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
Rufeng Li, Lixin Li, Yungang Xu, Juan Yang
Summary: The innovation of biotechnologies has accelerated the accumulation of omics data, leading to the era of 'big data'. Extracting valuable knowledge from omics data remains a challenging issue that requires innovative methods. The development and application of machine learning have greatly enhanced insights into biology and biomedicine, particularly in the field of precision medicine.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Sehwan Moon, Jeongyoung Hwang, Hyunju Lee
Summary: Integration of multi-omics data using the proposed supervised deep generalized canonical correlation analysis (SDGCCA) method improves phenotypic classification and biomarker identification. By considering complex/nonlinear cross-data correlations between multiple modalities, SDGCCA outperforms other methods in predicting Alzheimer's disease (AD) and discriminating early- and late-stage cancers. Additionally, SDGCCA enables feature selection and identifies important multi-omics biomarkers associated with AD.
JOURNAL OF COMPUTATIONAL BIOLOGY
(2022)
Review
Cell Biology
Wentao Li, Chongyu Shao, Huifen Zhou, Haixia Du, Haiyang Chen, Haitong Wan, Yu He
Summary: Ischemic stroke is a multifactorial neurological disorder with no biomarkers and unclear molecular mechanisms. Multiple omics approaches can provide insights into its pathogenesis and biomarker discovery, but single omics approaches have limitations. Integrated analysis of multi-omics data can enhance our understanding of stroke mechanisms and therapeutic targets.
AGEING RESEARCH REVIEWS
(2022)
Article
Mathematics
Georgiana Ingrid Stoleru, Adrian Iftene
Summary: Alzheimer's Disease is a prevalent condition that is often diagnosed late. Due to limitations in current diagnostic tools, developing an early predictive system based on Artificial Intelligence and identifying biomarkers are important research directions. This survey reviews the use of machine learning techniques for the detection of Alzheimer's Disease and Mild Cognitive Impairment, aiming to identify the most accurate and efficient diagnostic approaches.
Article
Biochemistry & Molecular Biology
Maxime Francois, Avinash Karpe, Jian-Wei Liu, David J. Beale, Maryam Hor, Jane Hecker, Jeff Faunt, John Maddison, Sally Johns, James D. Doecke, Stephen Rose, Wayne R. Leifert
Summary: The metabolomic and proteomic basis of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is not well understood. This study examined the plasma samples of individuals with MCI or AD, as well as age- and gender-matched cognitively normal individuals, to identify cellular pathways and biomarkers associated with the diseases. The analysis revealed specific proteins that distinguish AD from MCI and cognitively normal groups, and identified various metabolic pathways affected in AD. These findings contribute to the understanding of the diseases and may be useful for future clinical trials.
Review
Biotechnology & Applied Microbiology
Parminder S. Reel, Smarti Reel, Ewan Pearson, Emanuele Trucco, Emily Jefferson
Summary: With the advancement of high-throughput omics technologies, it is crucial for biomedical research to adopt integrative approaches to analyze diverse omics data using machine learning algorithms. This can lead to the discovery of novel biomarkers and improve disease prediction and precision medicine delivery.-Methods in machine learning have enabled researchers to gain a deeper insight into biological systems and provide recommendations for interdisciplinary professionals looking to incorporate machine learning skills in multi-omics studies.
BIOTECHNOLOGY ADVANCES
(2021)
Article
Genetics & Heredity
Haifeng Xu, Tonje Lien, Helga Bergholtz, Thomas Fleischer, Lounes Djerroudi, Anne Vincent-Salomon, Therese Sorlie, Tero Aittokallio
Summary: DCIS is a preinvasive form of breast cancer without accurate markers for distinguishing cases needing treatment. A machine learning approach was developed to identify biomarker combinations for distinguishing low-risk DCIS lesions. Features selected through multi-omics analysis were able to improve accuracy in classifying DCIS cases.
