Review
Biochemical Research Methods
Fei Wang, Yulian Ding, Xiujuan Lei, Bo Liao, Fang-Xiang Wu
Summary: Drug repositioning is an important method for exploring new uses of existing drugs in drug discovery, especially in pre-clinical stages. Computational approaches, including machine learning and deep learning, have shown great potential in saving time and reducing costs compared to traditional drug discovery methods.
CURRENT BIOINFORMATICS
(2022)
Review
Biochemistry & Molecular Biology
Sha Zhu, Qifeng Bai, Lanqing Li, Tingyang Xu
Summary: Drug repositioning plays a significant role in drug development and machine learning methods can accelerate this process. This article focuses on the repurposing potential of type 2 diabetes mellitus drugs for various diseases.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Biochemistry & Molecular Biology
Milan Picard, Marie -Pier Scott-Boyer, Antoine Bodein, Olivier Perin, Arnaud Droit
Summary: The increasing availability of high-throughput technologies has led to the generation of a growing number of omics data, representing various biological layers like genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Machine learning algorithms have been utilized to extract new insights and develop diagnostic biomarkers from these data, but most biomarkers only consider a single omic measurement at a time. Multi-omics data integration strategies are necessary to leverage the complementary knowledge from each omics layer, with five different integration strategies summarized in this mini-review.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2021)
Review
Biochemical Research Methods
Apurva Badkas, Sebastien De Landtsheer, Thomas Sauter
Summary: Drug repositioning has gained significant attention in the past decade. Computational approaches, particularly network-based methods, have played a crucial role in uncovering unintuitive functional relationships and identifying repositioning candidates in drug-disease and other networks. Various structural network measures contribute to these efforts and hold potential for wider applications, especially in the field of drug repositioning.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Computer Science, Theory & Methods
Dylan Chou, Meng Jiang
Summary: This survey presents the challenges faced by data-driven network intrusion detection, including the authenticity and representativeness of datasets. Trends in the past decade are analyzed, and future directions are proposed, including the application of NID in cloud-based environments, designing scalable models for large network data, and collecting labeled datasets from real-world networks.
ACM COMPUTING SURVEYS
(2022)
Article
Biochemistry & Molecular Biology
Lu Lu, Jiale Qin, Jiandong Chen, Na Yu, Satoru Miyano, Zhenzhong Deng, Chen Li
Summary: This article reviews the progress of drug repositioning and combination efforts for better treatment of COVID-19. The study found that graph theory and neural network were the most commonly used strategies with high potential for drug repositioning.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Review
Biochemical Research Methods
Huimin Luo, Min Li, Mengyun Yang, Fang-Xiang Wu, Yaohang Li, Jianxin Wang
Summary: Drug repositioning, through computational methods, can systematically identify potential drug-target interactions and drug-disease interactions, significantly reducing cost and duration. This review summarizes available biomedical data and public databases, discusses various drug repositioning approaches, and analyzes common data sets and evaluation metrics used in this field.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Xinxing Yang, Genke Yang, Jian Chu
Summary: This paper proposes a multi-task self-supervised learning framework for computational drug repositioning. The framework addresses label sparsity by learning a better drug representation and uses auxiliary tasks to improve the prediction accuracy of the main task. Experimental results demonstrate the effectiveness of this framework.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Mathematics, Applied
Ying Ying Keng, Kiam Heong Kwa, Kurunathan Ratnavelu
Summary: The study demonstrates the significance of central drugs in a drug network for drug repositioning, suggesting that top central drugs are more likely to repurpose their neighboring drugs as new treatment options. This research provides novel insights into complementing drug repositioning efforts and highlights the importance of network centrality measures in guiding systematic analysis.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Biochemical Research Methods
Xinxing Yang, Genke Yang, Jian Chu
Summary: Computational drug repositioning technology is an effective tool for accelerating drug development. However, existing models have limitations such as a large number of unvalidated drug-disease associations and issues with the inner product. This study proposes a novel PUON framework to address these deficiencies and extensive experiments on four real-world datasets showed that the PUON model achieved the best performance based on six evaluation metrics.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Qiuping Ma, Hongyan Li, Anders Thorstenson
