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PPISB: A Novel Network-Based Algorithm of Predicting Protein-Protein Interactions With Mixed Membership Stochastic Blockmodel

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第2期20卷 1606-1612页

作者： Wang, Xiaojuan Yang, Wen Yang, Yue He, Yizhou Zhang, Jun Wang, Lusheng Hu, Lun Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430070 Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong 999077 Peoples R China Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830000 Peoples R China

Protein-protein interactions (PPIs) play an essential role for most of biological processes in cells. Many computational algorithms have thus been proposed to predict PPIs. However, most of them heavily rest on the biological information of proteins while ignoring the latent structural features of proteins presented in a PPI network. In this paper, we propose an efficient network-based prediction algorithm, namely PPISB, based on a mixed membership stochastic blockmodel. By simulating the generative process of a PPI network, PPISB is able to capture the latent community structures. The inference procedure adopted by PPISB further optimizes the membership distributions of proteins over different complexes. After that, a distance measure is designed to compute the similarity between two proteins in terms of their likelihoods of being in the same complex, thus verifying whether they interact with each other or not. To evaluate the performance of PPISB, a series of extensive experiments have been conducted with five PPI networks collected from different species and the results demonstrate that PPISB has a promising performance when applied to predict PPIs in terms of several evaluation metrics. Hence, we reason that PPISB is preferred over state-of-the-art network-based prediction algorithms especially for predicting potential PPIs.

关键词： Machine learning protein-protein interaction prediction PPI network stochastic blockmodel

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UPSST: Unsupervised Pathology Domain Identification by Integrating Tissue Morphology, Imputing and Clustering of Spatial Transcriptomics With GAT

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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ieee TRANSACTIONS ON computational biology AND bioinformatics 2025年第1期22卷 298-309页

作者： Chen, Guiyun Hong, Xiaoyan Zhang, Lei Ding, Longzhen Li, Lihua Jiang, Chunjuan Ni, Jianming Miao, Kai Pan, Xiang Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214126 Peoples R China Zhejiang Prov Peoples Hosp Dept Vasc Surg Bijie Hosp Bijie 551700 Guizhou Peoples R China Hangzhou Dianzi Univ Inst Biomed Engn & Instrumentat Hangzhou 310005 Peoples R China Fudan Univ Shanghai Canc Ctr Dept Nucl Med Shanghai 200032 Peoples R China Jiangnan Univ Dept Radiol Med Ctr Wuxi 214002 Peoples R China Univ Macau Fac Hlth Sci Canc Ctr Taipa 999078 Macau Peoples R China Shanghai Univ Med & Hlth Sci Shanghai Key Lab Mol Imaging Shanghai 201318 Peoples R China

Spatial transcriptomics is an emerging technology that allows for analysis of cellular and molecular heterogeneity at spatial resolution. The accurate identification of pathological regions in spatial transcriptomics data is essential for understanding tissue heterogeneity and disease progression. We introduce UPSST, a comprehensive framework that integrates tissue morphology, imputes gene expression, and clusters spatial regions using a graph attention neural network (GAT). UPSST was evaluated across multiple spatial transcriptomics datasets, achieving high performance, such as achieving an Adjusted Rand Index (ARI) of 0.737 and a Fowlkes-Mallows Index (FMI) of 0.818 on slice 151671 of the LIBD human dorsolateral prefrontal cortex (DLPFC) dataset. These results highlight the robustness and precision of our approach in identifying pathology domains. Additionally, UPSST facilitates downstream analyses such as differential and enrichment analysis, which are crucial for deriving biological insights. In conclusion, UPSST offers a powerful and reliable tool for spatial transcriptomics analysis, advancing the identification of pathological regions with high accuracy.

