DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning.

BACKGROUND: Proteins play a pivotal role in the diverse array of biological processes, making the precise prediction of protein-protein interaction (PPI) sites critical to numerous disciplines including biology, medicine and pharmacy. While deep learning methods have progressively been implemented for the prediction of PPI sites within proteins, the task of enhancing their predictive performance remains an arduous challenge. RESULTS: In this paper, we propose a novel PPI site prediction model (DGCPPISP) based on a dynamic graph convolutional neural network and a two-stage transfer learning strategy. Initially, we implement the transfer learning from dual perspectives, namely feature input and model training that serve to supply efficacious prior knowledge for our model. Subsequently, we construct a network designed for the second stage of training, which is built on the foundation of dynamic graph convolution. CONCLUSIONS: To evaluate its effectiveness, the performance of the DGCPPISP model is scrutinized using two benchmark datasets. The ensuing results demonstrate that DGCPPISP outshines competing methods in terms of performance. Specifically, DGCPPISP surpasses the second-best method, EGRET, by margins of 5.9%, 10.1%, and 13.3% for F1-measure, AUPRC, and MCC metrics respectively on Dset_186_72_PDB164. Similarly, on Dset_331, it eclipses the performance of the runner-up method, HN-PPISP, by 14.5%, 19.8%, and 29.9% respectively.

Investigation on the mechanisms for biotransformation of saponins to diosgenin.

In order to improve the efficiency of biotransformation of saponins in Dioscorea zingiberensis to diosgenin, a new enzymatic model was developed to investigate the mechanism of the metabolic systems. Four main saponin hydrolases (E1, E2, E3 and E4) were purified from Trichoderma reesei. Using progracillin as substrate, the enzymatic hydrolysis experiments with E1, E2, E3 and E4 were carried out respectively. Saponin concentrations during each biotransformation reaction were constructed with a kinetic model consisting of a few Michaelis-Menten equations. During biotransformation, C-26 glycoside and C-3 terminal glycoside were cleaved sequentially from saponins by E1, E2, E3 and E4. Then C-3 terminal rhamnoside and C-3 glycoside were released from the aglycone stepwisely by E2 and E3, to yield diosgenin. E2 and E3 were the key enzymes in the system, and cleavage of the C-3 glycoside from saponins was the rate-limiting step in the biotransformation process. The proposed enzymatic model might be used to analyze the mechanism for biotransformation of saponins to diosgenin.

A machine learning based method for constructing group profiles of university students.

This study facilitates university student profiling by constructing a prediction model to forecast the classification of future students participating in a survey, thereby enhancing the utility and effectiveness of the questionnaire approach. In the context of the ongoing digital transformation of campuses, higher education institutions are increasingly prioritizing student educational development. This shift aligns with the maturation of big data technology, prompting scholars to focus on profiling university student education. While earlier research in this area, particularly foreign studies, focus on extracting data from specific learning contexts and often relied on single data sources, our study addresses these limitations. We employ a comprehensive approach, incorporating questionnaire surveys to capture a diverse array of student data. Considering various university student attributes, we create a holistic profile of the student population. Furthermore, we use clustering techniques to develop a categorical prediction model. In our clustering analysis, we employ the K-means algorithm to group student survey data. The results reveal four distinct student profiles: Diligent Learners, Earnest Individuals, Discerning Achievers, and Moral Advocates. These profiles are subsequently used to label student groups. For the classification task, we leverage these labels to establish a prediction model based on the Back Propagation neural network, with the goal of assigning students to their respective groups. Through meticulous model optimization, an impressive classification accuracy of 90.22% is achieved. Our research offers a novel perspective and serves as a valuable methodological reference for university student profiling.

DSnet: a new dual-branch network for hippocampus subfield segmentation.

The hippocampus is a critical component of the brain and is associated with many neurological disorders. It can be further subdivided into several subfields, and accurate segmentation of these subfields is of great significance for diagnosis and research. However, the structures of hippocampal subfields are irregular and have complex boundaries, and their voxel values are close to surrounding brain tissues, making the segmentation task highly challenging. Currently, many automatic segmentation tools exist for hippocampal subfield segmentation, but they suffer from high time costs and low segmentation accuracy. In this paper, we propose a new dual-branch segmentation network structure (DSnet) based on deep learning for hippocampal subfield segmentation. While traditional convolutional neural network-based methods are effective in capturing hierarchical structures, they struggle to establish long-term dependencies. The DSnet integrates the Transformer architecture and a hybrid attention mechanism, enhancing the network's global perceptual capabilities. Moreover, the dual-branch structure of DSnet leverages the segmentation results of the hippocampal region to facilitate the segmentation of its subfields. We validate the efficacy of our algorithm on the public Kulaga-Yoskovitz dataset. Experimental results indicate that our method is more effective in segmenting hippocampal subfields than conventional single-branch network structures. Compared to the classic 3D U-Net, our proposed DSnet improves the average Dice accuracy of hippocampal subfield segmentation by 0.57%.

