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Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective

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Computational and Systems Oncology

Computational and Systems Oncology 2023年第3期3卷

作者： Ai, Xusheng Smith, Melissa C Feltus, Frank Alex Electrical and Computer Engineering Department Clemson University Clemson SC United States Department of Genetics and Biochemistry Biomedical Data Science & Informatics Program Clemson Center for Human Genetics Clemson University Clemson SC United States

The remarkable flexibility and adaptability of generative adversarial networks (GANs) have led to the proliferation of its models in bioinformatics research. Proteomic and transcriptomic profiles have been shown to be promising methods for discovering and identifying disease biomarkers. However, those analyses were performed by trained human examiners making the process tedious, time consuming, and hard to standardize. With the development of GANs, it is now possible to reduce computational costs and human time for bioinformatics analysis to produce effective biomarkers. Moreover, GANs help address the lack of phenotypic state transitional gene expression data as well as avoid protected human data constraints by generating RNA sequencing (RNA-seq) data from random vectors. The purpose of this review is to summarize the use of GAN approaches and techniques to augment RNA-seq expression data and identify clinically useful biomarkers. We compare different studies that use different types of GAN models to examine the biomarkers. Also, we identify research gaps and challenges that apply GANs to bio-informatics. Finally, we propose potential directions for future research. © 2023 The Authors. Computational and Systems Oncology published by Wiley Periodicals LLC.

关键词： biomarkers data augmentation generative adversarial network RNA sequencing

Advances and challenges in unsupervised neural machine translation 16

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Advances and challenges in unsupervised neural machine trans...

16th Conference of the European Chapter of the Associationfor Computational Linguistics: Tutorials, EACL 2021

作者： Wang, Rui Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781954085039

Unsupervised cross-lingual language representation initialization methods, together with mechanisms such as denoising and back-translation, have advanced unsupervised neural machine translation (UNMT), which has achieved impressive results. Meanwhile, there are still several challenges for UNMT. This tutorial first introduces the background and the latest progress of UNMT. We then examine a number of challenges to UNMT and give empirical results on how well the technology currently holds up1 © 2020 Association for Computational Linguistics.

关键词： Neural machine translation

Text Simplification of College Admissions Instructions: A Professionally Simplified and Verified Corpus

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arXiv 2022年

作者： Taylor, Zachary W. Chu, Maximus H. Li, Junyi Jessy College of Education and Human Sciences University of Southern Mississippi United States Department of Computer Science The University of Texas at Austin United States Department of Linguistics The University of Texas at Austin United States

Access to higher education is critical for minority populations and emergent bilingual students. However, the language used by higher education institutions to communicate with prospective students is often too complex;concretely, many institutions in the US publish admissions application instructions far above the average reading level of a typical high school graduate, often near the 13th or 14th grade level. This leads to an unnecessary barrier between students and access to higher education. This work aims to tackle this challenge via text simplification. We present PSAT (Professionally Simplified Admissions Texts), a dataset with 112 admissions instructions randomly selected from higher education institutions across the US. These texts are then professionally simplified, and verified and accepted by subject-matter experts who are full-time employees in admissions offices at various institutions. Additionally, PSAT comes with manual alignments of 1,883 original-simplified sentence pairs. The result is a first-of-its-kind corpus for the evaluation and fine-tuning of text simplification systems in a high-stakes genre distinct from existing simplification resources. PSAT is available at https://***/10.5281/zenodo.7055024. © 2022, CC BY.

关键词： Students

human Decision-making is Susceptible to AI-driven Manipulation

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arXiv 2025年

作者： Sabour, Sahand Liu, June M. Liu, Siyang Yao, Chris Z. Cui, Shiyao Zhang, Xuanming Zhang, Wen Cao, Yaru Bhat, Advait Guan, Jian Wu, Wei Mihalcea, Rada Althoff, Tim Lee, Tatia M.C. Huang, Minlie The CoAI Group DCST Institute for Artificial Intelligence Tsinghua University Beijing China State Key Laboratory of Brain and Cognitive Sciences The University of Hong Kong Hong Kong Laboratory of Neuropsychology and Human Neuroscience The University of Hong Kong Hong Kong The LIT Group Department of Computer Science and Engineering University of Michigan Ann Arbor United States Department of Psychology University of International Relations Beijing China Department of Chinese Language and Literature Northwest Minzu University Lanzhou China Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States ANT Group China

