Alireza Nasiri

New York Structural Biology Center

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Jianjun Hu

University of South Carolina

Yong Zhao

Southwest Jiaotong University

Zhonghao Liu

Second Hospital of Shandong University

Yuxin Cui

First Affiliated Hospital of Xi'an Jiaotong University

Yuqi Song

Australian Regenerative Medicine Institute

Steph-Yves Louis

University of South Carolina

Ansi Zhang

Guizhou University

Zheng Xiong

Singapore Management University

Edirisuriya M. Dilanga Siriwardane

University of Colombo

Jing Jin

National Clinical Research Center for Digestive Diseases

Cooperative Institutions

University of South Carolina

Islamic Azad University of Damavand

Islamic Azad University, Tehran

Islamic Azad University, Science and Research Branch

Amirkabir University of Technology

Chinese Academy of Sciences

New York Structural Biology Center

Anhui University

Zhejiang University

Sun Yat-sen University

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NODE-SELECT: A FLEXIBLE GRAPH NEURAL NETWORK BASED ON REALISTIC PROPAGATION SCHEME

Steph-Yves Louis Alireza Nasiri Fatima Christina Rolland Cameron Mitro Jianjun Hu

While there exists a wide variety of graph neural networks (GNN) for node classification, only a minority of them adopt effective mechanisms to propagate the nodes' information with respect to these nodes' global importance. Additionally, two very important challenges that still significantly affect graph neural networks are the over-fitting and over-smoothing issues. Essentially, both issues cause poor generalization of the model and much poorer node classification performance. In this paper we propose the NODE-SELECT graph neural network (NSGNN): a novel and flexible graph neural network that uses subsetting filters to learn the contribution from the nodes selected to share their information. For the selected nodes, the way their learned information propagates resembles that of actual networks of the real world; where only a subset of nodes simultaneously share information. With the ability to manipulate the message passing operations through the use of numerous ensembled filters, our NODE-SELECT graph neural network is able to address the over-fitting problem and by-pass the over-smoothing challenge for graph neural networks. Furthermore, we also propose an efficient and informative measure named MICS to quantify the over-smoothing problem. Our NODE-SELECT achieved or matched state-of-the art results in a number of transductive experiments over different benchmark datasets.

Smoothing

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A new wavelet based blind audio source separation using Kurtosis

M.R. Mirarab M.A. Sobhani Alireza Nasiri

We consider the problem of blind audio source separation. A method to solve this problem is blind source separation (BSS) using independent component analysis (ICA). ICA exploits the non-Gaussianity of source in the mixtures. In this paper we propose a new wavelet based ICA method using Kurtosis for blind audio source separation. In this method, the observations are transformed into an adequate representation using wavelet packets decomposition and Kurtosis criterion. We consider instantaneous mixture of four sources. The results of performance measures show a considerable improvement when compared to FastICA and similar method.

Kurtosis

FastICA

Source Separation

10.1109/icacte.2010.5578955

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DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction

bioRxiv (Cold Spring Harbor Laboratory) (2018)

Zhonghao Liu Yuxin Cui Zheng Xiong Alireza Nasiri Ansi Zhang

Abstract Interactions between human leukocyte antigens (HLAs) and peptides play a critical role in the human immune system. Accurate computational prediction of HLA-binding peptides can be used for peptide drug discovery. Currently, the best prediction algorithms are neural network based pan-specific models, which take advantage of the large amount of data across HLA alleles. However, current pan-specific models are all based on the pseudo sequence encoding for modeling the binding context and depend on the available HLA protein-peptide bound structures. In this work, we proposed a novel deep convolutional neural network model (DCNN) for HLA-peptide binding prediction, in which the encoding of the HLA sequence and the binding context are both learned by the network itself without requiring the HLA-peptide bound structure information. Our DCNN model is also characterized by its binding context extraction layer and dual outputs with both binding affinity output and binding probability outputs. Evaluation on public benchmark datasets shows that our DeepSeqPan model without HLA structural information in training achieves state-of-the-art performance on a large number of HLA alleles with good generalization capability. Since our model only needs raw sequences from the HLA-peptide binding pairs, it can be applied to binding predictions of HLAs without structure information and can also be applied to other protein binding problems such as protein-DNA and protein-RNA bindings. The implementation code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPan .

