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General General

Lifelong Text-Audio Sentiment Analysis learning.

In Neural networks : the official journal of the International Neural Network Society

Sentiment analysis refers to the mining of textual context, which is conducted with the aim of identifying and extracting subjective opinions in textual materials. However, most existing methods neglect other important modalities, e.g., the audio modality, which can provide intrinsic complementary knowledge for sentiment analysis. Furthermore, much work on sentiment analysis cannot continuously learn new sentiment analysis tasks or discover potential correlations among distinct modalities. To address these concerns, we propose a novel Lifelong Text-Audio Sentiment Analysis (LTASA) model to continuously learn text-audio sentiment analysis tasks, which effectively explores intrinsic semantic relationships from both intra-modality and inter-modality perspectives. More specifically, a modality-specific knowledge dictionary is developed for each modality to obtain shared intra-modality representations among various text-audio sentiment analysis tasks. Additionally, based on information dependence between text and audio knowledge dictionaries, a complementarity-aware subspace is developed to capture the latent nonlinear inter-modality complementary knowledge. To sequentially learn text-audio sentiment analysis tasks, a new online multi-task optimization pipeline is designed. Finally, we verify our model on three common datasets to show its superiority. Compared with some baseline representative methods, the capability of the LTASA model is significantly boosted in terms of five measurement indicators.

Lin Yuting, Ji Peng, Chen Xiuyi, He Zhongshi

2023-Feb-17

Cross-modality learning, Lifelong machine learning, Multi-task learning, Text-audio sentiment analysis

General General

WDMNet: Modeling diverse variations of regional wind speed for multi-step predictions.

In Neural networks : the official journal of the International Neural Network Society

Regional wind speed prediction plays an important role in the development of wind power, which is usually recorded in the form of two orthogonal components, namely U-wind and V-wind. The regional wind speed has the characteristics of diverse variations, which are reflected in three aspects: (1) The spatially diverse variations of regional wind speed indicate that wind speed has different dynamic patterns at different positions; (2) The distinct variations between U-wind and V-wind denote that U-wind and V-wind at the same position exhibit different dynamic patterns; (3) The non-stationary variations of wind speed represent that the intermittent and chaotic nature of wind speed. In this paper, we propose a novel framework named Wind Dynamics Modeling Network (WDMNet) to model the diverse variations of regional wind speed and make accurate multi-step predictions. To jointly capture the spatially diverse variations and the distinct variations between U-wind and V-wind, WDMNet leverages a new neural block called Involution Gated Recurrent Unit Partial Differential Equation (Inv-GRU-PDE) as its key component. The block adopts involution to model spatially diverse variations and separately constructs hidden driven PDEs of U-wind and V-wind. The construction of PDEs in this block is achieved by a new Involution PDE (InvPDE) layers. Besides, a deep data-driven model is also introduced in Inv-GRU-PDE block as the complement to the constructed hidden PDEs for sufficiently modeling regional wind dynamics. Finally, to effectively capture the non-stationary variations of wind speed, WDMNet follows a time-variant structure for multi-step predictions. Comprehensive experiments have been conducted on two real-world datasets. Experimental results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art techniques.

Ye Rui, Feng Shanshan, Li Xutao, Ye Yunming, Zhang Baoquan, Zhu Yan, Sun Yao, Wang Yaowei

2023-Feb-22

Deep learning, Diverse variations, PDEs construction, Regional wind speed prediction

General General

Machine learning for genetic prediction of chemotherapy toxicity in cervical cancer.

In Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie

BACKGROUND : Locally advanced cervical cancer (LACC) is frequently treated with neoadjuvant chemotherapy (NACT), which includes paclitaxel and platinum. However, the development of severe chemotherapy toxicity is a barrier to successful NACT. Phosphatidylinositol 3-kinase (PI3K)/serine/threonine kinase (AKT) pathway is related to the occurrence of chemotherapeutic toxicity. In this research work, we employ a random forest (RF) machine learning model to forecast NACT toxicity (neurological, gastrointestinal, and hematological reactions).

MATERIALS AND METHODS : Twenty-four single nucleotide polymorphisms (SNPs) in the PI3K/AKT pathway from 259 LACC patients were used to construct a dataset. Following the data preprocessing, the RF model was trained. The Mean Decrease in Impurity approach was adopted to evaluate the relevance of 70 selected genotypes' importance by comparing chemotherapy toxicity grades 1-2 vs. 3.