Deep fusion of multi-modal features for brain tumor image segmentation.

Accurate segmentation of pathological regions in brain magnetic resonance images (MRI) is essential for the diagnosis and treatment of brain tumors. Multi-modality MRIs, which offer diverse feature information, are commonly utilized in brain tumor image segmentation. Deep neural networks have become prevalent in this field; however, many approaches simply concatenate different modalities and input them directly into the neural network for segmentation, disregarding the unique characteristics and complementarity of each modality. In this study, we propose a brain tumor image segmentation method that leverages deep residual learning with multi-modality image feature fusion. Our approach involves extracting and fusing distinct and complementary features from various modalities, fully exploiting the multi-modality information within a deep convolutional neural network to enhance the performance of brain tumor image segmentation. We evaluate the effectiveness of our proposed method using the BraTS2021 dataset and demonstrate that deep residual learning with multi-modality image feature fusion significantly improves segmentation accuracy. Our method achieves competitive segmentation results, with Dice values of 83.3, 89.07, and 91.44 for enhanced tumor, tumor core, and whole tumor, respectively. These findings highlight the potential of our method in improving brain tumor diagnosis and treatment through accurate segmentation of pathological regions in brain MRIs.

Deep convolutional neural network for hippocampus segmentation with boundary region refinement.

Accurately segmenting the hippocampus from magnetic resonance (MR) brain images is a crucial step in studying brain disorders. However, this task is challenging due to the low signal contrast of hippocampal images, the irregular shape, and small structural size of the hippocampi. In recent years, several deep convolutional networks have been proposed for hippocampus segmentation, which have achieved state-of-the-art performance. These methods typically use large image patches for training the network, as larger patches are beneficial for capturing long-range contextual information. However, this approach increases the computational burden and overlooks the significance of the boundary region. In this study, we propose a deep learning-based method for hippocampus segmentation with boundary region refinement. Our method involves two main steps. First, we propose a convolutional network that takes large image patches as input for initial segmentation. Then, we extract small image patches around the hippocampal boundary for training the second convolutional neural network, which refines the segmentation in the boundary regions. We validate our proposed method on a publicly available dataset and demonstrate that it significantly improves the performance of convolutional neural networks that use single-size image patches as input. In conclusion, our study proposes a novel method for hippocampus segmentation, which improves upon the current state-of-the-art methods. By incorporating a boundary refinement step, our approach achieves higher accuracy in hippocampus segmentation and may facilitate research on brain disorders.

Patch tensor decomposition and non-local means filter-based hybrid ASL image denoising.

BACKGROUND: Arterial spin labeling magnetic resonance imaging (ASL MRI) is a noninvasive technique to measure cerebral blood flow (CBF). It is widely used in the study of neurodegenerative diseases. Image denoising is an important step in ASL image processing because the signal-to-noise ratio (SNR) of an ASL CBF perfusion image is very small. NEW METHOD: We propose a new ASL image denoising method that exploits patch-based low-rank and sparse tensor decomposition and a non-local means filter. COMPARISON WITH EXISTING METHODS: The proposed method was compared with two existing ASL denoising methods: component-based noise correction method (CompCor) and low-rank and sparse matrix decomposition-based ASL image denoising method (LS-ASLd). RESULTS: Various image quality measures, namely SNR, tSNR and ASL CBF variance, show that the proposed method is more effective than existing ASL denoising methods. The proposed method was used to denoise images from a resting state ASL dataset to compute brain functional connectivity (FC) and images from a task-related ASL dataset to identify brain activation. The results show that the proposed denoising method is more effective to enhance the sensitivity of ASL CBF series when undertaking CBF time series-based FC analysis and task activation detection. CONCLUSIONS: Assessment of the performance of the proposed hybrid ASL CBF image denoising method confirms that it is especially well-suited to FC analysis and sensorimotor task analysis.

Multi-Scale Context-Guided Deep Network for Automated Lesion Segmentation With Endoscopy Images of Gastrointestinal Tract.