FRONTIERS IN GENETICS
(2021)
Review
Biotechnology & Applied Microbiology
Harpreet Kaur, Yuvraj Singh, Surjeet Singh, Raja B. Singh
Summary: The gut-brain axis is a biochemical link connecting the central nervous system and enteric nervous system, with gut microbiota playing a key regulatory role and being associated with the pathogenesis of various neurological disorders.
Article
Clinical Neurology
Patrick H. Luckett, Charlie Chen, Brian A. Gordon, Julie Wisch, Sarah B. Berman, Jasmeer P. Chhatwal, Carlos Cruchaga, Anne M. Fagan, Martin R. Farlow, Nick C. Fox, Mathias Jucker, Johannes Levin, Colin L. Masters, Hiroshi Mori, James M. Noble, Stephen Salloway, Peter R. Schofield, Adam M. Brickman, William S. Brooks, David M. Cash, Michael J. Fulham, Bernardino Ghetti, Clifford R. Jack, Jonathan Voeglein, William E. Klunk, Robert Koeppe, Yi Su, Michael Weiner, Qing Wang, Daniel Marcus, Deborah Koudelis, Nelly Joseph-Mathurin, Lisa Cash, Russ Hornbeck, Chengjie Xiong, Richard J. Perrin, Celeste M. Karch, Jason Hassenstab, Eric McDade, John C. Morris, Tammie L. S. Benzinger, Randall J. Bateman, Beau M. Ances
Summary: This study analyzed 19 biomarkers of Alzheimer's disease using hierarchical clustering and feature selection, and found that amyloid and tau measures were the primary predictors. Emerging biomarkers of neuronal integrity and inflammation showed weaker predictive ability.
ALZHEIMERS & DEMENTIA
(2023)
Article
Medicine, General & Internal
Giuseppe Murdaca, Sara Banchero, Alessandro Tonacci, Alessio Nencioni, Fiammetta Monacelli, Sebastiano Gangemi
Summary: The study suggests that low levels of vitamin D and folic acid may be associated with cognitive decline in Alzheimer's disease patients. Patients with simultaneous low levels of vitamin D and folic acid may have poorer cognitive function.
Article
Medicine, General & Internal
Taeho Jo, Junpyo Kim, Paula Bice, Kevin Huynh, Tingting Wang, Matthias Arnold, Peter J. Meikle, Corey Giles, Rima Kaddurah-Daouk, Andrew J. Saykin, Kwangsik Nho
Summary: This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting Alzheimer's Disease (AD) using serum-based metabolomics data. The results show that c-SWAT is effective in improving classification accuracy and in identifying key lipids associated with AD.
Article
Medicine, General & Internal
Parminder S. Reel, Smarti Reel, Josie C. van Kralingen, Katharina Langton, Katharina Lang, Zoran Erlic, Casper K. Larsen, Laurence Amar, Christina Pamporaki, Paolo Mulatero, Anne Blanchard, Marek Kabat, Stacy Robertson, Scott M. MacKenzie, Angela E. Taylor, Mirko Peitzsch, Filippo Ceccato, Carla Scaroni, Martin Reincke, Matthias Kroiss, Michael C. Dennedy, Alessio Pecori, Silvia Monticone, Jaap Deinum, Gian Paolo Rossi, Livia Lenzini, John D. McClure, Thomas Nind, Alexandra Riddell, Anthony Stell, Christian Cole, Isabella Sudano, Cornelia Prehn, Jerzy Adamski, Anne-Paule Gimenez-Roqueplo, Guillaume Assie, Wiebke Arlt, Felix Beuschlein, Graeme Eisenhofer, Eleanor Davies, Maria-Christina Zennaro, Emily Jefferson
Summary: Machine Learning is used to classify subtypes of endocrine hypertension in a large cohort of hypertensive patients. Multi-omics classifiers provide better classification performance compared to mono-omics classifiers in distinguishing different subtypes of hypertension.
Article
Genetics & Heredity
Olivier B. Poirion, Zheng Jing, Kumardeep Chaudhary, Sijia Huang, Lana X. Garmire
Summary: DeepProg is a framework that integrates deep-learning and machine-learning approaches for predicting patient survival subtypes using multi-omics data. It identifies two optimal survival subtypes in most cancers and offers better risk-stratification than other methods.
