6,118 results match your criteria ACM Transactions on Autonomous and Adaptive Systems[Journal]


Exploring the Equity Impact of Current Digital Health Design Practices: Protocol for a Scoping Review.

JMIR Res Protoc 2022 May 17;11(5):e34013. Epub 2022 May 17.

UNICEF, New York, NY, United States.

Background: The field of digital health has grown rapidly in part due to digital health tools' potential to reduce health inequities. However, such potential has not always been realized. The design approaches used in digital health are one of the known aspects that have an impact on health equity. Read More

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Use of Robots in Critical Care: Systematic Review.

J Med Internet Res 2022 May 16;24(5):e33380. Epub 2022 May 16.

Division of Respiratory & Critical Care Medicine, Department of Medicine, National University Hospital, Singapore, Singapore.

Background: The recent focus on the critical setting, especially with the COVID-19 pandemic, has highlighted the need for minimizing contact-based care and increasing robotic use. Robotics is a rising field in the context of health care, and we sought to evaluate the use of robots in critical care settings.

Objective: Although robotic presence is prevalent in the surgical setting, its role in critical care has not been well established. Read More

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Spatial transcriptomics unveils ZBTB11 as a regulator of cardiomyocyte degeneration in arrhythmogenic cardiomyopathy.

Cardiovasc Res 2022 May 16. Epub 2022 May 16.

Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW) and University Medical Center Utrecht, Utrecht, Netherlands.

Aims: Arrhythmogenic cardiomyopathy (ACM) is an inherited cardiac disorder that is characterized by progressive loss of myocardium that is replaced by fibro-fatty cells, arrhythmias, and sudden cardiac death. While myocardial degeneration and fibro-fatty replacement occur in specific locations, the underlying molecular changes remain poorly characterized. Here we aim to delineate local changes in gene expression to identify new genes and pathways that are relevant for specific remodelling processes occurring during ACM. Read More

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Biomedical Argument Mining Based on Sequential Multi-Task Learning.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 16;PP. Epub 2022 May 16.

Biomedical argument mining aims to automatically identify and extract the argumentative structure in biomedical text. It helps to determine not only what positions people adopt, but also why they hold such opinions, which provides valuable insights into medical decision making. Generally, biomedical argument mining consists of three subtasks: argument component identification, argument component classification and relation identification. Read More

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Modular multi--source prediction of drug side--effects with DruGNN.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 16;PP. Epub 2022 May 16.

Drug Side--Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side--effects, before their occurrence, is fundamental to reduce this impact, in particular on drug discovery. Candidate molecules could be screened before undergoing clinical trials, reducing the costs in time, money, and health of the participants. Read More

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Heterogeneous Multi-task Learning with Expert Diversity.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 16;PP. Epub 2022 May 16.

Predicting multiple heterogeneous biological and medical targets is a challenge for traditional deep learning models. In contrast to single-task learning, in which a separate model is trained for each target, multi-task learning (MTL) optimizes a single model to predict multiple related targets simultaneously. To address this challenge, we propose the Multi-gate Mixture-of-Experts with Exclusivity (MMoEEx). Read More

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Virtual Reality (VR) Technology for Treatment of Mental Health Problems during COVID-19: A Systematic Review.

Int J Environ Res Public Health 2022 Apr 28;19(9). Epub 2022 Apr 28.

Department of Psychiatry, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur 56000, Malaysia.

There was a surge in psychological distress and emotional burnout during the COVID-19 pandemic. Virtual reality (VR) is helpful as a psychological intervention whilst maintaining physical or social distancing. The present systematic review assessed the role of VR as a psychological intervention tool for mental health problems during the COVID-19 pandemic. Read More

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Prenatal glucocorticoid exposure selectively impairs neuroligin 1-dependent neurogenesis by suppressing astrocytic FGF2-neuronal FGFR1 axis.

Cell Mol Life Sci 2022 May 13;79(6):294. Epub 2022 May 13.

Department of Veterinary Physiology, College of Veterinary Medicine, Research Institute for Veterinary Science, and BK21 Four Future Veterinary Medicine Leading Education and Research Center, Seoul National University, Seoul, 08826, Korea.

Exposure to maternal stress irreversibly impairs neurogenesis of offspring by inducing life-long effects on interaction between neurons and glia under raging differentiation process, culminating in cognitive and neuropsychiatric abnormalities in adulthood. We identified that prenatal exposure to stress-responsive hormone glucocorticoid impaired neurogenesis and induced abnormal behaviors in ICR mice. Then, we used human induced pluripotent stem cell (iPSC)-derived neural stem cell (NSC) to investigate how neurogenesis deficits occur. Read More

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A Biomathematical Model of Tumor Response to Radioimmunotherapy with PDL1 and CTLA4.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 11;PP. Epub 2022 May 11.

There is evidence of synergy between radiotherapy and immunotherapy. Radiotherapy can increase liberation of tumor antigens, causing activation of antitumor T-cells. This effect can be boosted with immunotherapy. Read More

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MCWS-Transformers: Towards an Efficient Modeling of Protein Sequences via Multi Context-Window Based Scaled Self-Attention.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 10;PP. Epub 2022 May 10.

