Article
Biochemistry & Molecular Biology
Tzu-Hsien Yang, Chung-Yu Wang, Hsiu-Chun Tsai, Ya-Chiao Yang, Cheng-Tse Liu
Summary: Cells adapt to environmental stresses through transcription reprogramming, and the interactions between transcription factors (TF) and target genes play a key role in correct transcription control. YTLR is a pipeline tool that automates the extraction of TF-gene relations from literature, achieving high performance in the tasks.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Seonghyeon Moon, Gitaek Lee, Seokho Chi
Summary: This research aimed to develop an automated system for reviewing construction specifications by analyzing the different semantic properties using natural language processing techniques. The proposed system showed promising results in reducing time, supplementing reviewer's experience, enhancing accuracy, and achieving consistency, contributing positively to risk management in the construction industry.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Software Engineering
Jaydeb Sarker, Asif Kamal Turzo, Ming Dong, Amiangshu Bosu
Summary: Toxic conversations during software development can have detrimental effects on FOSS projects, leading to demotivation and attrition. ToxiCR, a supervised learning based toxicity identification tool, outperforms existing detectors by achieving 95.8% accuracy and an 88.9% F1-score in identifying toxic texts in code review comments.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2023)
Article
Computer Science, Information Systems
Syed Ali Raza, Sagheer Abbas, Taher M. Ghazal, Muhammad Adnan Khan, Munir Ahmad, Hussam Al Hamadi
Summary: In the world of big data, organizing files based on their similarities is a challenging task. This research proposes an automated file organization system that categorizes files based on their content similarities using supervised and unsupervised machine learning approaches, and demonstrates its effectiveness and efficiency in real-world experiments.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Interdisciplinary Applications
Hammami Lindaa, Paglialonga Alessia, Pruneri Giancarlo, Torresani Michele, Sant Milenaa, Bono Carlo, Caiani Enrico Gianluca, Baili Paolo
Summary: Pathology reports are crucial for cancer registries but manual extraction and coding of information from these reports is labor-intensive. Currently, there is limited availability of NLP approaches for pathology reports in languages other than English like Italian. This study aimed to develop an automated algorithm based on NLP techniques to efficiently process Italian pathology reports.
JOURNAL OF BIOMEDICAL INFORMATICS
(2021)
Article
Chemistry, Multidisciplinary
Jieh-Haur Chen, Mu-Chun Su, Vidya Trisandini Azzizi, Ting-Kwei Wang, Wei-Jen Lin
Summary: The research aims to address communication interaction issues in the construction industry using technology, and has successfully developed an automatic contract management platform by extracting key contract keywords with tools like PSENet, CRNN, and BRNN-CNN.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Biomedical
Javier O. Corvi, Austin McKitrick, Jose M. Fernandez, Carla V. Fuenteslopez, Josep L. Gelpi, Maria-Pau Ginebra, Salvador Capella-Gutierrez, Osnat Hakimi
Summary: This article introduces an automated system for extracting biomaterial-related information from MEDLINE research abstracts using text mining technologies. The system identifies 16 concept types related to biomaterials and deposits them, along with the abstract and relevant metadata, into the DEBBIE database. DEBBIE is accessible through a web application that enables keyword searches and displays results in a user-friendly manner, facilitating efficient organization and mapping of biomaterials information.
ADVANCED HEALTHCARE MATERIALS
(2023)
Article
Construction & Building Technology
Seonghyeon Moon, Seokho Chi, Seok-Been Im
Summary: This paper presents a clause classification model based on the BERT method for construction specifications, which demonstrates excellent performance in various risk categories. It contributes to improving the review process and risk management in the construction industry.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Psychology, Multidisciplinary
Carly Fox, Sharad Jones, Sandra Laing Gillam, Megan Israelsen-Augenstein, Sarah Schwartz, Ronald Bradley Gillam
Summary: The study developed the LLUNA system for automatically evaluating six aspects of literate language in narratives, showing strong inter-rater reliability with expert scorers and surpassing reliability levels of non-expert scorers in four aspects. The system has potential for automating scoring of literate language in language sample analysis and narrative samples for assessment and progress-monitoring purposes.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Ecology
Ritwik Kulkarni, Enrico Di Minin
Summary: The study demonstrates the successful application of natural language processing to extract information from digital text content, showcasing the potential for investigating human-nature interactions in conservation science and practice. The automated methods developed can be applied to multiple digital data platforms simultaneously, offering a cost-efficient and effective approach to addressing global biodiversity crisis.
