Article
Computer Science, Artificial Intelligence
Nayna Birla, Manoj Kumar Jain, Avinash Panwar
Summary: This paper presents a qualitatively enhanced methodology for automated score prediction of subjective assignments by analyzing multiple linguistic features. The study observed the effect of appropriate feature selection using Mutual Information Regression and investigated four ML algorithms. The results showed that a 3 Layer Neural Network with feature selection performed best among the chosen ML algorithms. Additionally, a new hybrid model was proposed by combining features with a higher level deep neural network to further improve accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Aerospace
Shenghan Zhou, Chaofan Wei, Pan Li, Anying Liu, Wenbing Chang, Yiyong Xiao
Summary: This study proposes a text-based fault diagnosis model that utilizes Word2Vec for text feature extraction and a classifier based on stacking ensemble learning scheme, achieving high accuracy on a real aircraft fault dataset.
Review
Computer Science, Artificial Intelligence
Shazia Usmani, Jawwad A. Shamsi
Summary: Stock market prediction is a challenging task that requires deep insights for extraction of news events, analysis of historic data, and impact of news events on stock price trends. This paper presents a detailed survey covering key terms and phases of generic stock prediction methodology, challenges, data preprocessing techniques, feature extraction techniques, prediction techniques, and future directions for news sensitive stock prediction. The significance of using structured text features, opinion extraction techniques, domain knowledge, and deep neural network based prediction techniques is highlighted.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Information Systems
Jianzhuo Yan, Lihong Chen, Yongchuan Yu, Hongxia Xu, Qingcai Gao, Kunpeng Cao, Jianhui Chen
Summary: This paper proposes an end-to-end Chinese emergency event extraction model using a deep adversarial network, which simplifies the event extraction into four subtasks and introduces adversarial training to enhance the model's performance on small-scale labeled corpora.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2022)
Review
Computer Science, Information Systems
Yi-Wei Lai, Mu-Yen Chen
Summary: This article discusses the research focus and technological development of text mining, introducing various methods and application fields. It emphasizes the challenges in text mining and the importance of data preprocessing, as well as the role of fuzzy logic in improving accuracy.
Article
Chemistry, Multidisciplinary
Waseemullah, Zainab Fatima, Shehnila Zardari, Muhammad Fahim, Maria Andleeb Siddiqui, Ag. Asri Ag. Ibrahim, Kashif Nisar, Laviza Falak Naz
Summary: Text summarization is a technique for shortening long texts or documents. Manual summarization can be costly and time-consuming, while an extractive summarization model balances compression and retention ratios by preserving meaningful sentences and filtering out redundant information.
APPLIED SCIENCES-BASEL
(2022)
Review
Biochemical Research Methods
Yang Qiu, Yang Zhang, Yifan Deng, Shichao Liu, Wen Zhang
Summary: This paper provides a comprehensive review of computational methods for detecting drug-drug interactions (DDIs). It discusses three categories of methods: literature-based extraction methods, machine learning-based prediction methods, and pharmacovigilance-based data mining methods. The paper presents the research background, data sources, representative approaches, and evaluation metrics for each category. It also discusses the current challenges and potential opportunities for future directions in DDI detection.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
John A. Bachman, Benjamin M. Gyori, Peter K. Sorger
Summary: This study presents an approach to accurately assemble molecular mechanisms by using multiple natural language processing systems and INDRA, which improves the reliability of machine reading and assembles non-redundant mechanistic knowledge. Through this approach, the study extends protein-protein interaction databases and provides explanations for co-dependencies in the Cancer Dependency Map.
MOLECULAR SYSTEMS BIOLOGY
(2023)
Article
Computer Science, Information Systems
Hao Xu, Chengzhi Jiang, Chuanfeng Huang, Yiyang Chen, Mengxue Yi, Zhentao Zhu
Summary: This study utilizes text mining methods to extract key information and talk patterns from YiXi talks in China. The results show that the proposed keyword extraction technology provides strategic reading to a certain extent and the mature speech mode serves as a reference for new speakers.
Article
Computer Science, Information Systems
Emilio Sulis, Llio Humphreys, Fabiana Vernero, Ilaria Angela Amantea, Davide Audrito, Luigi Di Caro
Summary: This paper describes a general framework for identifying and classifying implicit inter-relationships within legal texts. Through experiments, the usefulness of co-occurrence networks of terms is demonstrated, and a model integrating network analysis features is proposed to identify the type of relationships. The results show that the adoption of co-occurrence network features improves relationship identification.