This paper advances the self-attention mechanism in the standard transformer network specific to the modeling of the protein sequences. We introduce a novel context-window based scaled self-attention mechanism for processing protein sequences that is based on the notion of (i) local context and (ii) large contextual pattern. Both notions are essential to building a good representation for protein sequences. Read More

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ELMo4m6A: a contextual language embedding-based predictor for detecting RNA N6-methyladenosine sites.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 10;PP. Epub 2022 May 10.

N6-methyladenosine (m6A) is a universal post-transcriptional modification of RNAs, and it is widely involved in various biological processes. Identifying m6A modification sites accurately is indispensable to further investigate m6A-mediated biological functions. How to better represent RNA sequences is crucial for building effective computational methods for detecting m6A modification sites. Read More

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Robust Kalman Filter State Estimation for Gene Regulatory Networks.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 10;PP. Epub 2022 May 10.

This paper proposes a revised version of the robust generalized maximum likelihood (GM)-type unscented Kalman filter (GM-UKF) for the state estimation of gene regulatory networks (GRNs) in the presence of different types of deviations from assumptions. As known, the parameters and the power of the assumed noises within the GRN model may change abruptly as a result of jump behavior and bursting process in transcription and translation phases. Moreover, there may be outlying samples among genomic measurement data. Read More

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SCAMPER: accurate type-specific prediction of calcium-binding residues using sequence-derived features.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 10;PP. Epub 2022 May 10.

Understanding molecular mechanisms involved in calcium-protein interactions and modeling corresponding docking rely on the accurate identification of calcium-binding residues (CaBRs). The defects of experimentally annotating protein functions enhances the development of computational approaches that correctly identify calcium-binding interactions. Studies have reported that current methods severely cross-predict residues that interact with other types of molecules (e. Read More

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Semi-supervised Deep Learning for Cell Type Identification from Single-Cell Transcriptomic Data.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 10;PP. Epub 2022 May 10.

Cell type identification from single-cell transcriptomic data is a common goal of single-cell RNA sequencing (scRNAseq) data analysis. Deep neural networks have been employed to identify cell types from scRNAseq data with high performance. However, it requires a large mount of individual cells with accurate and unbiased annotated types to train the identification models. Read More

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Transfer Learning Based Lightweight Ensemble Model for Imbalanced Breast Cancer Classification.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 10;PP. Epub 2022 May 10.

Automated classification of breast cancer can often save lives, as manual detection is usually time-consuming & expensive. Since the last decade, deep learning techniques have been most widely used for the automatic classification of breast cancer using histopathology images. This paper has performed the binary and multi-class classification of breast cancer using a transfer learning-based ensemble model. Read More

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LitMC-BERT: transformer-based multi-label classification of biomedical literature with an application on COVID-19 literature curation.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 10;PP. Epub 2022 May 10.

The rapid growth of biomedical literature poses a significant challenge for curation and interpretation. This has become more evident during the COVID-19 pandemic. LitCovid, a literature database of COVID-19 related papers in PubMed, has accumulated over 180,000 articles with millions of accesses. Read More

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Involving Crowdworkers with Lived Experience in Content-Development for Push-Based Digital Mental Health Tools: Lessons Learned from Crowdsourcing Mental Health Messages.

Proc ACM Hum Comput Interact 2022 Apr 7;6(CSCW1). Epub 2022 Apr 7.

University of California, Irvine, USA.

Digital tools can support individuals managing mental health concerns, but delivering sufficiently engaging content is challenging. This paper seeks to clarify how individuals with mental health concerns can contribute content to improve push-based mental health messaging tools. We recruited crowdworkers with mental health symptoms to evaluate and revise expert-composed content for an automated messaging tool, and to generate new topics and messages. Read More

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Meaningful patient and public involvement in digital health innovation, implementation and evaluation: A systematic review.

Health Expect 2022 May 8. Epub 2022 May 8.

Centre for Health Technology, University of Plymouth, Plymouth, UK.

Introduction: The importance of meaningfully involving patients and the public in digital health innovation is widely acknowledged, but often poorly understood. This review, therefore, sought to explore how patients and the public are involved in digital health innovation and to identify factors that support and inhibit meaningful patient and public involvement (PPI) in digital health innovation, implementation and evaluation.

Methods: Searches were undertaken from 2010 to July 2020 in the electronic databases MEDLINE, EMBASE, PsycINFO, CINAHL, Scopus and ACM Digital Library. Read More

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Trends and Outcomes of Patients with Amyloid Cardiomyopathy Listed for Heart Transplantation.

Can J Cardiol 2022 May 4. Epub 2022 May 4.

Cardiac Amyloidosis Program, Department of Radiology, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA.

Background: Heart transplantation in patients with amyloid cardiomyopathy (ACM) has been historically underutilized due to the risk of amyloid recurrence.

Methods: Using data from the United Network for Organ Sharing database on patients listed for single-organ heart transplant between 2010 and 2019, we evaluated trend in heart transplant, and compared waitlist mortality and graft survival between patients with ACM and dilated cardiomyopathy (DCM). Also, we evaluated for independent predictors of outcomes. Read More

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Information and communication technology-based interventions for chronic diseases consultation: Scoping review.