Summary: This study proposes a big data-driven root cause analysis system utilizing Machine Learning techniques to improve the accuracy and efficiency of root cause analysis. By applying a unified feature-based approach and supervised Machine Learning methods, the system can predict root causes of quality problems and save time and cost for manufacturing companies. The research aims to bridge the gap between the theoretical development of Machine Learning methods and their practical application in operations management.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Ghodai Abdelrahman, Qing Wang
Summary: Teaching plays a crucial role in human learning, but current machine teaching methods often neglect the underlying learning concepts in a learning task by directly assessing individual training samples. In this paper, a novel method called Knowledge Augmented Data Teaching (KADT) is proposed to optimize the data teaching strategy by tracking the knowledge progress of a student model over multiple learning concepts. The experimental results show that the KADT method consistently outperforms state-of-the-art methods in various machine learning tasks, including knowledge tracing, sentiment analysis, movie recommendation, and image classification.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Pharmacology & Pharmacy
Rui Xuan Huang, Damrongrat Siriwanna, William C. Cho, Tsz Kin Wan, Yan Rong Du, Adam N. Bennett, Qian Echo He, Jun Dong Liu, Xiao Tai Huang, Kei Hang Katie Chan
Summary: In this study, a pipeline based on machine learning was developed to predict potential target genes for LUAD and discover potential drugs for its treatment through drug repositioning. The pipeline achieved good predictive performance and identified several potential therapeutic drugs for LUAD.
FRONTIERS IN PHARMACOLOGY
(2022)
Review
Biochemical Research Methods
Xiaoxia Wen, Ping Leng, Jiasi Wang, Guishu Yang, Ruiling Zu, Xiaojiong Jia, Kaijiong Zhang, Birga Anteneh Mengesha, Jian Huang, Dongsheng Wang, Huaichao Luo
Summary: The recent focus on big data in medicine and the rise of artificial intelligence (AI) have the potential to greatly impact healthcare. The combination of medicine and AI, especially in the field of clinical laboratory data, shows promising applications and the need for further validation.
BMC BIOINFORMATICS
(2022)
Article
Materials Science, Multidisciplinary
Mahsa Golmohammadi, Masoud Aryanpour
Summary: Machine Learning (ML) has become the main approach in tackling the challenges and opportunities in the Information Age. This article presents a review of the applications of ML models in Materials Science in recent years. The similarities and differences between Machine Learning and Screening approaches are highlighted, with a focus on direct ML applications. The article provides valuable information, data, and guidelines for researchers in the field of Materials Science.
MATERIALS TODAY COMMUNICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Shang Gao, Alan Chen, Ali Rahmani, Tamer Jarada, Reda Alhajj, Doug Demetrick, Jia Zeng
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2013)
Article
Computer Science, Artificial Intelligence
Ela Yildizer, Ali Metin Balci, Tamer N. Jarada, Reda Alhajj
KNOWLEDGE-BASED SYSTEMS
(2012)
Article
Biochemical Research Methods
Ala Qabaja, Tamer Jarada, Abdallah Elsheikh, Reda Alhajj
JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY
(2014)
Article
Biochemical Research Methods
Tamer N. Jarada, Jon G. Rokne, Reda Alhajj
Summary: Drug repositioning, using similarity measures and deep learning models, shows significant potential for predicting novel drug-disease interactions. SNF-NN outperforms baseline methods and recent state-of-the-art methods in terms of performance and robustness, indicating its efficiency in predicting new drug-disease interactions.
BMC BIOINFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Tamer N. Jarada, Jon G. Rokne, Reda Alhajj
Summary: Drug repositioning is an emerging approach to identify novel therapeutic potentials for approved drugs and discover therapies for previously untreatable diseases. Recent research has shown that integrating similarity measures and deep learning models can greatly improve the accuracy of predicting novel drug-disease interactions in computational drug repositioning.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Public, Environmental & Occupational Health
R. Liam Sutherland, Devon J. Boyne, Tamer N. Jarada, Lisa M. Lix, Jill Tinmouth, Linda Rabeneck, Steven J. Heitman, Nauzer Forbes, Robert J. Hilsden, Darren R. Brenner
Summary: This study aimed to develop a risk prediction model for high risk adenomas (HRAs) detected at screening colonoscopy using readily available participant information. The model showed moderate predictive ability, with a strong capability in ruling out individuals without HRAs and potential for improving resource allocation and screening efficiency.