关键词： Transcriptomics Gene expression Spatial databases Feature extraction Vectors bioinformatics Autoencoders Sequential analysis Mutual information computational biology Spatial transcriptomics domain identification spatial heterogeneity imputation histology images graph attention neural network

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MKMGCN-DDI: Predicting Drug-Drug Interactions via Magnetic Graph Convolutional Network With Multiple Kernels

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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ieee TRANSACTIONS ON computational biology AND bioinformatics 2025年第2期22卷 469-480页

作者： Chen, Ming Pan, Yunhan Lei, Xiujuan Ji, Chunyan Dai, Yinglong Pan, Yi Hunan Normal Univ Dept Artificial Intelligence Changsha 410081 Hunan Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China BNU HKBU United Int Coll Comp Sci Dept Zhuhai 519087 Peoples R China Shenzhen Inst Adv Technol Shenzhen Key Lab Intelligent Bioinformat Shenzhen 518055 Peoples R China Shenzhen Univ Adv Technol Fac Comp Sci & Control Engn Shenzhen 518055 Peoples R China

Polypharmacy is a common means of clinical treatments, but detecting drug-drug interactions (DDIs) behind unexpected effects can be costly and faces clinical limitations. Recently, graph neural networks (GNNs) have demonstrated encouraging performance in predicting DDIs. However, most studies overlook the comprehensive aspects of DDIs, such as the coexistence of types of pharmacological changes and the asymmetric roles of drugs. In this article, we define new prediction tasks, taking into account both enhancive or depressive changes and the roles of drugs, and then establish spectral GNNs to predict comprehensive information of DDIs. First, we formally define several tasks, including joint prediction tasks designed to leverage both types and directions. These tasks deduce to sub-tasks in previous studies. Then, we propose a unified framework, the MKMGCN-DDI, via introducing two Magnetic Laplacian matrices to encode comprehension information within DDIs, defining multiple graph filters, and designing multiple-kernel based Magnetic graph convolutional networks (MKMGCN). Experiments across three datasets show that it not only has good adaptability to multiple tasks but also significantly improves results on simple tasks. Case studies on breast neoplasms and lung neoplasms verify its feasibility, as over half of top-10 items are supported.

关键词： Drugs Predictive models Magnetic separation Laplace equations bioinformatics Sodium Market research Graph convolutional networks Computer science computational modeling Drug-drug interactions directed graphs graph neural network magnetic Laplacians graph signal processing

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2020 ieee Conference on computational intelligence in bioinformatics and computational biology, CIBCB 2020

2020 IEEE Conference on Computational Intelligence in Bioinf...

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2020 ieee Conference on computational intelligence in bioinformatics and computational biology, CIBCB 2020

ISBN: (纸本)9781728194684

The proceedings contain 32 papers. The topics discussed include: a survey of loss functions for semantic segmentation;analysis of interacting factors contributing to the onset of diabetes type 2 in the female pima population: a step towards understanding the links;a closed-loop optimization framework for personalized cancer therapy design;dilated squeeze-and-excitation u-net for fetal ultrasound image segmentation;multiclass yeast segmentation in microstructured environments with deep learning;an ensemble deep transfer-learning approach to identify COVID-19 cases from chest x-ray images;using genetic programming to investigate a novel model of resting energy expenditure for bariatric surgery patients;and a computational model to analyze E/I (excitation/inhibition) dynamics for neural network integrated with astrocyte.

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A Cautionary Note on Poli's Stability Condition for Particle Swarm Optimization

A Cautionary Note on Poli's Stability Condition for Particle...

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2023 ieee symposium Series on computational intelligence, SSCI 2023

作者： Von Eschwege, Daniel H. Engelbrecht, Andries P. Stellenbosch University Department of Industrial Engineering Stellenbosch South Africa Stellenbosch University Department of Industrial Engineering and Computer Science Division Stellenbosch South Africa Gulf University for Science and Technology Center for Applied Mathematics and Bioinformatics Kuwait Kuwait

ISBN: (纸本)9781665430654

Particle swarm optimization (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. PSO performance depends on the use of a suitable control parameter (CP) configuration, which governs the trade-off between exploration and exploitation in the swarm. Various methods of adapting or tuning CPs exist, but many result in exploding particle velocities and an unstable search process. Poli's stability condition ensures convergence in the mathematical limit, and is often used to inform CP configuration. However, this study shows that since it does not place any practical convergence constraints, it cannot be used to guarantee a stable search process. Velocity explosion occurs nonetheless and can lead to floating-point overflow and numerical instability. The investigation into various CP configurations across diverse functions and measurements of particle velocities provides empirical evidence of velocity explosion, and cautions against the assumption that enforcing Poli's criterion guarantees stability. The findings underline the need for comprehensive understanding of CP tuning and stability conditions in PSO, as well as the crucial role of empirical evidence in evaluating the real-world performance of swarm intelligence algorithms. © 2023 ieee.

