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    School of Computer Science Reports Findings in Machine Learning (Enabling personalised disease diagnosis by combining a patient’s time-specific gene expression profile with a biomedical knowledge base)

    29-30页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting from Galway, Ireland, by NewsRx journalists, research stated, “Recent developments in the domain of biomedical knowledge bases (KBs) open up new ways to exploit biomedical knowledge that is available in the form of KBs. Significant work has been done in the direction of biomedical KB creation and KB completion, specifically, those having gene-disease associations and other related entities.” Financial support for this research came from Science Foundation Ireland. The news correspondents obtained a quote from the research from the School of Computer Science, “However, the use of such biomedical KBs in combination with patients’ temporal clinical data still largely remains unexplored, but has the potential to immensely benefit medical diagnostic decision support systems. We propose two new algorithms, LOADDx and SCADDx, to combine a patient’s gene expression data with gene-disease association and other related information available in the form of a KB, to assist personalized disease diagnosis. We have tested both of the algorithms on two KBs and on four real-world gene expression datasets of respiratory viral infection caused by Influenza-like viruses of 19 subtypes. We also compare the performance of proposed algorithms with that of five existing state-of-the-art machine learning algorithms (k-NN, Random Forest, XGBoost, Linear SVM, and SVM with RBF Kernel) using two validation approaches: LOOCV and a single internal validation set. Both SCADDx and LOADDx outperform the existing algorithms when evaluated with both validation approaches. SCADDx is able to detect infections with up to 100% accuracy in the cases of Datasets 2 and 3. Overall, SCADDx and LOADDx are able to detect an infection within 72 h of infection with 91.38% and 92.66% average accuracy respectively considering all four datasets, whereas XGBoost, which performed best among the existing machine learning algorithms, can detect the infection with only 86.43% accuracy on an average. We demonstrate how our novel idea of using the most and least differentially expressed genes in combination with a KB can enable identification of the diseases that a patient is most likely to have at a particular time, from a KB with thousands of diseases.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Data on Retinitis Pigmentosa Reported by Carlos Loucera and Colleagues (The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery)

    30-31页
    查看更多>>摘要：New research on Eye Diseases and Conditions - Retinitis Pigmentosa is the subject of a report. According to news reporting out of Seville, Spain, by NewsRx editors, research stated, “Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms.” Financial supporters for this research include Consejeria de Salud y Consumo, Junta de Andalucia, H2020 Marie Sklodowska-Curie Actions, Ministerio de Ciencia e Innovacion, Instituto de Salud Carlos III. Our news journalists obtained a quote from the research, “The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP. By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa. A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARa1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa. The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Dalian University of Technology Reports Findings in Drug-Induced Liver Injury (Deep Learning Algorithm Based on Molecular Fingerprint for Prediction of Drug-Induced Liver Injury)

    31-32页
    查看更多>>摘要：New research on Drugs and Therapies - Drug-Induced Liver Injury is the subject of a report. According to news reporting from Liaoning, People’s Republic of China, by NewsRx journalists, research stated, “Drug-induced liver injury (DILI) is one the rare adverse drug reaction (ADR) and multifactorial endpoints. Current preclinical animal models struggle to anticipate it, and in silico methods have emerged as a way with significant potential for doing so.” The news correspondents obtained a quote from the research from the Dalian University of Technology, “In this study, a high-quality dataset of 1573 compounds was assembled. The 48 classification models, which depended on six different molecular fingerprints, were built via deep neural network (DNN) and seven machine learning algorithms. Comparing the results of the DNN and machine learning models, the optional performing model was found as the one developed based on the DNN with ECFP_6 as input, which achieved the area under the receiver operating characteristic curve (AUC) of 0.713, balanced accuracy (BA) of 0.680, and F1 of 0.753. In addition, we used the SHapley Additive exPlanations (SHAP) algorithm to interpret the models, identified the crucial structural fragments related to DILI risk, and selected the top ten substructures with the highest contribution rankings to serve as warning indicators for subsequent drug hepatotoxicity screening studies.”
      原文链接:
    • NETL
    • NSTL

    Freeman Hospital Reports Findings in Artificial Intelligence (Use of Temporally Validated Machine Learning Models To Predict Outcomes of Percutaneous Nephrolithotomy Using Data from the British Association of Urological Surgeons Percutaneous ...)