Artificial Intelligence (AI) systems are increasingly intertwined with daily life, assisting users in executing various tasks and providing guidance on decision-making. This integration introduces risks of AI-driven manipulation, where such systems may exploit users’ cognitive biases and emotional vulnerabilities to steer them toward harmful outcomes. Through a randomized controlled trial with 233 participants, we examined human susceptibility to such manipulation in financial (e.g., purchases) and emotional (e.g., conflict resolution) decision-making contexts. Participants interacted with one of three AI agents: a neutral agent (NA) optimizing for user benefit without explicit influence, a manipulative agent (MA) designed to covertly influence beliefs and behaviors, or a strategy-enhanced manipulative agent (SEMA) employing established psychological tactics to reach its hidden objectives. To ensure participant well-being, this study involved hypothetical scenarios, pre-screening for at-risk individuals, and comprehensive post-study debriefing. By analyzing participants’ decision patterns and shifts in their preference ratings post-interaction, we found significant susceptibility to AI-driven manipulation. Particularly, across both decision-making domains, participants interacting with the manipulative agents shifted toward harmful options at substantially higher rates (financial, MA: 62.3%, SEMA: 59.6%;emotional, MA: 42.3%, SEMA: 41.5%) compared to the NA group (financial, 35.8%;emotional, 12.8%). Notably, our results reveal that even simple manipulative objectives (MA) can be as effective as employing established psychological strategies (SEMA) in swaying human decision-making. These findings suggest that AI-driven manipulation could become widely spread, as it does not require sophisticated tactics and expertise to influence users. By revealing the potential for covert AI influence, this study highlights a critical vulnerability in human-AI interactions, emphasiz

关键词： Finance

Analytics in Higher Education: Scoping the Landscape of Research in the Area 11

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Analytics in Higher Education: Scoping the Landscape of Rese...

11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Kaul, Natashaa Deshpande, Amruta Mittal, Amit Jain, Vishal Symbiosis Center for Management and Human Resource Development Pune India India Symbiosis International University Pune India Indira School of Business Studies Pgdm Pune India Chitkara Business School Chitkara University Punjab India School of Engineering and Technology Sharda University Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9781665487320

Understanding the research landscape of any domain is of primary importance at all stages of a concept's evolution. Research in the domain of analytics has increased exponentially in the past decade, analytics is a tool being applied to increase effectiveness and efficiency within all domains. Similarly, there are numerous research studies that have applied analytics to the domain of higher education. Assessing research activity is important for planning future research and identifying the way forward to make meaningful contributions. The objective of this paper is to examine the current research in this domain and to identify emerging areas of research to make meaningful contributions. Bibliometric analysis was applied using Scopus. Documents on analytics and higher education keywords in business, accounting and management and published in journals were considered for the analysis. Vos Viewer was used for bibliometric analysis in the area, to identify emerging research themes and to understand the scope of applying analytics in higher education. The search query found 1635 documents when using the keywords of 'analytics' and 'higher education'. This was further narrowed down to research in Business Management and Accounting and using Journal articles and conference articles, finally yielding 103 articles. After a primary read to remove articles misplaced in the group the final number of articles include for the bibliometric analysis came down to 98. There has been an increase in publications since 2018 with interest increasing post pandemic in the area. The research categories are Student outcomes and perspective, academician outcomes and perspective, design of learning systems, etc. Based on this, it is recommended that research should focus on theoretical aspects of applying analytics to HEIs, ethical issues in data collection, etc.. © 2022 IEEE.

关键词： Learning systems

Prompt-Based Data Augmentation Using Contrastive Learning Under Scarcity of Annotated Data 27

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Prompt-Based Data Augmentation Using Contrastive Learning Un...

27th European Conference on Artificial Intelligence, ECAI 2024

作者： Ul Haq, Muhammad Uzair Rigoni, Davide Sperduti, Alessandro Department of Mathematics "Tullio Levi-Civita" University of Padova Via Trieste 63 Padova35121 Italy Human Inspired Technology Research Centre University of Padova Via Luzzatti 4 Padova35122 Italy Amajor SB S.p.A Via Noventana 192 Noventa Padovana35027 Italy Department of Pharmaceutical and Pharmacological Sciences University of Padova Via Marzolo 5 Padova35131 Italy Augmented Intelligence Center Bruno Kessler Foundation Via Sommarive 18 Povo38123 Italy Department of Information Engineering and Computer Science University of Trento Via Sommarive 9 TN Povo38123 Italy

ISBN: (纸本)9781643685489

Named Entity Recognition is a crucial task in Natural Language Processing (NLP) which aims to identify the entities in text. Given an adequate amount of annotated data, Large Language Models (LLMs) have been shown to be effective in this task when fine-tuned. However, the performance of LLMs is severely affected when annotated datasets are limited. To alleviate this problem, adding synthetic data via Data Augmentation (DA) techniques is a viable approach. Even so, DA for token-level tasks suffers from two main limitations: (i) token-label misalignment problem;and (ii) quality of generated synthetic data. In this paper, we propose a novel prompt-based DA approach using contrastive learning. The proposed method can generate high-quality synthetic data while preserving the token-label correspondences. Experimental results demonstrate that the proposed approach, when compared against multiple baselines on well-known Named Entity Recognition (NER) datasets, achieves State-of-the-Art performance. © 2024 The Authors.

