Article
Oncology
April Lo, Maria McSharry, Alice H. Berger
Summary: Aberrant RNA splicing is common in lung adenocarcinoma and can be caused by dysregulation of the Ras signaling pathway. Analyzing transcriptome and proteome data, we found that mutated KRAS alters splicing factor phosphorylation and leads to significant alterations in alternative splicing events.
Article
Cell Biology
Zhe Liu, Wei Wang, Xinru Li, Xiujuan Zhao, Hongyu Zhao, Wuritu Yang, Yongchun Zuo, Lu Cai, Yongqiang Xing
Summary: Alternative splicing plays an important role in zebrafish embryo development, especially during the maternal-to-zygotic transition process where it is highly abundant and dynamic. Splicing factors are expressed with developmental stage specificity, with a higher expression during the maternal-to-zygotic transition. The inclusion level of alternative splicing events performs well in cluster analysis and can be used as a novel parameter.
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
(2022)
Article
Genetics & Heredity
He Zhang, Baoai Han, Xingxing Han, Yuying Zhu, Hui Liu, Zhiyong Wang, Yanfen Cui, Ran Tian, Zicong Gao, Ruinan Tian, Sixin Ren, Xiaoyan Zuo, Jianfei Tian, Fei Zhang, Ruifang Niu
Summary: Recent evidence suggests that splicing factors (SFs) and alternative splicing (AS) play important roles in cancer progression. This study constructed SF-risk-models and AS-risk-models in different BRCA subtypes, providing potential multidimensional biomarkers for the diagnosis, prognosis, and treatment of BRCA. The functional roles of the selected SFs were found to be highly context-dependent among different BRCA subtypes.
FRONTIERS IN GENETICS
(2021)
Article
Health Care Sciences & Services
Rassanee Bissanum, Sitthichok Chaichulee, Rawikant Kamolphiwong, Raphatphorn Navakanitworakul, Kanyanatt Kanokwiroon
Summary: TNBC lacks well-defined molecular targets and is highly heterogeneous, making treatment challenging. This study attempted to define gene signatures for each TNBC subtype and develop a classification method using machine learning. SVM algorithm achieved the highest accuracy in classifying TNBC into four subtypes, but some samples could not be classified.
JOURNAL OF PERSONALIZED MEDICINE
(2021)
Article
Biochemistry & Molecular Biology
Yu Jin, Maxim Ivanov, Anna Nelson Dittrich, Andrew D. L. Nelson, Sebastian Marquardt
Summary: This study identified a long noncoding RNA (FLAIL) in Arabidopsis that is associated with flowering. FLAIL directly interacts with target genes and affects their expression through RNA-DNA interactions and alternative splicing. The findings suggest that lncRNAs can regulate gene expression and organismal development as accessory components of the spliceosome.
Article
Biochemical Research Methods
Jing Zhao, Bowen Zhao, Xiaotong Song, Chujun Lyu, Weizhi Chen, Yi Xiong, Dong-Qing Wei
Summary: The Subtype-DCC method, which integrates multi-omics data, is proposed for cancer subtyping and demonstrates superior performance compared to existing clustering methods. It has potential applications in cancer diagnosis, prognosis, and treatment.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemistry & Molecular Biology
Da-Cheng Hao, Hao Chen, Pei-Gen Xiao, Tao Jiang
Summary: In this study, the first global analysis of AS events in Dichocarpum was conducted using full-length transcriptome datasets of five Chinese endemic species. The research identified numerous AS events and successfully predicted the functions of AS isoforms.
Article
Cell Biology
Trishna Pani, Kajal Rajput, Animesh Kar, Harsh Sharma, Rituparna Basak, Nihal Medatwal, Sandhini Saha, Gagan Dev, Sharwan Kumar, Siddhi Gupta, Arnab Mukhopadhyay, Dipankar Malakar, Tushar Kanti Maiti, Aneeshkumar G. Arimbasseri, S. V. S. Deo, Ravi Datta Sharma, Avinash Bajaj, Ujjaini Dasgupta
Summary: The study identified specific alternative splicing events of sphingolipid genes in different subtypes of breast cancer, with an event in CERS2 being a poor prognostic factor for Luminal B subtype, leading to a decrease in very-long-chain ceramides and enhanced cancer cell proliferation and migration. These findings suggest subtype-specific alternative splicing of sphingolipid genes as a potential therapeutic target in breast tumors.
