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26th International conference on Research in computational Molecular biology, RECOMB 2022

作者： Zhang, Ruochi Zhou, Tianming Ma, Jian Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9783031047480

The advent of high-throughput whole-genome mapping methods for the three-dimensional (3D) genome organization such as Hi-C has revealed distinct features of chromatin folding in various scales within the cell nucleus. These multiscale 3D genome features collectively contribute to vital genome functions such as transcription. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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DL2G: Anatomical Landmark Detection with Deep Local Features and Geometric Global Constraint

DL2G: Anatomical Landmark Detection with Deep Local Features...

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2024 ieee International conference on bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Rui Yang, Wanli Xiao, Kuntao Sun, Yi Sheng, Shurong Lv, Zhao Gao, Jiahong Auhui University Auhui Hefei China Hefei Comprehensive National Science Center Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei China Anhui Province Key Laboratory of Multimodal Cognitive Computation Auhui Hefei China Peking University Center for Mri Research Academy for Advanced Interdisciplinary Studies Beijing China Peking University Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Beijing China Peking University McGovern Institute for BrainResearch Beijing China

ISBN: (纸本)9798350386226

Anatomical landmark detection, a pivotal research area in medical image processing, holds immense value in surgical navigation, image registration, and related fields. Traditional machine learning methods struggle with generalization and robustness. Current supervised end-to-end approaches lacks in-terpretability in capturing global information, and GPU memory constraints restrict their application to extensive 3D medical image datasets. Moreover, existing methods overlook intrinsic geometric cues within images and point sets. Herein, we introduce a novel anatomical landmark detection framework that integrates deep learning's representation capabilities with global geometric information derived from images and point sets (DL2G). This approach facilitates anatomical landmark localization in a local-to-global fashion. Initially, we train a deep feature descriptor based on self-supervised contrastive learning, which is further used to screen candidate points for the target one by comparing local patch embeddings. Subsequently, geometric constraints constructed from labeled template point sets enable the selection of geometrically consistent matching point sets. Ultimately, the framework utilizes the global mutual information of images to perform landmark localization through iterative optimization. Adhering to the AFID data annotation protocol, we evaluated our method on two public MRI head datasets, OASIS and HCP, for detecting 32 anatomical landmarks. The results convincingly demonstrate the superiority of our method over current state-of-the-art approaches in terms of both accuracy and computational efficiency. © 2024 ieee.

关键词： Contrastive Learning

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An Effective Swarm intelligence Optimization Algorithm for Flexible Ligand Docking

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第5期19卷 2672-2684页

作者： Li, Chao Sun, Jun Li, Li-Wei Wu, Xiaojun Palade, Vasile Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Jiangsu Peoples R China Coventry Univ Fac Engn & Comp Coventry CV1 5FB W Midlands England

In general, flexible ligand docking is used for docking simulations under the premise that the position of the binding site is already known, and meanwhile it can also be used without prior knowledge of the binding site. However, most of the optimization search algorithms used in popular docking software are far from being ideal in the first case, and they can hardly be directly utilized for the latter case due to the relatively large search area. In order to design an algorithm that can flexibly adapt to different sizes of the search area, we propose an effective swarm intelligence optimization algorithm in this paper, called diversity-controlled Lamarckian quantum particle swarm optimization (DCL-QPSO). The highlights of the algorithm are a diversity-controlled strategy and a modified local search method. Integrated with the docking environment of Autodock, the DCL-QPSO is compared with Autodock Vina, Glide and other two Autodock-based search algorithms for flexible ligand docking. Experimental results revealed that the proposed algorithm has a performance comparable to those of Autodock Vina and Glide for dockings within a certain area around the binding sites, and is a more effective solver than all the compared methods for dockings without prior knowledge of the binding sites.

关键词： Optimization Particle swarm optimization Search problems Software algorithms Proteins Birds Trajectory Flexible ligand docking search algorithm quantum particle swarm optimization diversity-controlled strategy solis and wets local search autodock

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Temporal Group Attention Network with Affective Complementary Learning for Gait Emotion Recognition

Temporal Group Attention Network with Affective Complementar...

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2024 ieee International conference on bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Ziyi Chen, C. L. Philip Liu, Haiqi Zhang, Tong School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Lab Guangzhou China Guangdong Provincial Key Laboratory of Computational Ai Models and Cognitive Intelligence China Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human China

ISBN: (纸本)9798350386226

Skeleton-based methods in Gait Emotion Recognition (GER) that abstract gait as a spatial-temporal graph in a non-Euclidean space have achieved remarkable success. However, existing studies neglect capturing crucial spatial-temporal implicit dependencies and make insufficient use of manually crafted affective complementary information. In this paper, we propose a novel Temporal Group Attention Network with Affective Complementary Learning (T2A). Specifically, we propose a Temporal Group Attention (TGA) module, which captures spatial-temporal implicit dependencies and crucial features across space-time. Moreover, we design an affective complementary learning strategy, which first introduces an Affective Denoising (AD) module to reduce the impact of noise in weak emotion-related information and learn augmented affective features. Then, we adopt a decision-level fusion of the denoised affective features with the deep skeleton features to compensate for the discrepancies between different feature spaces. Experimental results on Emotion-Gait and EMOGAIT demonstrate that our proposed method significantly outperforms state-of-the-art methods. © 2024 ieee.