Accurate lesion segmentation based on endoscopy images is a fundamental task for the automated diagnosis of gastrointestinal tract (GI Tract) diseases. Previous studies usually use hand-crafted features for representing endoscopy images, while feature definition and lesion segmentation are treated as two standalone tasks. Due to the possible heterogeneity between features and segmentation models, these methods often result in sub-optimal performance. Several fully convolutional networks have been recently developed to jointly perform feature learning and model training for GI Tract disease diagnosis. However, they generally ignore local spatial details of endoscopy images, as down-sampling operations (e.g., pooling and convolutional striding) may result in irreversible loss of image spatial information. To this end, we propose a multi-scale context-guided deep network (MCNet) for end-to-end lesion segmentation of endoscopy images in GI Tract, where both global and local contexts are captured as guidance for model training. Specifically, one global subnetwork is designed to extract the global structure and high-level semantic context of each input image. Then we further design two cascaded local subnetworks based on output feature maps of the global subnetwork, aiming to capture both local appearance information and relatively high-level semantic information in a multi-scale manner. Those feature maps learned by three subnetworks are further fused for the subsequent task of lesion segmentation. We have evaluated the proposed MCNet on 1,310 endoscopy images from the public EndoVis-Ab and CVC-ClinicDB datasets for abnormal segmentation and polyp segmentation, respectively. Experimental results demonstrate that MCNet achieves [Formula: see text] and [Formula: see text] mean intersection over union (mIoU) on two datasets, respectively, outperforming several state-of-the-art approaches in automated lesion segmentation with endoscopy images of GI Tract.

FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation.

Segmentation of medical images using multiple atlases has recently gained immense attention due to their augmented robustness against variabilities across different subjects. These atlas-based methods typically comprise of three steps: atlas selection, image registration, and finally label fusion. Image registration is one of the core steps in this process, accuracy of which directly affects the final labeling performance. However, due to inter-subject anatomical variations, registration errors are inevitable. The aim of this paper is to develop a deep learning-based confidence estimation method to alleviate the potential effects of registration errors. We first propose a fully convolutional network (FCN) with residual connections to learn the relationship between the image patch pair (i.e., patches from the target subject and the atlas) and the related label confidence patch. With the obtained label confidence patch, we can identify the potential errors in the warped atlas labels and correct them. Then, we use two label fusion methods to fuse the corrected atlas labels. The proposed methods are validated on a publicly available dataset for hippocampus segmentation. Experimental results demonstrate that our proposed methods outperform the state-of-the-art segmentation methods.

Functional parcellation of the hippocampus by semi-supervised clustering of resting state fMRI data.

Many unsupervised methods are widely used for parcellating the brain. However, unsupervised methods aren't able to integrate prior information, obtained from such as exiting functional neuroanatomy studies, to parcellate the brain, whereas the prior information guided semi-supervised method can generate more reliable brain parcellation. In this study, we propose a novel semi-supervised clustering method for parcellating the brain into spatially and functionally consistent parcels based on resting state functional magnetic resonance imaging (fMRI) data. Particularly, the prior supervised and spatial information is integrated into spectral clustering to achieve reliable brain parcellation. The proposed method has been validated in the hippocampus parcellation based on resting state fMRI data of 20 healthy adult subjects. The experimental results have demonstrated that the proposed method could successfully parcellate the hippocampus into head, body and tail parcels. The distinctive functional connectivity patterns of these parcels have further demonstrated the validity of the parcellation results. The effects of aging on the three hippocampus parcels' functional connectivity were also explored across the healthy adult subjects. Compared with state-of-the-art methods, the proposed method had better performance on functional homogeneity. Furthermore, the proposed method had good test-retest reproducibility validated by parcellating the hippocampus based on three repeated resting state fMRI scans from 24 healthy adult subjects.

Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation.

Automatic and reliable segmentation of the hippocampus from magnetic resonance (MR) brain images is extremely important in a variety of neuroimage studies. To improve the hippocampus segmentation performance, a local binary pattern based feature extraction method is developed for machine learning based multi-atlas hippocampus segmentation. Under the framework of multi-atlas image segmentation (MAIS), a set of selected atlases are registered to images to be segmented using a non-linear image registration algorithm. The registered atlases are then used as training data to build linear regression models for segmenting the images based on the image features, referred to as random local binary pattern (RLBP), extracted using a novel image feature extraction method. The RLBP based MAIS algorithm has been validated for segmenting hippocampus based on a data set of 135 T1 MR images which are from the Alzheimer's Disease Neuroimaging Initiative database (adni.loni.usc.edu). By using manual segmentation labels produced by experienced tracers as the standard of truth, six segmentation evaluation metrics were used to evaluate the image segmentation results by comparing automatic segmentation results with the manual segmentation labels. We further computed Cohen's d effect size to investigate the sensitivity of each segmenting method in detecting volumetric differences of the hippocampus between different groups of subjects. The evaluation results showed that our method was competitive to state-of-the-art label fusion methods in terms of accuracy. Hippocampal volumetric analysis showed that the proposed RLBP method performed well in detecting the volumetric differences of the hippocampus between groups of Alzheimer's disease patients, mild cognitive impairment subjects, and normal controls. These results have demonstrated that the RLBP based multi-atlas image segmentation method could facilitate efficient and accurate extraction of the hippocampus and may help predict Alzheimer's disease. The codes of the proposed method is available (https://www.nitrc.org/frs/?group_id=1242).