关键词： Particle swarm optimization Poli Stability condition Swarm intelligence

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SMOTE for gene regulatory network sampling

SMOTE for gene regulatory network sampling

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Gonzalo A. Ruz Nitesh V. Chawla Facultad de Ingeniería y Ciencias Universidad Adolfo Ibáñez Center of Applied Ecology and Sustainability (CAPES) Santiago Chile Department of Computer Science Lucy Family Institute for Data and Society University of Notre Dame Notre Dame IN USA

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

In many cases, the search for synthetic functional networks of a gene regulatory network model can be a difficult and time-consuming task. In this paper, we present a method that uses the popular SMOTE algorithm to sample synthetic functional networks in order to boost the results obtained by an evolutionary computation framework in a previous stage. We consider threshold Boolean networks for gene regulatory network modeling and apply the proposed method to the search for functional networks of the tryptophan operon in E. coli model. The results confirm the effectiveness of the proposed method by increasing the number of functional networks from fifteen, originally found by an evolutionary computation framework in more than nineteen hours, to twenty-nine in only a few minutes, allowing a more reliable characterization of the neutral space for the biological model.

关键词： Analytical models computational modeling Biological system modeling Escherichia coli Evolutionary computation Search problems Reliability bioinformatics computational biology

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APMG: 3D Molecule Generation Driven by Atomic Chemical Properties

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第6期21卷 2269-2279页

作者： Hua, Yang Feng, Zhenhua Song, Xiaoning Li, Hui Xu, Tianyang Wu, Xiao-Jun Yu, Dong-Jun Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

Recently, mask-fill-based 3D Molecular Generation (MG) methods have become very popular in virtual drug design. However, the existing MG methods ignore the chemical properties of atoms and contain inappropriate atomic position training data, which limits their generation capability. To mitigate the above issues, this paper presents a novel mask-fill-based 3D molecule generation model driven by atomic chemical properties (APMG). Specifically, we construct a new attention-MPNN-based encoder and introduce the electronic information into atom representations to enrich chemical properties. Also, a multi-functional classifier is designed to predict the electronic information of each generated atom, guiding the type prediction of elements and bonds. By design, the proposed method uses the chemical properties of atoms and their correlations for high-quality molecule generation. Second, to optimize the atomic position training data, we propose a novel atomic training position generation approach using the Chi-Square distribution. We evaluate our APMG method on the CrossDocked dataset and visualize the docking states of the pockets and generated molecules. The obtained results demonstrate the superiority and merits of APMG over the state-of-the-art approaches.

关键词： Atoms Chemicals Three-dimensional displays Vectors Training Proteins Feature extraction 3D molecule generation virtual drug design atomic chemical properties

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RedCDR: Dual Relation Distillation for Cancer Drug Response Prediction

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第5期21卷 1468-1479页

作者： Xu, Muhao Zhu, Zhenfeng Zhao, Yawei He, Kunlun Huang, Qinghua Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing 100044 Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Big Data Res Ctr Beijing 100853 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

Based on multi-omics data and drug information, predicting the response of cancer cell lines to drugs is a crucial area of research in modern oncology, as it can promote the development of personalized treatments. Despite the promising performance achieved by existing models, most of them overlook the variations among different omics and lack effective integration of multi-omics data. Moreover, the explicit modeling of cell line/drug attribute and cell line-drug association has not been thoroughly investigated in existing approaches. To address these issues, we propose RedCDR, a dual relation distillation model for cancer drug response (CDR) prediction. Specifically, a parallel dual-branch architecture is designed to enable both the independent learning and interactive fusion feasible for cell line/drug attribute and cell line-drug association information. To facilitate the adaptive interacting integration of multi-omics data, the proposed multi-omics encoder introduces the multiple similarity relations between cell lines and takes the importance of different omics data into account. To accomplish knowledge transfer from the two independent attribute and association branches to their fusion, a dual relation distillation mechanism consisting of representation distillation and prediction distillation is presented. Experiments conducted on the GDSC and CCLE datasets show that RedCDR outperforms previous state-of-the-art approaches in CDR prediction.