PREVENTIVE MEDICINE
(2021)
Meeting Abstract
Oncology
Dylan E. O'Sullivan, Devon J. Boyne, Iqra A. Syed, Cal S. Shephard, Derek L. Clouthier, Eric M. Yoshida, Jennifer L. Spratlin, Atul Batra, Rodrigo Rigo, Malek Hannouf, Xun Yang Hu, Tamer N. Jarada, Darren Brenner, Winson Y. Cheung
JOURNAL OF CLINICAL ONCOLOGY
(2022)
Meeting Abstract
Oncology
Tamer N. Jarada, Geoffrey Gotto, Bimal Bhindi, Natalie Beaton, Tahir Feroz, Teagan Maier-Downing, Darren Brenner, Winson Y. Cheung, Devon J. Boyne
JOURNAL OF CLINICAL ONCOLOGY
(2022)
Article
Oncology
Robert B. Basmadjian, Shiying Kong, Devon J. Boyne, Tamer N. Jarada, Yuan Xu, Winson Y. Cheung, Sasha Lupichuk, May Lynn Quan, Darren R. Brenner
Summary: This study focuses on developing prediction models for pathologic complete response (pCR) among breast cancer patients. Two types of models were compared, and machine learning methods were shown to be effective in optimizing pCR prediction models. Additional variables beyond clinical expertise did not significantly improve predictive ability.
JCO CLINICAL CANCER INFORMATICS
(2022)
Article
Oncology
Tamer N. Jarada, Dylan E. O'Sullivan, Darren R. Brenner, Winson Y. Cheung, Devon J. Boyne
Summary: Real-world evidence is used to evaluate emerging therapies, but there may be selection bias in these investigations. This study quantified the bias by examining outcomes in individuals with metastatic cancer in Alberta, Canada. It found that referral to a medical oncologist was associated with higher rates of systemic therapy initiation and longer median overall survival.
Article
Oncology
Yibing Ruan, Emily Heer, Matthew T. Warkentin, Tamer N. Jarada, Dylan E. O'Sullivan, Desiree Hao, Doreen Ezeife, Winson Cheung, Darren R. Brenner
Summary: This study found an association between neighborhood-level income and cancer stage at diagnosis as well as cancer-specific mortality in Alberta, Canada.
Article
Oncology
Nimira Alimohamed, Simrun Grewal, Heidi S. Wirtz, Zsolt Hepp, Stephanie Sauvageau, Devon J. Boyne, Darren R. Brenner, Winson Y. Cheung, Tamer N. Jarada
Summary: Real-world data on treatment patterns and survival outcomes of patients with unresectable locally advanced or metastatic urothelial carcinoma (la/mUC) in Canada are limited. This study found that the majority of patients did not receive systemic therapy, and those who did had poor survival outcomes. The results highlight the significant unmet need for safe and efficacious therapies for la/mUC patients in Canada.
Article
Oncology
Dylan E. O'Sullivan, Tamer N. Jarada, Amman Yusuf, Leo (Xun Yang) Hu, Priyanka Gogna, Darren R. Brenner, Erica Abbie, Jennifer B. Rose, Kiefer Eaton, Julia Elia-Pacitti, Emmanuel M. Ewara, Aliyah Pabani, Winson Y. Cheung, Devon J. Boyne
Summary: This study used administrative databases in Alberta, Canada to evaluate EGFR testing and mutation prevalence in EGFR positive NSCLC patients, as well as their characteristics, treatment patterns, and outcomes. The findings suggest that Exon20ins mutations represent a rare subset of NSCLC with limited treatment options and worse survival outcomes compared to more common types of EGFR mutations.
Article
Mathematical & Computational Biology
Wadhah Almansoori, Shang Gao, Tamer N. Jarada, Abdallah M. Elsheikh, Ayman N. Murshed, Jamal Jida, Reda Alhajj, Jon Rokne
NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS
(2012)