Dilated Dense U-Net for Infant Hippocampus Subfield Segmentation.

Accurate and automatic segmentation of infant hippocampal subfields from magnetic resonance (MR) images is an important step for studying memory related infant neurological diseases. However, existing hippocampal subfield segmentation methods were generally designed based on adult subjects, and would compromise performance when applied to infant subjects due to insufficient tissue contrast and fast changing structural patterns of early hippocampal development. In this paper, we propose a new fully convolutional network (FCN) for infant hippocampal subfield segmentation by embedding the dilated dense network in the U-net, namely DUnet. The embedded dilated dense network can generate multi-scale features while keeping high spatial resolution, which is useful in fusing the low-level features in the contracting path with the high-level features in the expanding path. To further improve the performance, we group every pair of convolutional layers with one residual connection in the DUnet, and obtain the Residual DUnet (ResDUnet). Experimental results show that our proposed DUnet and ResDUnet improve the average Dice coefficient by 2.1 and 2.5% for infant hippocampal subfield segmentation, respectively, when compared with the classic 3D U-net. The results also demonstrate that our methods outperform other state-of-the-art methods.

Arterial spin labeling perfusion MRI signal denoising using robust principal component analysis.

BACKGROUND: Arterial spin labeling (ASL) perfusion MRI provides a non-invasive way to quantify regional cerebral blood flow (CBF) and has been increasingly used to characterize brain state changes due to disease or functional alterations. Its use in dynamic brain activity study, however, is still hampered by the relatively low signal-to-noise-ratio (SNR) of ASL data. NEW METHOD: The aim of this study was to validate a new temporal denoising strategy for ASL MRI. Robust principal component analysis (rPCA) was used to decompose the ASL CBF image series into a low-rank component and a sparse component. The former captures the slowly fluctuating perfusion patterns while the latter represents spatially incoherent spiky variations and was discarded as noise. While there still lacks a way to determine the parameter for controlling the balance between the low-rankness and sparsity of the decomposition, we designed a method to solve this problem based on the unique data structures of ASL MRI. Method evaluations were performed with ASL CBF-based functional connectivity (FC) analysis and a sensorimotor functional ASL MRI study. COMPARISON WITH EXISTING METHOD(S): The proposed method was compared with the component based noise correction method (CompCor). RESULTS: The proposed method markedly increased temporal signal-to-noise-ratio (TSNR) and sensitivity of ASL CBF images for FC analysis and task activation detection. CONCLUSIONS: We proposed a new temporal ASL CBF image denoising method, and showed its benefit for the CBF time series-based FC analysis and task activation detection.

Patch-based local learning method for cerebral blood flow quantification with arterial spin-labeling MRI.

Arterial spin-labeling (ASL) perfusion MRI is a non-invasive method for quantifying cerebral blood flow (CBF). Standard ASL CBF calibration mainly relies on pair-wise subtraction of the spin-labeled images and controls images at each voxel separately, ignoring the abundant spatial correlations in ASL data. To address this issue, we previously proposed a multivariate support vector machine (SVM) learning-based algorithm for ASL CBF quantification (SVMASLQ). But the original SVMASLQ was designed to do CBF quantification for all image voxels simultaneously, which is not ideal for considering local signal and noise variations. To fix this problem, we here in this paper extended SVMASLQ into a patch-wise method by using a patch-wise classification kernel. At each voxel, an image patch centered at that voxel was extracted from both the control images and labeled images, which was then input into SVMASLQ to find the corresponding patch of the surrogate perfusion map using a non-linear SVM classifier. Those patches were eventually combined into the final perfusion map. Method evaluations were performed using ASL data from 30 young healthy subjects. The results showed that the patch-wise SVMASLQ increased perfusion map SNR by 6.6% compared to the non-patch-wise SVMASLQ.

Metric Learning for Multi-atlas based Segmentation of Hippocampus.

Automatic and reliable segmentation of hippocampus from MR brain images is of great importance in studies of neurological diseases, such as epilepsy and Alzheimer's disease. In this paper, we proposed a novel metric learning method to fuse segmentation labels in multi-atlas based image segmentation. Different from current label fusion methods that typically adopt a predefined distance metric model to compute a similarity measure between image patches of atlas images and the image to be segmented, we learn a distance metric model from the atlases to keep image patches of the same structure close to each other while those of different structures are separated. The learned distance metric model is then used to compute the similarity measure between image patches in the label fusion. The proposed method has been validated for segmenting hippocampus based on the EADC-ADNI dataset with manually labelled hippocampus of 100 subjects. The experiment results demonstrated that our method achieved statistically significant improvement in segmentation accuracy, compared with state-of-the-art multi-atlas image segmentation methods.