RESULTS : In the Mean Decrease in Impurity analysis, neurological toxicity was much more likely to occur in LACC patients with homozygous AA in Akt2 rs7259541 than in those with AG or GG genotypes. The CT genotype of PTEN rs532678 and the CT genotype of Akt1 rs2494739 increased the risk of neurological toxicity. The top three loci were rs4558508, rs17431184, and rs1130233, which were attributed to an elevated risk of gastrointestinal toxicity. LACC patients who had heterozygous AG in Akt2 rs7259541 exhibited an obviously greater risk of hematological toxicity than those who had AA or GG genotypes. And the CT genotype for Akt1 rs2494739 and the CC genotype in PTEN rs926091 showed a tendency to increase the risk of suffering from hematological toxicity.

CONCLUSION : Akt2 rs7259541 and rs4558508, Akt1 rs2494739 and rs1130233, PTEN rs532678, rs17431184, and rs926091 polymorphisms are associated with different toxic effects during the chemotherapy treatment of LACC.

Guo Lu, Wang Wei, Xie Xiaodong, Wang Shuihua, Zhang Yudong

2023-Mar-10

Chemotherapy toxicity, Locally advanced cervical cancer, Machine learning, Neoadjuvant chemotherapy, PI3K/AKT pathway, Random forest, Single nucleotide polymorphisms

General General

Is ChatGPT a valid author?

In Nurse education in practice ; h5-index 36.0

This letter to the editors takes a deeper look at the validity and ethics of authorship of a recently published article in Nurse Education in Practice in which authorship was shared with a chatbox software program, ChatGPT (https://doi.org/10.1016/j.nepr.2022.103537). In particular, a closer assessment is made of the authorship of that article from the established principles of authorship as delineated by the ICMJE.

Teixeira da Silva Jaime A

2023-Mar-07

Artificial intelligence (AI), Authorship principles, COPE, Ethics, ICMJE, Responsibility

Radiology Radiology

Convolutional neural network-based automated maxillary alveolar bone segmentation on cone-beam computed tomography images.

In Clinical oral implants research ; h5-index 55.0

OBJECTIVES : To develop and assess the performance of a novel artificial intelligence (AI)-driven convolutional neural network (CNN)-based tool for automated three-dimensional (3D) maxillary alveolar bone segmentation on cone-beam computed tomography (CBCT) images.

MATERIAL AND METHODS : A total of 141 CBCT scans were collected for performing training (n=99), validation (n=12) and testing (n=30) of the CNN model for automated segmentation of the maxillary alveolar bone and its crestal contour. Following automated segmentation, the 3D models with under- or over-estimated segmentations were refined by an expert for generating a refined-AI (R-AI) segmentation. The overall performance of CNN model was assessed. Also, 30% of the testing sample was randomly selected and manually segmented to compare the accuracy of AI and manual segmentation. Additionally, the time required to generate a 3D model was recorded in seconds (s).

RESULTS : The accuracy metrics of automated segmentation showed an excellent range of values for all accuracy metrics. However, the manual method (95% HD: 0.20±0.05 mm; IoU: 95%±3.0; DSC: 97%±2.0) showed slightly better performance than the AI segmentation (95% HD: 0.27±0.03 mm; IoU: 92%±1.0; DSC: 96%±1.0). There was a statistically significant difference of the time-consumed amongst the segmentation methods (p<0.001). The AI-driven segmentation (51.5±10.9s) was 116 times faster than the manual segmentation (5973.3±623.6s). The R-AI method showed intermediate time-consumed (1666.7±588.5s).

CONCLUSION : Although the manual segmentation showed slightly better performance, the novel CNN-based tool also provided a highly accurate segmentation of the maxillary alveolar bone and its crestal contour consuming 116 times less than the manual approach.

Fontenele Rocharles Cavalcante, Gerhardt Maurício do Nascimento, Picoli Fernando Fortes, Gerven Adriaan Van, Nomidis Stefanos, Willems Holger, Freitas Deborah Queiroz, Jacobs Reinhilde

2023-Mar-12

alveolar crest, artificial intelligence, cone-beam computed tomography, dental implant, jaw bone, maxilla, neural networks

oncology Oncology

A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics.

In Proteomics

Accurate retention time prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision and biology. We assess the performance of the transformer architecture for retention time prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field. This article is protected by copyright. All rights reserved.

Pham Thang V, Nguyen Vinh V, Vu Duong, Henneman Alex A, Richardson Robin A, Piersma Sander R, Jimenez Connie R

2023-Mar-12

DIA-MS, Deep learning, retention time prediction, spectral library, transformer architecture