METHODS IN ECOLOGY AND EVOLUTION
(2021)
Article
Computer Science, Information Systems
Badriyya B. Al-onazi, Saud S. Alotaib, Saeed Masoud Alshahrani, Najm Alotaibi, Mrim M. Alnfiai, Ahmed S. Salama, Manar Ahmed Hamza
Summary: This study investigates challenges in Arabic text classification and proposes a new model, AATC-HTHDL, which achieves superior performance compared to other approaches.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Computer Science, Information Systems
Youren Yu, Yangsen Zhang, Xueyang Liu, Siwen Zhu
Summary: This paper proposes a novel relationship extraction model that effectively utilizes interaction information between subjects and objects and captures spatial location relationships between entities. Experimental results show that the model outperforms state-of-the-art models on multiple datasets.
Article
Psychology, Mathematical
Miriam Koschate, Elahe Naserian, Luke Dickens, Avelie Stuart, Alessandra Russo, Mark Levine
Summary: Group and category memberships play a crucial role in shaping our thoughts, emotions, behavior, and social relations, and are increasingly linked to our mental and physical well-being. However, current assessment methods lack applications to natural data, which ASIA addresses by providing researchers with a tool to understand the dynamics and impact of group memberships.
BEHAVIOR RESEARCH METHODS
(2021)
Article
Rehabilitation
Adarsh R. Hegde, R. S. Sujala Reddy, P. Kruthika, B. C. Pragathi, Sreerama Sai Lahari, N. Deepamala, G. Shobha
Summary: This paper highlights the challenges faced by monolingual individuals in India and proposes a solution called Dhvani voicebot, which helps users identify suitable government schemes and fill out forms.
DISABILITY AND REHABILITATION-ASSISTIVE TECHNOLOGY
(2023)
Article
Chemistry, Multidisciplinary
Jin Tao, Kelly A. Brayton, Shira L. Broschat
Summary: This study introduces a novel approach using ensemble learning and natural language processing to verify protein annotation, ensuring accuracy. The model achieves good results in experiments and outperforms other models.
APPLIED SCIENCES-BASEL
(2021)
Article
Psychology, Biological
Jordi Merino, Hassan S. Dashti, Chloe Sarnowski, Jacqueline M. Lane, Petar Todorov, Miriam S. Udler, Yanwei Song, Heming Wang, Jaegil Kim, Chandler Tucker, John Campbell, Toshiko Tanaka, Audrey Y. Chu, Linus Tsai, Tune H. Pers, Daniel Chasman, Martin K. Rutter, Josee Dupuis, Jose C. Florez, Richa Saxena
Summary: In a multivariate genetic analysis, 26 genomic regions associated with carbohydrate, protein and fat intake were identified, implicating brain regions and neuronal subtypes in influencing eating behaviour. This research enhances understanding of individual differences in dietary intake by highlighting neural mechanisms and potentially offering new avenues for the prevention and treatment of complex metabolic diseases.
NATURE HUMAN BEHAVIOUR
(2022)
Article
Multidisciplinary Sciences
I-M Launonen, N. Lyytikainen, J. Casado, E. A. Anttila, A. Szabo, U-M Haltia, C. A. Jacobson, J. R. Lin, Z. Maliga, B. E. Howitt, K. C. Strickland, S. Santagata, K. Elias, A. D. D'Andrea, P. A. Konstantinopoulos, P. K. Sorger, A. Farkkila
Summary: In this study, the authors used highly multiplexed imaging to analyze the immune microenvironment of high-grade serous ovarian cancers (HGSOC) and identified phenotypic characteristics and spatial interactions associated with BRCA1/2 gene mutations. These findings have important implications for improving immunotherapeutic strategies and patient stratification.
NATURE COMMUNICATIONS
(2022)
Review
Oncology
Deborah Plana, Adam C. Palmer, Peter K. Sorger
Summary: Combination therapies are more effective in treating cancer than monotherapy, addressing tumor heterogeneity. The model of independent drug action provides multiple opportunities for benefit from at least one drug, and personalized, targeted combination therapy can increase therapeutic benefits.