关键词： Contrastive Learning

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A Multi-objective Cluster-based Biased Random-Key Genetic Algorithm with Online Parameter Control Applied to Protein Structure Prediction 17

A Multi-objective Cluster-based Biased Random-Key Genetic Al...

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17th conference on Computer Science and intelligence Systems (FedCSIS)

作者： Marchi, Felipe Parpinelli, Rafael Stubs Santa Catarina State Univ Appl Comp Grad Program Joinville SC Brazil

ISBN: (数字)9788396242396

ISBN: (纸本)9788396242396

The protein structure prediction problem is one of the most important bioinformatics problems. computational methods can be used to approach this problem and de novo methods are able to generate protein structures without the need of having known similar structures to the predicted protein. These methods transform the prediction problem into an optimization problem, using optimization models that combine different energy functions and high-level information. These models usually have only a single optimization objective. However, it is known that this single objective optimization approach may harm the optimization search due to the existence of conflicts between the different terms that compose the optimization objective. The proposed model has three objectives: energy function, secondary structure, and contact maps. A multi-objective Biased RandomKey Genetic Algorithm (BRKGA) with online parameter control, named MOBO, is proposed as the optimizer. The final predictor comprises two phases of the MOBO algorithm and selects a final structure using the MUFOLD-CL clustering method. Results obtained demonstrated that the proposed predictor generated highly competitive results with the literature.

关键词： bioinformatics Multi-objective Optimization Evolutionary Computation Clustering Online Parameter Tuning Protein Structure Prediction

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LCCH:A low computational complexity hybrid model based on the half-router attention for biopharmaceutical indicators prediction

LCCH:A low computational complexity hybrid model based on th...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yichen Song Chenxi Xia Simengxu Qiao Changdi Li Qunshan He Xinggao Liu Zhejiang University

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

AI4Biopharmaceutical is an important field of interest in both academia and industry. Due to the characteristics of biopharmaceutical industry data and the hardware limitation of real factories, biopharmaceutical indicators prediction models need to balance computational complexity and prediction effectiveness. We propose a low computational complexity hybrid model (LCCH) combing the biopharmaceutical mechanism with artificial intelligence for biopharmaceutical indicators prediction. We use the Doolittle method to reduce the computational complexity of the online decomposition phase to O(1), while employing the half-router attention incorporating biopharmaceutical mechanism to significantly reduce the computational complexity of the prediction phase. The model achieves the state-of-the-art results for the prediction of five biopharmaceutical indicators: amino nitrogen, reducing sugar, total sugar, bacterial concentration,and viscosity in the erythromycin pharmaceutical scenario. Compared to the other outstanding baseline models from the last three years, LCCH demonstrates the superiority and potential of the hybrid model for biopharmaceutical industrial applications.

关键词： Viscosity Microorganisms computational modeling Biological system modeling Predictive models Sugar industry Production facilities Nitrogen computational complexity Pharmaceuticals

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SSGCN: A Spatial Domain Identification Framework Based on Graph Convolutional Neural Network with Adaptively Reweighting Edges

SSGCN: A Spatial Domain Identification Framework Based on Gr...

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ieee International conference on bioinformatics and computational biology (ICBCB)

作者： Wenhui Du Chunyang Chai Yujie Gu Shuang Hao Huiyan Sun School of Artificial Intelligence JiLin University Changchun China

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

Leveraging spatial location and gene expression of spots for spatial domain identification through spatial transcriptomics data is important for understanding the tissue heterogeneity, and comprehending developmental processes. In this study, we propose a spatial domain identification framework SSGCN based on graph neural network, which fully considers spatial locations and similarity between spots according to their gene expression. Specifically, SSGCN takes spatial location and gene expression as input, reassigns edges' weights of graph by calculating the expression similarity between nodes, and ultimately learns a low-dimensional representation for spatial domain assignment. We conduct extensive experiments on four datasets from various platforms with different resolutions. The experimental results show that SSGCN is superior to other spatial clustering methods, and also demonstrate its cross-platform applicability.

关键词： Runtime Convolution Transcriptomics Olfactory Graph neural networks Spatial databases Breast cancer Organisms Gene expression Spatial resolution

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Determination of Neuron Activation States Facilitated by Artificial intelligence

Determination of Neuron Activation States Facilitated by Art...