CELL DEATH & DISEASE
(2021)
Article
Oncology
Bin Liu, Yuanlin Sun, Yang Zhang, Yanpeng Xing, Jian Suo
Summary: DEK is a potential proto-oncogene highly expressed in gastric cancer (GC). This study analyzed the global transcription and alternative splicing profiles regulated by DEK in a human GC cell line. It revealed that DEK regulates the expression and alternative splicing of multiple cancer-related genes, indicating its role as a possible oncogene. These findings expand the importance and feasibility of DEK as a clinical therapeutic target for human malignancies including GC.
Article
Biology
Marelize Snyman, Sen Xu
Summary: Understanding the relationship between mutations and their effects on gene expression and alternative splicing is important in evolutionary biology. This study investigates the impact of mutations on gene expression and alternative splicing using whole-genome sequencing and RNA sequencing data from Daphnia mutant lines. The results show that trans-effects play a major role in the differences in gene expression and alternative splicing between wild-type and mutant lines, while cis mutations only affect a limited number of genes. Exonic mutations are found to be an important driver of altered gene expression.
PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
(2023)
Article
Biochemical Research Methods
Francisco Cristovao, Silvia Cascianelli, Arif Canakoglu, Mark Carman, Luca Nanni, Pietro Pinoli, Marco Masseroli
Summary: This study explores the potential of machine learning and deep learning for breast cancer subtyping, utilizing pan-cancer and non-cancer data to design semi-supervised models. Results show that simpler models perform as well as deep semi-supervised approaches on gene expression data.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Plant Sciences
Fei Shen, Chenyang Hu, Xin Huang, Hao He, Deng Yang, Jirong Zhao, Xiaozeng Yang
Summary: This article discusses the importance of alternative splicing in plants and how algorithms and deep learning techniques can be used to identify and analyze alternative splicing events. It also highlights the significance of conducting alternative splicing studies in a pan-genomic background, as well as the importance of integrated strategies.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Plant Sciences
Zhibin Zhang, Hongwei Xun, Ruili Lv, Xiaowan Gou, Xintong Ma, Juzuo Li, Jing Zhao, Ning Li, Lei Gong, Bao Liu
Summary: Homoeologous exchange (HE) is a mechanism that generates genetic variation and affects gene expression and transcript diversity in allopolyploids. This study shows that HE impacts gene expression primarily through cis-acting dosage effects in HE regions, leading to changes in expression levels of homoeologous gene pairs. Additionally, HE influences gene expression in non-HE regions through trans-regulation, resulting in convergent expression of homoeologs. Furthermore, HE induces individual-specific changes in alternative splicing events. Overall, HE has multifaceted immediate effects on gene expression and transcript diversity in nascent allopolyploidy.
Review
Biochemistry & Molecular Biology
Immanuel D. Green, Renjing Liu, Justin J. L. Wong
Summary: Vascular smooth muscle cells (VSMCs) exhibit remarkable phenotypic plasticity, allowing them to differentiate or dedifferentiate based on environmental cues. Alternative splicing plays a crucial role in VSMC gene expression regulation, contributing to protein diversity and alterations in gene expression levels. Recent advancements in splicing-modulating therapies hold promise for treating VSMC-related pathologies.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Review
Computer Science, Artificial Intelligence
Pengzhi Li, Yan Pei, Jianqiang Li
Summary: Autoencoder is an unsupervised learning model that automatically learns data features and acts as a dimensionality reduction method. This paper explains the principle and development process of a conventional autoencoder, proposes a taxonomy of autoencoders based on their structures and principles, analyzes and discusses various autoencoder models, introduces their applications in different fields, and summarizes the shortcomings of the current autoencoder algorithm while addressing future development directions.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.