Article
Engineering, Biomedical
Rumana Rashid, Yu-An Chen, John Hoffer, Jeremy L. Muhlich, Jia-Ren Lin, Robert Krueger, Hanspeter Pfister, Richard Mitchell, Sandro Santagata, Peter K. Sorger
Summary: This Perspective discusses the importance of the software ecosystem for tissue image analysis and emphasizes the need for interactive online guides to help histopathologists make complex images understandable to non-specialists. The concept of software interfaces like Minerva, which integrates multi-omic and tissue-atlas features and can be accessed via web browsers, is introduced to effectively disseminate digital histology data and aid their interpretation.
NATURE BIOMEDICAL ENGINEERING
(2022)
Article
Biology
Sanja Vickovic, Denis Schapiro, Konstantin Carlberg, Britta Lotstedt, Ludvig Larsson, Franziska Hildebrandt, Marina Korotkova, Aase H. Hensvold, Anca Catrina, Peter K. Sorger, Vivianne Malmstrom, Aviv Regev, Patrik L. Stahl
Summary: Spatial transcriptomics is used to study the local interactions in synovial tissue of rheumatoid arthritis patients. The results provide valuable insights into the spatial organization of cell populations and inflammation associated with rheumatoid arthritis.
COMMUNICATIONS BIOLOGY
(2022)
Meeting Abstract
Oncology
Kenichi Shimada, Yvonne X. Cui, Jonathan S. Goldberg, Ricardo Pastorello, Janae Davis, Tuulia Vallius, Lukas Kania, Ashka Patel, Mckenna Moore, Esther R. Ogayo, Deborah Dillon, Peter K. Sorger, Jennifer L. Guerriero, Elizabeth A. Mittendorf
Correction
Biotechnology & Applied Microbiology
Ratul Chowdhury, Nazim Bouatta, Surojit Biswas, Christina Floristean, Anant Kharkar, Koushik Roy, Charlotte Rochereau, Gustaf Ahdritz, Joanna Zhang, George M. Church, Peter K. Sorger, Mohammed AlQuraishi
NATURE BIOTECHNOLOGY
(2022)
Article
Biotechnology & Applied Microbiology
Ratul Chowdhury, Nazim Bouatta, Surojit Biswas, Christina Floristean, Anant Kharkare, Koushik Roye, Charlotte Rochereau, Gustaf Ahdritz, Joanna Zhang, George M. Church, Peter K. Sorger, Mohammed AlQuraishi
Summary: This article reports the development of an end-to-end differentiable recurrent geometric network (RGN) called RGN2 for predicting protein structure using the protein sequence. Compared to AlphaFold2 and RoseTTAFold, RGN2 performs better on orphan proteins and designed protein classes, while achieving a computational speedup of up to 10^6-fold. These findings demonstrate the practical and theoretical advantages of protein language models over multiple sequence alignments (MSAs) in protein structure prediction.
NATURE BIOTECHNOLOGY
(2022)
Article
Multidisciplinary Sciences
Giulia Notarangelo, Jessica B. Spinelli, Elizabeth M. Perez, Gregory J. Baker, Kiran Kurmi, Ilaria Elia, Sylwia A. Stopka, Gerard Baquer, Jia-Ren Lin, Alexandra J. Golby, Shakchhi Joshi, Heide F. Baron, Jefte M. Drijvers, Peter Georgiev, Alison E. Ringel, Elma Zaganjor, Samuel K. McBrayer, Peter K. Sorger, Arlene H. Sharpe, Kai W. Wucherpfennig, Sandro Santagata, Nathalie Y. R. Agar, Mario L. Suva, Marcia C. Haigis
Summary: Gain-of-function mutations in IDH result in the production of oncometabolite D-2HG, which promotes tumorigenesis by affecting the metabolism and antitumor functions of CD8(+) T cells.
Article
Oncology
Guihong Wan, Nga Nguyen, Feng Liu, Mia S. DeSimone, Bonnie W. Leung, Ahmad Rajeh, Michael R. Collier, Min Seok Choi, Munachimso Amadife, Kimberly Tang, Shijia Zhang, Jordan S. Phillipps, Ruple Jairath, Nora A. Alexander, Yining Hua, Meng Jiao, Wenxin Chen, Diane Ho, Stacey Duey, Istvan Balazs Nemeth, Gyorgy Marko-Varga, Jeovanis Gil Valdes, David Liu, Genevieve M. Boland, Alexander Gusev, Peter K. Sorger, Kun-Hsing Yu, Yevgeniy R. Semenov
Summary: This study assesses the effectiveness of machine-learning algorithms in predicting melanoma recurrence based on clinical and histopathologic features. The results show that machine-learning algorithms can extract predictive signals from these features and achieve high prediction performance, enabling the identification of patients who may benefit from adjuvant immunotherapy.