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2022 ieee International conference on bioinformatics and Biomedicine, BIBM 2022

作者： Topasna, Ana Xie, Eric Park, Sammy Huang, Sophia Wu, Xiaowei Xie, Hehuang Epigenomics and Computational Biology Lab Fralin Life Sciences Institute Virginia Tech BlacksburgVA24061 United States Department of Biomedical Sciences and Pathobiology Virginia-Maryland College of Veterinary Medicine Virginia Tech BlacksburgVA24061 United States Department of Biology Department of Psychology Reed College PortlandOR97202 United States Department of Computer Science University of Virginia CharlottesvilleVA22903 United States Department of Engineering Systems and Environment University of Virginia CharlottesvilleVA22903 United States Department of Statistics Virginia Tech BlacksburgVA24061 United States

ISBN: (纸本)9781665468190

Environmental stimuli evoke gene expression changes in neurons to achieve synaptic reconfiguration and memory formation. During the neuronal activation process, immediate early genes (IEGs) and late response genes (LRGs) have been defined according to their temporal changes in gene expression. Recent single cell studies indicated that different subtypes of neurons may have distinct sets of IEGs and LRGs in response to neuronal activation. Currently, it remains unexplored whether the gene expression patterns provided by single cell studies are sufficient to distinguish neuronal subtypes together with their activation states. In this study, we adopt Artificial Neural Network (ANN) models to predict neuron cell subtypes and activation states simultaneously based on a single cell RNA-Seq dataset generated from mouse visual cortex in response to light stimulus exposure. Four kinds of ANN architectures were designed to compare the prediction efficacies using two kinds of inputs: the full list of genes or the markers selected for neuron cell subtypes, IEGs, and LRGs. We observed that, with single-cell RNA-Seq data, neuron cell subtypes and activation states can be predicted accurately by ANN models. Moreover, compared to feeding the entire list of genes to fully connected ANNs, using selected marker genes and partially connected ANNs achieves comparable prediction accuracy with substantially improved computational efficiency. Altogether, our results demonstrated that ANNs are powerful in predicting neuron activation states and cell subtypes and may serve as a useful data mining approach to explore single cell RNA-Seq profiles. © 2022 ieee.

关键词： RNA

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Flatter Minima of Loss Landscapes Correspond with Strong Corruption Robustness 27th

Flatter Minima of Loss Landscapes Correspond with Strong C...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Zhong, Liqun Zhu, Kaijie Yang, Ge School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Science Beijing100190 China

ISBN: (纸本)9783031781063

To develop deep neural networks (DNNs) that maintain strong performance under different natural image corruptions such as noise and blur, namely with strong corruption robustness, is essential for real-world applications. Current methods to enhance corruption robustness focus primarily on data augmentation. But these methods are often empirically designed without a clear theoretical foundation. Here, we show that flatter minima of loss landscapes generally correspond with stronger corruption robustness. Based on this finding, we have developed a method named LASAM-DA that combines layer adaptive sharpness-aware minimization(LASAM) with data augmentation (DA) to boost corruption robustness. LASAM pushes DNNs towards flat minima of loss landscapes, while DA guides DNNs to memorize diverse data distributions. LASAM and DA reinforce each other without interference. Experiments on CIFRA-10-C, CIFAR-100-C and Tiny-ImageNet-C show that LASAM-DA outperforms competing methods and achieves state-of-the-art performance. LASAM-DA is versatile, not restricted to specific data types, network architectures or data augmentation methods. Code is openly accessible at https://***/cbmi-group/Flatter-Minima-Helps-for-Robustness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

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ieee TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE intelligence 2022年第10期44卷 7112-7127页

作者： Elnaggar, Ahmed Heinzinger, Michael Dallago, Christian Rehawi, Ghalia Wang, Yu Jones, Llion Gibbs, Tom Feher, Tamas Angerer, Christoph Steinegger, Martin Bhowmik, Debsindhu Rost, Burkhard Tech Univ Munich TUM Dept Informat Bioinformat & Computat Biol i12 D-85748 Garching Germany Med AI Technol Wu Xi Ltd Wuxi 214000 Jiangsu Peoples R China Google Google AI Mountain View CA 94043 USA NVIDIA Santa Clara CA 95051 USA Seoul Natl Univ Sch Biol Sci Seoul 08826 South Korea Oak Ridge Natl Lab POB 2009 Oak Ridge TN 37830 USA

computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The protein LMs (pLMs) were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%);(2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=81%) and membrane versus water-soluble (2-state accuracy Q2=91%). For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that pLMs learned some of the grammar of the language of life. All our models are available through https://***/agemagician/ProtTrans.

关键词： Proteins Training Amino acids Task analysis Databases computational modeling Three-dimensional displays computational biology high performance computing machine learning language modeling deep learning

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