关键词： Contrastive Learning

StyleTalk: One-shot Talking Head Generation with Controllable Speaking Styles

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arXiv 2023年

作者： Ma, Yifeng Wang, Suzhen Hu, Zhipeng Fan, Changjie Lv, Tangjie Ding, Yu Deng, Zhidong Yu, Xin Department of Computer Science and Technology BNRist THUAI State Key Laboratory of Intelligent Technology and Systems Tsinghua University China Virtual Human Group Netease Fuxi AI Lab China Zhejiang University China University of Technology Sydney Australia

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://***/FuxiVirtualhuman/styletalk. Copyright © 2023, The Authors. All rights reserved.

关键词： Decoding

Using EEG Signals to Assess Workload during Memory Retrieval in a Real-world Scenario

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arXiv 2023年

作者： Chiang, Kuan-Jung Dong, Steven Cheng, Chung-Kuan Jung, Tzyy-Ping Department of Computer Science and Engineering University of California San Diego United States Human Factors Center of Excellence Microsoft Corporation United States Institute for Neural Computation Institute of Engineering in Medicine University of California San Diego United States

Objective: The Electroencephalogram (EEG) is gaining popularity as a physiological measure for neuroergonomics in human factor studies because it is objective, less prone to bias, and capable of assessing the dynamics of cognitive states. This study investigated the associations between memory workload and EEG during participants’ typical office tasks on a single-monitor and dual-monitor arrangement. We expect a higher memory workload for the single-monitor arrangement. Approach: We designed an experiment that mimics the scenario of a subject performing some office work and examined whether the subjects experienced various levels of memory workload in two different office setups: 1) a single-monitor setup and 2) a dual-monitor setup. We used EEG band power, mutual information, and coherence as features to train machine learning models to classify high versus low memory workload states. Main results: The study results showed that these characteristics exhibited significant differences that were consistent across all participants. We also verified the robustness and consistency of these EEG signatures in a different data set collected during a Sternberg task in a prior study. Significance: The study found the EEG correlates of memory workload across individuals, demonstrating the effectiveness of using EEG analysis in conducting real-world neuroergonomic studies. © 2023, CC BY-NC-ND.

关键词： Electroencephalography

Dihydrotestosterone-androgen receptor signaling suppresses EBV-positive gastric cancer through DNA demethylation-mediated viral reactivation

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Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association 2025年 2025 Jun 12页

作者： Miyeon Cho Hyeji Byun Sun Hee Lee Sohyun Youn Inuk Jung Joohee Jung Junho Lee Hyosun Cho Hyojeung Kang Vessel-Organ Interaction Research Center Healthcare Convergence Educational Group for Infectious Disease Management Research Institute of Pharmaceutical Science College of Pharmacy Kyungpook National University 80 Daehak-ro Buk-gu Daegu 702-701 Republic of Korea. Department of Computer Science and Engineering Kyungpook National University Daegu Korea. Duksung Innovative Drug Center College of Pharmacy Duksung Women's University Seoul Korea. Department of Veterinary Surgery College of Veterinary Medicine Kyungpook National University Daegu Korea. Vessel-Organ Interaction Research Center Healthcare Convergence Educational Group for Infectious Disease Management Research Institute of Pharmaceutical Science College of Pharmacy Kyungpook National University 80 Daehak-ro Buk-gu Daegu 702-701 Republic of Korea. hkang72@knu.ac.kr.

BACKGROUND:Our Kaplan-Meier analysis reveals that gastric cancer patients with high androgen receptor (AR) expression demonstrate poorer survival outcomes compared to those with low AR expression, particularly in patients with characteristics typical of EBV-positive gastric cancer. However, the molecular mechanisms driving this seemingly contradictory relationship have remained poorly understood, as our experimental findings suggest AR signaling actually suppresses tumor growth in EBVaGC. METHODS:The study utilized AR-positive EBV-infected gastric cancer cell lines treated with dihydrotestosterone (DHT) to investigate molecular pathways. Comprehensive analyses included examination of apoptosis, miRNA expression, signaling pathways, DNA methylation patterns, and viral gene expression. In vivo validation was performed using xenograft models with MKN1-EBV and SNU719 cells to assess tumor growth and immune response. RESULTS:DHT treatment triggered early apoptosis through upregulation of pro-apoptotic miRNAs, particularly miR-204-5p, while activating the PI3K-Akt pathway and enhancing DNA damage response through increased phosphorylation of key proteins. The treatment reduced DNMT3A expression, leading to genome-wide DNA demethylation and increased expression of both lytic (BZLF1) and latent (EBNA1, LMP1) EBV genes. Xenograft studies confirmed these findings, showing reduced tumor growth, increased lymphocyte infiltration, and enhanced viral gene expression specifically in AR-positive tumors. CONCLUSION:The study reveals that AR signaling suppresses EBV-positive gastric cancer by modulating both cellular apoptosis and EBV reactivation through epigenetic mechanisms. These findings suggest that AR agonists might have therapeutic potential in treating AR-positive EBV-associated gastric cancer.

关键词： Androgen receptor DNA methylation Dihydrotestosterone Epigenetic regulation Epstein-Barr virus Gastric cancer