NPJ PRECISION ONCOLOGY
(2022)
Article
Biology
Sean M. Gross, Mark A. Dane, Rebecca L. Smith, Kaylyn L. Devlin, Ian C. McLean, Daniel S. Derrick, Caitlin E. Mills, Kartik Subramanian, Alexandra B. London, Denis Torre, John Erol Evangelista, Daniel J. B. Clarke, Zhuorui Xie, Cemal Erdem, Nicholas Lyons, Ted Natoli, Sarah Pessa, Xiaodong Lu, James Mullahoo, Jonathan Li, Miriam Adam, Brook Wassie, Moqing Liu, David F. Kilburn, Tiera A. Liby, Elmar Bucher, Crystal Sanchez-Aguila, Kenneth Daily, Larsson Omberg, Yunguan Wang, Connor Jacobson, Clarence Yapp, Mirra Chung, Dusica Vidovic, Yiling Lu, Stephan Schurer, Albert Lee, Ajay Pillai, Aravind Subramanian, Malvina Papanastasiou, Ernest Fraenkel, Heidi S. Feiler, Gordon B. Mills, Jake D. Jaffe, Avi Ma'ayan, Marc R. Birtwistle, Peter K. Sorger, James E. Korkola, Joe W. Gray, Laura M. Heiser
Summary: The phenotype and molecular state of a cell are influenced by external signals, and dysregulation of these signals can lead to various diseases. To understand the relationship between molecular and phenotypic changes, researchers have generated a comprehensive dataset that documents the transcriptional, proteomic, epigenomic, and phenotypic responses of MCF10A mammary epithelial cells after exposure to different ligands. This dataset serves as a valuable resource for the scientific community to gain biological insights, compare signals across different molecular modalities, and develop new computational methods for data analysis.
COMMUNICATIONS BIOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Jia-Ren Lin, Shu Wang, Shannon Coy, Yu -An Chen, Clarence Yapp, Madison Tyler, Maulik K. Nariya, Cody N. Heiser, Ken S. Lau, Sandro Santagata, Peter K. Sorger
Summary: We used advanced imaging techniques, 3D reconstruction, spatial statistics, and machine learning to identify cell types and states associated with diagnostic and prognostic features in colorectal cancer. We found that at the tumor invasive margin, T cell suppression involves multiple cell types and that seemingly localized features such as tertiary lymphoid structures are interconnected and have graded molecular properties. These findings challenge the notion that discrete changes in tumor state are most important, demonstrating the presence of large-scale morphological and molecular gradients.
Article
Biochemistry & Molecular Biology
Fabian Froehlich, Luca Gerosa, Jeremy Muhlich, Peter K. Sorger
Summary: In this article, the mechanisms of adaptive rewiring in BRAF(V600E) melanoma cells were studied using an energy-based implementation of ordinary differential equation (ODE) modeling in combination with proteomic, transcriptomic and imaging data. Two parallel MAPK reaction channels were identified, which showed differential sensitivity to RAF and MEK inhibitors due to differences in protein oligomerization and drug binding. The study also revealed the time scale separation between immediate-early signaling and transcriptional feedback, creating a state in which the RAS-regulated MAPK channel can be activated by growth factors under conditions of fully inhibited BRAF(V600E)-driven channel. Further development of the approaches in this article is expected to yield a unified model of adaptive drug resistance in melanoma.
MOLECULAR SYSTEMS BIOLOGY
(2023)
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
Chemistry, Medicinal
Changchang Liu, Peter Kutchukian, Nhan D. Nguyen, Mohammed AlQuraishi, Peter K. Sorger
Summary: This study presents a computational approach for qualitative and quantitative kinome-wide binding measurements using structure-based machine learning. The approach outperforms methods trained on crystal structures alone and structure-free methods in predicting kinase-compound interaction affinities. It also successfully captures known kinase biochemistry and generalizes well to distant kinase sequences and compound scaffolds.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