Article
Computer Science, Information Systems
Waqas Haider Bangyal, Najeeb Ur Rehman, Asma Nawaz, Kashif Nisar, Ag. Asri Ag. Ibrahim, Rabia Shakir, Danda B. Rawat
Summary: This paper presents a method for diagnosing Alzheimer's disease using deep convolutional neural networks and compares it with other machine learning methods. The experimental results show that the deep learning approach achieves high accuracy.
Article
Neurosciences
Siti Hajar Rehiman, Siong Meng Lim, Fei Tieng Lim, Ai-Vyrn Chin, Maw Pin Tan, Shahrul Bahyah Kamaruzzaman, Kalavathy Ramasamy, Abu Bakar Abdul Majeed
Summary: This study identified differential protein expression of fibrinogen isoforms in patients with Alzheimer's disease (AD) and found a weak but significant correlation between fibrinogen and cognitive decline. This suggests that fibrinogen may serve as a promising blood-based biomarker for AD.
INTERNATIONAL JOURNAL OF NEUROSCIENCE
(2022)
Article
Medicine, General & Internal
Yi Ru Tan, Maw Pin Tan, Mei Mei Khor, Hon Bing Hoh, Nor'Izzati Saedon, Kejal Hasmukharay, Kit Mun Tan, Ai Vyrn Chin, Shahrul B. Kamaruzzaman, Terence Ong, Gareth Davey, Hui Min Khor
Summary: This study surveyed the acceptance of virtual medical consultations among older adults and caregivers in geriatric outpatient services during the COVID-19 pandemic. The findings indicate a high level of acceptance among caregivers and patients, suggesting that virtual consultations are a viable alternative to face-to-face care for older people and their caregivers.
POSTGRADUATE MEDICINE
(2022)
Article
Oncology
Mahno Noor Ezmas, Abdullah Norlia, Aziz Suraya, Wan Md Hafiz Wan Md Adnan, Lai Meng Looi
Summary: A 34-year-old woman with a history of left breast carcinoma underwent breast conserving surgery and subsequent reconstructive surgery. However, the silicone implant ruptured 5 years after the surgery, and she later developed painless hematuria, which was diagnosed as IgA nephropathy by renal biopsy.
FRONTIERS IN ONCOLOGY
(2022)
Article
Information Science & Library Science
Tracey Elliott, Bisma Fazeen, Asfawossen Asrat, Ana Maria Cetto, Stefan Eriksson, Lai Meng Looi, Diane Negra
Summary: A global survey revealed that over 80% of researchers perceive predatory academic journals and conferences as a serious problem, with the potential to undermine the research enterprise. The survey also found that at least 24% of respondents admitted to having published in predatory journals or participated in predatory conferences. A lack of awareness about predatory practices was identified as the main reason for such engagement. The impact on individuals varied, with some reporting no impact while others experienced negative and detrimental effects.
LEARNED PUBLISHING
(2022)
Article
Urology & Nephrology
Albert Hing Wong, Wei-Kei Wong, Lai-Meng Looi, Jeyakantha Ratnasingam, Soo-Kun Lim
Summary: PTU-induced ANCA-associated vasculitis is a rare disease without optimal treatment. Prompt withdrawal of offending medication, therapeutic plasma exchange, and immunosuppressants are crucial for managing severe cases with life-threatening organ involvement.
