on the PDBbind v2019 core set and CSAR-HiQ sets. The PrtCmm IFP Score outperformed several other models in predicting protein-ligand binding affinities. Besides, conventional ECFPs were simplified to generate new IFPs, which provided consistent but faster predictions. The relationship between the base atom properties of ECFPs and the accuracy of predictions was also investigated. AVAILABILITY: PrtCmm IFP has been implemented in the IFP Score Toolkit on github (https://github.com/debbydanwang/IFPscore). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Graphical models have been widely used to learn the conditional dependence structures among random variables. In many controlled experiments, such as the studies of disease or drug effectiveness, learning the structural changes of graphical models under two different conditions is of great importance. However, most existing graphical models are developed for estimating a single graph and based on a tacit assumption that there is no missing relevant variables, which wastes the common information provided by multiple heterogeneous data sets and underestimates the influence of latent/unobserved relevant variables. In this paper, we propose a joint differential network analysis (JDNA) model to jointly estimate multiple differential networks with latent variables from multiple data sets. The JDNA model is built on a penalized D-trace loss function, with group lasso or generalized fused lasso penalties. We implement a proximal gradient-based alternating direction method of multipliers to tackle the corresponding convex optimization problems. Extensive simulation experiments demonstrate that JDNA model outperforms state-of-the-art methods in estimating the structural changes of graphical models. Moreover, a series of experiments on several real-world data sets have been performed and experiment results consistently show that our proposed JDNA model is effective in identifying differential networks under different conditions.

We explore the drug resistance mechanism in non-small cell lung cancer treatment by characterizing the drug-binding site of a protein mutant based on local surface and energy features. These features are transformed to an eigen-binding site space and used for drug resistance level prediction and analysis.

Recently, machine learning techniques have become an indispensable alternative for computational studies of cancers and efficient prediction of cancer-drug responses or drug resistance levels. Meanwhile, in cancer characterization, molecular dynamics (MD) simulations can greatly reveal the dynamic and functional features of cancer-related proteins. In our work, MD simulations were implemented to extract the EGFR TK mutation (dynamic) features of a non-small-cell lung cancer (NSCLC)-patient group. Specifically, the relative positions of a drug-binding site and a drug molecule in the dynamics-trajectory were calculated and used for characterizing the dynamic features. These derived features, couples with patient personal features, were subsequently handled by a model called SFABSRM, which combines Supervised Factor Analysis and Softmax Regression Model. SFABSRM first uses factor analysis to evaluate the contributions of the selected features, and in our analysis it suggested that dynamic features play an important role in correlating with the cancer-drug responses. Further, SFABSRM applies the regression model for a drug response prediction, which further verified the important contribution of dynamic characteristics to this prediction. The support vector machine (SVM) model was conducted as a comparison with SFABSRM, leading to an agreement with the earlier conclusion. Overall, these studies can greatly benefit the NSCLC studies and drug discovery.

BACKGROUND: Epidermal growth factor receptor (EGFR) mutation-induced drug resistance has caused great difficulties in the treatment of non-small-cell lung cancer (NSCLC). However, structural information is available for just a few EGFR mutants. In this study, we created an EGFR Mutant Structural Database (freely available at http://bcc.ee.cityu.edu.hk/data/EGFR.html ), including the 3D EGFR mutant structures and their corresponding binding free energies with two commonly used inhibitors (gefitinib and erlotinib). RESULTS: We collected the information of 942 NSCLC patients belonging to 112 mutation types. These mutation types are divided into five groups (insertion, deletion, duplication, modification and substitution), and substitution accounts for 61.61% of the mutation types and 54.14% of all the patients. Among all the 942 patients, 388 cases experienced a mutation at residue site 858 with leucine replaced by arginine (L858R), making it the most common mutation type. Moreover, 36 (32.14%) mutation types occur at exon 19, and 419 (44.48%) patients carried a mutation at exon 21. In this study, we predicted the EGFR mutant structures using Rosetta with the collected mutation types. In addition, Amber was employed to refine the structures followed by calculating the binding free energies of mutant-drug complexes. CONCLUSIONS: The EGFR Mutant Structural Database provides resources of 3D structures and the binding affinity with inhibitors, which can be used by other researchers to study NSCLC further and by medical doctors as reference for NSCLC treatment.

An important way to improve the performance of naive Bayesian classifiers (NBCs) is to remove or relax the fundamental assumption of independence among the attributes, which usually results in an estimation of joint probability density function (p.d.f.) instead of the estimation of marginal p.d.f. in the NBC design. This paper proposes a non-naive Bayesian classifier (NNBC) in which the independence assumption is removed and the marginal p.d.f. estimation is replaced by the joint p.d.f. estimation. A new technique of estimating the class-conditional p.d.f. based on the optimal bandwidth selection, which is the crucial part of the joint p.d.f. estimation, is applied in our NNBC. Three well-known indexes for measuring the performance of Bayesian classifiers, which are classification accuracy, area under receiver operating characteristic curve, and probability mean square error, are adopted to conduct a comparison among the four Bayesian models, i.e., normal naive Bayesian, flexible naive Bayesian (FNB), the homologous model of FNB (FNBROT), and our proposed NNBC. The comparative results show that NNBC is statistically superior to the other three models regarding the three indexes. And, in the comparison with support vector machine and four boosting-based classification methods, NNBC achieves a relatively favorable classification accuracy while significantly reducing the training time.

Mutations in EGFR kinase domain can cause non-small-cell lung cancer, which is one of the most lethal diseases in the world. However, current therapy is limited by the drug resistance effect in different EGFR mutants. There is an urgent demand for developing computational methods to predict drug resisted mutations. In this study, we use quantum mechanics and molecular mechanics models to generate EGFR mutants, and apply molecular dynamic to simulate EGFR-drug interactions. Hydrogen bonds and binding free energy are used to reveal the underlying principle of drug resistance in EGFR. The results show that drug resisted mutants do not establish hydrogen bond between the drug and the protein molecule while having large binding free energy. These properties can be used to predict resistance to anti-EGFR drugs due to protein mutations.

In this paper, we present a method to analyze the molecular structure and dynamics of nucleosomes, which are the basic unit in eukaryotic cells. In nucleosomal DNA chains, periodic dinucleotides are key patterns in protein-DNA interactions. This paper aims at revealing the significances of these periodic dinucleotides in the nucleosomal DNA motions. Normal mode analysis (NMA) is used to detect significant structural deformations of nucleosomal DNA. We have found that periodic dinucleotides are usually located at the peaks or valleys of DNA and protein motions, revealing they dominate the nucleosome dynamics. Also, a specific dinucleotide pattern CA/TG appears most frequently.