Benchmark (surveying)

10.1101/299412

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SO(3)-equivariant neural networks from cryo-EM particle picking

Acta Crystallographica Section A Foundations and Advances (2023)

Darnell Granberry Alireza Nasiri Jianzhong Shou Tristan Bepler

Equivariant map

Particle (ecology)

10.1107/s2053273323092161

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Optimum Speed Calculation for Electrical Machine Optimization in Variable Speed Applications

Research Journal of Applied Sciences Engineering and Technology (2014)

Alireza Nasiri B. Abdi S. M. M. Mirtalaei Mohammad Ghiasi

Electro-Mechanical Batteries have important advantages compared with chemical batteries, especially in Low Earth Orbit satellites applications. High speed, slot-less, external rotor, permanent magnet machines are used in these systems as Motor/Generator. Commonly, optimizations of electrical machines are done in constant, nominal speed. But Electro-Mechanical Batteries are variable speed system. The efficiency is variable versus time, so, optimization in each single speed cannot necessarily cause the overall efficiency optimization. In this study, iron and copper losses are formulated versus speed variations of rotor. Then, for energy optimization, the optimization speed is achieved by equalizing the average iron loss and copper loss over a period of charge and discharge.

Copper loss

10.19026/rjaset.7.332

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DeepSeqPanII: an interpretable recurrent neural network model with attention mechanism for peptide-HLA class II binding prediction

bioRxiv (Cold Spring Harbor Laboratory) (2019)

Zhonghao Liu Jing Jin Yuxin Cui Zheng Xiong Alireza Nasiri

Abstract Human leukocyte antigen (HLA) complex molecules play an essential role in immune interactions by presenting peptides on the cell surface to T cells. With significant progress in deep learning, a series of neural network based models have been proposed and demonstrated with their good performances for peptide-HLA class I binding prediction. However, there still lack effective binding prediction models for HLA class II protein binding with peptides due to its inherent challenges. In this work, we present a novel sequence-based pan-specific neural network structure, DeepSeaPanII, for peptide-HLA class II binding prediction. Compared with existing pan-specific models, our model is an end-to-end neural network model without the need for pre- or post-processing on input samples. Besides state-of-the-art peformance in binding affinity prediction, DeepSeqPanII can also extract biological insight on the binding mechanism over the peptide and HLA sequences by its attention mechanism based binding core prediction capability. The leave-one-allele-out cross validation and benchmark evaluation results show that our proposed network model achieved state-of-the-art performance in HLA-II peptide binding. The source code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPanII .

Benchmark (surveying)

10.1101/817502

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SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification

arXiv (Cornell University) (2021)

Alireza Nasiri Jianjun Hu

Environmental Sound Classification (ESC) is a challenging field of research in non-speech audio processing. Most of current research in ESC focuses on designing deep models with special architectures tailored for specific audio datasets, which usually cannot exploit the intrinsic patterns in the data. However recent studies have surprisingly shown that transfer learning from models trained on ImageNet is a very effective technique in ESC. Herein, we propose SoundCLR, a supervised contrastive learning method for effective environment sound classification with state-of-the-art performance, which works by learning representations that disentangle the samples of each class from those of other classes. Our deep network models are trained by combining a contrastive loss that contributes to a better probability output by the classification layer with a cross-entropy loss on the output of the classifier layer to map the samples to their respective 1-hot encoded labels. Due to the comparatively small sizes of the available environmental sound datasets, we propose and exploit a transfer learning and strong data augmentation pipeline and apply the augmentations on both the sound signals and their log-mel spectrograms before inputting them to the model. Our experiments show that our masking based augmentation technique on the log-mel spectrograms can significantly improve the recognition performance. Our extensive benchmark experiments show that our hybrid deep network models trained with combined contrastive and cross-entropy loss achieved the state-of-the-art performance on three benchmark datasets ESC-10, ESC-50, and US8K with validation accuracies of 99.75\%, 93.4\%, and 86.49\% respectively. The ensemble version of our models also outperforms other top ensemble methods. The code is available at https://github.com/alireza-nasiri/SoundCLR.