CASE REPORTS IN NEPHROLOGY AND DIALYSIS
(2022)
Letter
Medicine, General & Internal
Nurul Nabilah Akmal Hashim, Sumaiyah Mat, Phyo Kyaw Myint, Sheng Hui Kioh, Mirela Delibegovic, Ai-Vyrn Chin, Shahrul Bahyah Kamaruzzaman, Noran Naqiah Hairi, Selina Khoo, Maw Pin Tan
EUROPEAN JOURNAL OF CLINICAL INVESTIGATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Aizatul Shafiqah Mohd Faizal, Wei Yin Hon, T. Malathi Thevarajah, Sook Mei Khor, Siow-Wee Chang
Summary: A machine learning approach was used to identify potential biomarkers for early detection and treatment of acute myocardial infarction (AMI). It was found that using fewer features generally performed better than using all features, and a five-feature model including cardiac troponin I, HDL cholesterol, HbA1c, anion gap, and albumin was identified as potential biomarkers to predict the prognosis of AMI patients.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2023)
Article
Chemistry, Multidisciplinary
Mei Sze Tan, Phaik-Leng Cheah, Ai-Vyrn Chin, Lai-Meng Looi, Siow-Wee Chang
Summary: Alzheimer's disease (AD) is a neurodegenerative disease that impairs cognition and function. This study focused on miRNAs as potential biomarkers for AD in blood. Using statistical and machine learning approaches, three miRNA candidates (hsa-miR-6501-5p, hsa-miR-4433b-5p, and hsa-miR-143-3p) were identified as significant and correlated with each other. The study verified their roles in AD development by predicting their target mRNAs and investigating their interaction networks. Pathway analysis revealed the involvement of oxidative phosphorylation, mitochondrial dysfunction, and calcium-mediated signaling in AD development. These findings highlight the importance of studying miRNA expression changes in AD.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Wan Siti Halimatul Munirah Wan Ahmad, Mohammad Faiza Ahmad Fauzi, Md Jahid Hasan, Zaka Ur Rehman, Jenny Tung Hiong Lee, See Yee Khor, Lai-Meng Looi, Fazly Salleh Abas, Afzan Adam, Elaine Wan Ling Chan, Sei-Ichiro Kamata
Summary: In this article, a thorough analysis of 37 breast cancer cases was conducted using DenseNet deep learning architecture. ER-stained nuclei were classified and scored, with the best concordance reached between the automated scoring and the pathologist's manual score. This study holds significance in the accurate scoring of ER-IHC stained whole slide images and paves the way for further development of deep learning models for nuclei classification.
Article
Pathology
Joon Hi Tham, Lai Meng Looi, Razmin Ghazali
Summary: This study compares the cost and cancer detection rate of two different biopsy protocols for prostate cancer. The results show a 3.38-fold increase in costs and a reduction in cancer detection rate when comparing transrectal and transperineal biopsy. The reason for the reduced detection rate remains unclear.
MALAYSIAN JOURNAL OF PATHOLOGY
(2022)
Article
Engineering, Biomedical
Afiqah Abu Samah, Mohammad Faizal Ahmad Fauzi, See Yee Khor, Jenny Tung Hiong Lee, Kean Hooi Teoh, Lai Meng Looi, Sarina Mansor
Summary: In this paper, a system for detecting mitotic cells in breast carcinoma using image processing and morphological analysis is proposed, achieving accurate detection and classification of cells.
INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY
(2022)
Article
Pathology
Yin Ping Wong, Farveen Marican A. B. U. Backer, Geok Chin Tan, Lai Meng Looi, Mohd Jamsani M. A. T. Salleh, Palani Ammal A. Subramaniam, Razuin Rahimi, Roziana Ariffin, Ruzi Hamimi Razali, Sheue Feng Siew, Soon Keng Cheong, Subashini C. Thambiah, Suhaila M. D. Hanapiah, Thatcheiany Kumariah, Yee Loong Tang, Zetti Z. A. I. N. O. L. Rashid
MALAYSIAN JOURNAL OF PATHOLOGY
(2022)
Article
Geriatrics & Gerontology
S. Risbridger, R. Walker, W. K. Gray, S. B. Kamaruzzaman, C. Ai-Vyrn, N. N. Hairi, P. L. Khoo, T. M. Pin
Summary: The study found that social isolation and non-engagement in community activities were associated with increased frailty, while social isolation also increased the risk of falls. This highlights the importance of social participation in reducing frailty and falls among elderly individuals.
JOURNAL OF FRAILTY & AGING
(2022)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)