关键词： Cancer drug response (CDR) prediction graph convolutional networks knowledge distillation multi-omics

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SMGCN: Multiple Similarity and Multiple Kernel Fusion Based Graph Convolutional Neural Network for Drug-Target Interactions Prediction

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第1期21卷 143-154页

作者： Wang, Wei Yu, Mengxue Sun, Bin Li, Juntao Liu, Dong Zhang, Hongjun Wang, Xianfang Zhou, Yun Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Peoples R China Key Lab Artificial Intelligence & Personalized Lea Xinxiang 453007 Peoples R China Henan Normal Univ Sch Math & Informat Sci Xinxiang 453007 Peoples R China Henan Polytech Univ Hebi Inst Engn & Technol Hebi 458030 Peoples R China Henan Inst Technol Coll Comp Sci & Technol Xinxiang 453000 Henan Peoples R China

Accurately identifying potential drug-target interactions (DTIs) is a critical step in accelerating drug discovery. Despite many studies that have been conducted over the past decades, detecting DTIs remains a highly challenging and complicated process. Therefore, we propose a novel method called SMGCN, which combines multiple similarity and multiple kernel fusion based on Graph Convolutional Network (GCN) to predict DTIs. In order to capture the features of the network structure and fully explore direct or indirect relationships between nodes, we propose the method of multiple similarity, which combines similarity fusion matrices with Random Walk with Restart (RWR) and cosine similarity. Then, we use GCN to extract multi-layer low-dimensional embedding features. Unlike traditional GCN methods, we incorporate Multiple Kernel Learning (MKL). Finally, we use the Dual Laplace Regularized Least Squares method to predict novel DTIs through combinatorial kernels in drug and target spaces. We conduct experiments on a golden standard dataset, and demonstrate the effectiveness of our proposed model in predicting DTIs through showing significant improvements in Area Under the Curve (AUC) and Area Under the Precision-Recall Curve (AUPR). In addition, our model can also discover some new DTIs, which can be verified by the KEGG BRITE Database and relevant literature.

关键词： Drugs Kernel Proteins Predictive models Feature extraction Convolutional neural networks Compounds Drug discovery drug-target interactions graph convolutional neural network multiple similarity

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De Novo Drug Design by Multi-Objective Path Consistency Learning With Beam A* Search

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第6期21卷 2459-2470页

作者： Zhao, Dengwei Zhou, Jingyuan Tu, Shikui Xu, Lei Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Guangdong Inst Intelligence Sci & Technol Zhuhai 519031 Guangdong Peoples R China

Generating high-quality and drug-like molecules from scratch within the expansive chemical space presents a significant challenge in the field of drug discovery. In prior research, value-based reinforcement learning algorithms have been employed to generate molecules with multiple desired properties iteratively. The immediate reward was defined as the evaluation of intermediate-state molecules at each step, and the learning objective would be maximizing the expected cumulative evaluation scores for all molecules along the generative path. However, this definition of the reward was misleading, as in reality, the optimization target should be the evaluation score of only the final generated molecule. Furthermore, in previous works, randomness was introduced into the decision-making process, enabling the generation of diverse molecules but no longer pursuing the maximum future rewards. In this paper, immediate reward is defined as the improvement achieved through the modification of the molecule to maximize the evaluation score of the final generated molecule exclusively. Originating from the A* search, path consistency (PC), i.e., f values on one optimal path should be identical, is employed as the objective function in the update of the f value estimator to train a multi-objective de novo drug designer. By incorporating the f value into the decision-making process of beam search, the DrugBA* algorithm is proposed to enable the large-scale generation of molecules that exhibit both high quality and diversity. Experimental results demonstrate a substantial enhancement over the state-of-the-art algorithm QADD in multiple molecular properties of the generated molecules.

关键词： Beam search multi-objective optimization path consistency q learning de novo drug design

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