INFORMATION SYSTEMS
(2022)
Article
Neurosciences
Shaofu Lin, Zhe Xu, Ying Sheng, Lihong Chen, Jianhui Chen
Summary: This paper proposes a literature mining-based approach for research sharing-oriented neuroimaging provenance construction. By defining a group of neuroimaging event-containing attributes and introducing a joint extraction model based on deep adversarial learning, the event extraction in a few-shot learning scenario is achieved. Experimental results demonstrate that the proposed method provides a practical approach for quickly collecting research information for neuroimaging provenance construction oriented to open research sharing.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Wassim El-Hajj, Hazem Hajj
Summary: This paper proposes a new feature selection method called MFX, which optimally selects a subset of features by mathematically formulating the selection problem as an optimization problem. MFX considers both classification accuracy and feature discriminability, and has two distinguishing features of treating all documents from the same category as one extended document and choosing discriminative terms that are frequent within the category and rare in other categories. Experimental results on various datasets demonstrate the superiority of MFX over other methods, and its performance is shown to outperform recent text classification algorithms based on neural networks and word embeddings when combined with the Support Vector Machine (SVM) classifier.
COMPUTER SPEECH AND LANGUAGE
(2022)
Article
Computer Science, Artificial Intelligence
Qiuqiang Lin, Chuanhou Gao
Summary: With the increasing size of data sets, the importance of feature selection grows. Considering interactions between original features can lead to high dimensionality, especially for categorical features with one-hot encoding. Thus, mining useful features and their interactions becomes more worthwhile. Inspired by association rule mining, we propose a method that utilizes association rules to select features and their interactions, making modifications for practical concerns. Our analysis of the computational complexity demonstrates the efficiency of the proposed algorithm, and a series of experiments confirm its effectiveness.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Tingting Zhang, Baozhen Lee, Qinghua Zhu, Xi Han, Ke Chen
Summary: A keyword extraction method based on a semantic hierarchical graph model is proposed in this paper. It fully takes into account the context and internal structure of keywords, and effectively reveals the hierarchical association between terms within the semantic graph by mining the deep hidden structure of feature terms. Experiments on released datasets show that the proposed method outperforms existing methods in terms of precision, recall, and F-measure.
Article
Computer Science, Interdisciplinary Applications
Jake Vasilakes, Panagiotis Georgiadis, Nhung T. H. Nguyen, Makoto Miwa, Sophia Ananiadou
Summary: Automatic extraction of patient medication histories from clinical notes can enhance clinicians' access to relevant information. To accurately construct patient timelines, clinical text mining systems need to predict event context, including negation, uncertainty, and time of occurrence. In this study, we present Levitated Context Markers (LCMs), a transformer-based model that enables global representation utilization and event-focused attention mechanism for contextualized event extraction. LCMs outperform a strong baseline model on the Contextualized Medication Event Dataset and demonstrate interpretable predictions by detecting relevant context cues in an unsupervised manner.
JOURNAL OF BIOMEDICAL INFORMATICS
(2023)
Article
Automation & Control Systems
Xiao-Fei Zhang, Le Ou-Yang, Ting Yan, Xiaohua Tony Hu, Hong Yan
Summary: The study introduces a joint graphical model to estimate multiple gene networks simultaneously, leveraging network decomposition and group lasso penalty to examine similarities and differences among different subpopulations and data types, leading to improved accuracy in gene network reconstruction.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Automation & Control Systems
Yu-Feng Yu, Guoxia Xu, Min Jiang, Hu Zhu, Dao-Qing Dai, Hong Yan
Summary: In this paper, a robust graph matching (RGM) model is proposed to improve the effectiveness and robustness in matching graphs with deformations, rotations, outliers, and noise. The RGM model embeds joint geometric transformation and utilizes $L_{2,1}$ -norm as the similarity metric for enhanced robustness. Extensive experiments demonstrate the competitive performance of the RGM model in various graph matching tasks.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Information Systems
Rizwan Qureshi, Mengxu Zhu, Hong Yan
Summary: This study investigates the mechanism of drug resistance caused by EGFR mutations in NSCLC, using molecular dynamics simulations and a PCA-based method to analyze drug resistance. The establishment of a systematic method for visualizing protein-drug interactions provides an effective framework for the atomic-level analysis of drug resistance in lung cancer.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2021)
Article
Automation & Control Systems
Chuan-Xian Ren, Pengfei Ge, Dao-Qing Dai, Hong Yan
Summary: A new kernel learning method called KLN is proposed in this paper to enhance the discrimination performance of Conditional Maximum Mean Discrepancy (CMMD) by iteratively operating on deep network features. By considering a compound kernel, the effectiveness of CMMD for data category description is improved, leading to state-of-the-art classification performance on benchmark datasets.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Automation & Control Systems