Spectrogram

Transfer of learning

10.48550/arxiv.2103.01929

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Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

Applied Sciences (2020)

Steph-Yves M. Louis Alireza Nasiri Jingjing Bao Yuxin Cui Yong Zhao

Prognosis techniques for prediction of remaining useful life (RUL) are of crucial importance to the management of complex systems for they can lead to appropriate maintenance interventions and improvements in reliability. While various data-driven methods have been introduced to predict the remaining useful life (RUL) of machinery systems or batteries, no research has been reported on the remaining useful strength (RUS) prediction of silicon carbide fiber reinforced silicon carbide matrix (SiCf-SiCm) materials with pivotal role in its potential usage as a structural material in nuclear reactors and turbine engines. Knowledge of its degradation process is of the utmost importance to the manufacturers. For this purpose, two approaches based on the machine-learning techniques of random-forest (RF) and convolutional neural network (CNN) are proposed to predict the RUS of SiCf-SiCm using only acoustic emission (AE) signals generated during the material’s stress applying process. Experimental results show that the CNN models achieved better predictive performance than the RF models but the latter with expert-engineered features achieves better prediction for AE signals in the early stage of degradation. Additionally, our results demonstrate that both models can correctly predict the SiCf-SiCm RUS as evaluated by our robust testing method from which the best average root mean square error (RMSE) and Pearson correlation coefficient of 3.55 ksi units and 0.85 were obtained.

Acoustic Emission

10.3390/app10082680

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Citations (10)

Active learning based generative design for the discovery of wide bandgap materials

arXiv (Cornell University) (2021)

Rui Xin Edirisuriya M. Dilanga Siriwardane Yuqi Song Yong Zhao Steph-Yves Louis

Active learning has been increasingly applied to screening functional materials from existing materials databases with desired properties. However, the number of known materials deposited in the popular materials databases such as ICSD and Materials Project is extremely limited and consists of just a tiny portion of the vast chemical design space. Herein we present an active generative inverse design method that combines active learning with a deep variational autoencoder neural network and a generative adversarial deep neural network model to discover new materials with a target property in the whole chemical design space. The application of this method has allowed us to discover new thermodynamically stable materials with high band gap (SrYF$_5$) and semiconductors with specified band gap ranges (SrClF$_3$, CaClF$_5$, YCl$_3$, SrC$_2$F$_3$, AlSCl, As$_2$O$_3$), all of which are verified by the first principle DFT calculations. Our experiments show that while active learning itself may sample chemically infeasible candidates, these samples help to train effective screening models for filtering out materials with desired properties from the hypothetical materials created by the generative model. The experiments show the effectiveness of our active generative inverse design approach.

Autoencoder

Chemical space

Generative model

Generative Design

10.48550/arxiv.2103.00608

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DeepSeqPanII: An Interpretable Recurrent Neural Network Model With Attention Mechanism for Peptide-HLA Class II Binding Prediction

IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021)

Zhonghao Liu Jing Jin Yuxin Cui Zheng Xiong Alireza Nasiri

Human leukocyte antigen (HLA) complex molecules play an essential role in immune interactions by presenting peptides on the cell surface to T cells. With significant deep learning progress, a series of neural network-based models have been proposed and demonstrated with their excellent performances for peptide-HLA class I binding prediction. However, there is still a lack of effective binding prediction models for HLA class II protein binding with peptides due to its inherent challenges. We present a novel sequence-based pan-specific neural network structure, DeepSeaPanII, for peptide-HLA class II binding prediction in this work. Our model is an end-to-end neural network model without the need for pre-or post-processing on input samples compared with existing pan-specific models. Besides state-of-the-art performance in binding affinity prediction, DeepSeqPanII can also extract biological insight on the binding mechanism over the peptide by its attention mechanism-based binding core prediction capability. The leave-one-allele-out cross-validation and benchmark evaluation results show that our proposed network model achieved state-of-the-art performance in HLA-II peptide binding. The source code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPanII .

Benchmark (surveying)

10.1109/tcbb.2021.3074927

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Citations (28)