Int J Med Inform 2022 Apr 29;163:104784. Epub 2022 Apr 29.

Department of Computer Science, Faculty of Science and Technology, UiT The Arctic University of Norway, Tromsø, Norway.

Background: Medical consultations are often critical meetings between patients and health personnel to provide treatment, health-management advice, and exchange of information, especially for people living with chronic diseases. The adoption of patient-operated Information and Communication Technologies (ICTs) allows the patients to actively participate in their consultation and treatment. The consultation can be divided into three different phases: before, during, and after the meeting. Read More

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Atrial cardiomyopathy markers and new-onset atrial fibrillation risk in patients with acute myocardial infarction.

Eur J Intern Med 2022 May 2. Epub 2022 May 2.

Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, No.193, Lianhe Road, Dalian, Liaoning 116000, China. Electronic address:

Background: New-onset atrial fibrillation (NOAF) after acute myocardial infarction (AMI) is common and independently correlated with poor prognosis. The purpose of this study is to explore whether atrial cardiomyopathy (ACM) markers improve NOAF risk assessment and contribute to therapy decision-making to improve prognosis.

Methods: We retrospectively analyzed 4713 patients with AMI without a documented history of atrial fibrillation (AF). Read More

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Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 5;PP. Epub 2022 May 5.

Drug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are co-prescribed. With various data sources that describe the relationships and properties between drugs, the comprehensive approach that integrates multiple data sources would be considerably effective in making high-accuracy prediction. Read More

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Deep nonnegative matrix factorization using a variational autoencoder with application to single-cell RNA sequencing data.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 5;PP. Epub 2022 May 5.

Single-cell RNA sequencing is used to analyze the gene expression data of individual cells, thereby adding to existing knowledge of biological phenomena. Accordingly, this technology is widely used in numerous biomedical studies. Recently, the variational autoencoder has emerged and has been adopted for the analysis of single-cell data owing to its high capacity to manage large-scale data. Read More

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Identifying Taxonomic Units in Metagenomic DNA Streams on Mobile Devices.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 5;PP. Epub 2022 May 5.

With the emergence of portable DNA sequencers, such as Oxford Nanopore Technology MinION, metagenomic DNA sequencing can be performed in real-time and directly in the field. However, because metagenomic DNA analysis tasks, e.g. Read More

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A Novel Innovation and Entrepreneurship (I&E) Training Program for Biomedical Research Trainees.

Acad Med 2022 May 3. Epub 2022 May 3.

J.M. Garbutt is professor of medicine, Division of General Medical Sciences, Washington University School of Medicine, St. Louis, Missouri.

Problem: Contemporary science emphasizes efficient translation of scientific discoveries into tangible, innovative products and services to improve human health. Therefore, researchers need skills in innovation and entrepreneurship (I&E) to select which problems to address and bring to market the most promising solutions. Yet training in this skillset is not currently available to most biomedical research trainees. Read More

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Joint Sparse Collaborative Regression on Imaging Genetics Study of Schizophrenia.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 3;PP. Epub 2022 May 3.

The imaging genetics approach generates large amount of high dimensional and multi-modal data, providing complementary information for comprehensive study of Schizophrenia, a complex mental disease. However, at the same time, the variety of these data in structures, resolutions, and formats makes their integrative study a forbidding task. In this paper, we propose a novel model called Joint Sparse Collaborative Regression (JSCoReg), which can extract class-specific features from different health conditions/disease classes. Read More

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Revealing Free Energy Landscape from MD Data via Conditional Angle Partition Tree.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 3;PP. Epub 2022 May 3.

Deciphering the free energy landscape of biomolecular structure space is crucial for understanding many complex molecular processes, such as protein-protein interaction, RNA folding, and protein folding. A major source of current dynamic structure data is Molecular Dynamics (MD) simulations. Several methods have been proposed to investigate the free energy landscape from MD data, but all of them rely on the assumption that kinetic similarity is associated with global geometric similarity, which may lead to unsatisfactory results. Read More

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FMGNN: A method to predict compound- protein interaction with pharmacophore features and physicochemical properties of amino acids.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 3;PP. Epub 2022 May 3.

Identifying interactions between compounds and proteins is an essential task in drug discovery. To recommend compounds as drug candidates, applying the computational approaches has a lower cost than conducting the wet-lab experiments. Deep learning-based methods have advantages in learning complex feature interactions. Read More

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Predicting miRNA-disease associations via node-level attention graph auto-encoder.

IEEE/ACM Trans Comput Biol Bioinform 2022 May 3;PP. Epub 2022 May 3.

Previous studies have confirmed microRNA (miRNA), small single-stranded non-coding RNA, participates in various biological processes and plays vital roles in many complex human diseases. Therefore, developing an efficient method to infer potential miRNA disease associations could greatly help understand operational mechanisms for diseases at the molecular level. However, during these early stages for miRNA disease prediction, traditional biological experiments are laborious and expensive. Read More

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