Hu Zhu, Chunfeng Cui, Lizhen Deng, Ray C. C. Cheung, Hong Yan
Summary: The paper proposed an elastic net constraint-based tensor model for high-order graph matching, introducing a tradeoff between sparsity and accuracy. A nonmonotone spectral projected gradient method was derived for optimization, proving global convergence and superiority of the method through experiments.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Engineering, Electrical & Electronic
Jianjun Liu, Dunbin Shen, Zebin Wu, Liang Xiao, Jun Sun, Hong Yan
Summary: This paper proposes a patch-aware deep fusion approach for hyperspectral image fusion, aiming to improve the fusion result by utilizing patch information under subspace representation. The proposed approach builds a fusion model and solves it using an optimization algorithm, resulting in a structured deep fusion network. The performance is further improved by an aggregation fusion technique.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Biochemical Research Methods
Yu-Ting Tan, Le Ou-Yang, Xingpeng Jiang, Hong Yan, Xiao-Fei Zhang
Summary: Learning how gene regulatory networks change under different conditions is important. Existing methods for inferring differential networks have limitations. In this study, a new method is proposed and shown to outperform other methods in simulation studies and applications.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Rizwan Qureshi, Avirup Ghosh, Hong Yan
Summary: This study examines the complete structure of the multi-domain EGFR protein and its mutants using molecular dynamics simulations and normal mode analysis. The findings reveal different patterns of correlated motions in each domain of EGFR mutants compared to the wildtype, and the mutant structures have fewer hydrogen bonds. These findings are important for understanding the dynamics and communications in EGFR domains.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
You-Wei Luo, Chuan-Xian Ren, Dao-Qing Dai, Hong Yan
Summary: This paper proposes a Riemannian manifold learning framework for achieving transferability and discriminability simultaneously in unsupervised domain adaptation. A probabilistic discriminant criterion is established on the target domain using soft labels, and manifold metric alignment is used to be compatible with the embedding space. Experimental results demonstrate the superiority of the proposed method.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Biochemical Research Methods
Meng-Guo Wang, Le Ou-Yang, Hong Yan, Xiao-Fei Zhang
Summary: In this study, a novel method called prior network-dependent gene network inference (pGNI) is proposed to estimate gene co-expression networks by integrating gene expression data and prior protein interaction network data. The method successfully captures the modular structures in the networks and is demonstrated to be effective through simulation studies and real datasets.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Bo Li, Ke Jin, Le Ou-Yang, Hong Yan, Xiao-Fei Zhang
Summary: The single-cell RNA sequencing (scRNA-seq) technique is used to analyze gene expression patterns in complex tissues at single-cell resolution, but dropout events can hinder downstream analyses. We developed a new imputation method, scTSSR2, which combines matrix decomposition with two-side sparse self-representation to effectively impute dropout events in scRNA-seq data. Comparative experiments show that scTSSR2 outperforms existing imputation methods in terms of computational speed and memory usage. We also provide a user-friendly R package, scTSSR2, for denoising scRNA-seq data and improving data quality.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Ali Raza Shahid, Mehmood Nawaz, Xinqi Fan, Hong Yan
Summary: This article proposes a view-adaptive mechanism that transforms the skeleton view into a more consistent virtual perspective, reducing the influence of view variations.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2023)
Article
Biochemical Research Methods
Subin Qian, Huiyi Liu, Xiaofeng Yuan, Wei Wei, Shuangshuang Chen, Hong Yan
Summary: This paper proposes a biclustering method called RCSBC, which aims to find checkerboard patterns within gene expression data. By exploiting the relationship between the row/column structure of a gene expression matrix and the structure of biclusters, the method achieves low time and space complexity and outperforms existing algorithms in terms of clustering accuracy and time/space complexity.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Engineering, Electrical & Electronic
Yi Yang, Lixin Han, Yuanzhen Liu, Jun Zhu, Hong Yan
Summary: Inspired by the accuracy and efficiency of the gamma-norm of a matrix, the study generalizes the gamma-norm to tensors and proposes a new tensor completion approach within the tensor singular value decomposition framework. An efficient algorithm, combining the augmented Lagrange multiplier and closed-resolution of a cubic equation, is developed to solve the associated nonconvex tensor multi-rank minimization problem. Experimental results demonstrate that the proposed approach outperforms current state of the art algorithms in recovery accuracy.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Computer Science, Information Systems
Mengxu Zhu, Rizwan Qureshi, Hong Yan
Summary: EGFR plays a vital role in lung cell proliferation and mutations in its kinase domain may lead to cancer. This study investigated the binding mechanism of EGFR drug mutant complex through molecular dynamics simulation and geometrical properties analysis. The results showed that drug-sensitive mutants have tighter interactions, while drug-resistant mutants have looser bindings.