    32-33页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news originating from Newcastle upon Tyne, United Kingdom, by NewsRx correspondents, research stated, “Machine learning (ML) is a subset of artificial intelligence that uses data to build algorithms to predict specific outcomes. Few ML studies have examined percutaneous nephrolithotomy (PCNL) outcomes.” Our news journalists obtained a quote from the research from Freeman Hospital, “Our objective was to build, streamline, temporally validate, and use ML models for prediction of PCNL outcomes (intensive care admission, postoperative infection, transfusion, adjuvant treatment, postoperative complications, visceral injury, and stone-free status at follow-up) using a comprehensive national database (British Association of Urological Surgeons PCNL). This was an ML study using data from a prospective national database. Extreme gradient boosting (XGB), deep neural network (DNN), and logistic regression (LR) models were built for each outcome of interest using complete cases only, imputed, and oversampled and imputed/oversampled data sets. All validation was performed with complete cases only. Temporal validation was performed with 2019 data only. A second round used a composite of the most important 11 variables in each model to build the final model for inclusion in the shiny application. We report statistics for prognostic accuracy. The database contains 12 810 patients. The final variables included were age, Charlson comorbidity index, preoperative haemoglobin, Guy’s stone score, stone location, size of outer sheath, preoperative midstream urine result, primary puncture site, preoperative dimercapto-succinic acid scan, stone size, and image guidance The areas under the receiver operating characteristic curve was ＞0.6 in all cases. This is the largest ML study on PCNL outcomes to date. The models are temporally valid and therefore can be implemented in clinical practice for patient-specific risk profiling. Further work will be conducted to externally validate the models. We applied artificial intelligence to data for patients who underwent a keyhole surgery to remove kidney stones and developed a model to predict outcomes for this procedure.”
      原文链接:
    • NETL
    • NSTL

    Guangdong Provincial Academy of Chinese Medical Sciences Reports Findings in Osteoarthritis (Transcriptomic analyses and machine-learning methods reveal dysregulated key genes and potential pathogenesis in human osteoarthritic cartilage)

    33-34页
    查看更多>>摘要：New research on Musculoskeletal Diseases and Conditions - Osteoarthritis is the subject of a report. According to news reporting originating from Guangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database.” Our news editors obtained a quote from the research from the Guangdong Provincial Academy of Chinese Medical Sciences, “Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization. A total of 46 genes were obtained from the intersection of significantly upregulated genes in osteoarthritic cartilage and the key module genes screened by WGCNA. Functional annotation analysis revealed that these genes were closely related to pathological responses associated with OA, such as inflammation and immunity. Four key dysregulated genes (cartilage acidic protein 1 (CRTAC1), iodothyronine deiodinase 2 (DIO2), angiopoietin-related protein 2 (ANGPTL2), and MAGE family member D1 (MAGED1)) were identified after using machine-learning algorithms. These genes had high diagnostic value in both the training cohort and external validation cohort (receiver operating characteristic ＞0.8). The upregulated expression of these hub genes in osteoarthritic cartilage signified higher levels of immune infiltration as well as the expression of metalloproteinases and mineralization markers, suggesting harmful biological alterations and indicating that these hub genes play an important role in the pathogenesis of OA. A competing endogenous RNA network was constructed to reveal the underlying post-transcriptional regulatory mechanisms. The current study explores and validates a dysregulated key gene set in osteoarthritic cartilage that is capable of accurately diagnosing OA and characterizing the biological alterations in osteoarthritic cartilage; this may become a promising indicator in clinical decision-making.”
      原文链接:
    • NETL
    • NSTL

    Study Findings from University of Illinois Provide New Insights into Machine Learning (Enabling Pathway Design By Multiplex Experimentation and Machine Learning)

    34-35页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting from Urbana, Illinois, by NewsRx journalists, research stated, “The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell.” Funders for this research include United States Department of Energy (DOE), Molecule Maker Lab Institute: An AI Research Institutes program, National Science Foundation (NSF).
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Reports from University of Petrosani Advance Knowledge in Artificial Intelligence (Modeling reflection in artificial intelligence systems: state of art and prospects)

    35-36页
    查看更多>>摘要：Data detailed on artificial intelligence have been presented. According to news reporting from the University of Petrosani by NewsRx journalists, research stated, “Abstract.” Our news journalists obtained a quote from the research from University of Petrosani: “The paper is devoted to analyzing the current state of research and assessing the prospects for modeling reflexive processes using artificial intelligence systems, in particular, multi-agent systems for relevant simulation of collective problem solving. Reflexive modeling of each other by agents will ensure the development of a coherent model of the control object, the purpose of collective work and norms of interaction, as well as the developing of effective interaction between agents.” According to the news reporters, the research concluded: “This will allow the system, by self-organizing, to adapt to the characteristics of arising problems, in particular, to take into account their complex structure, the network nature of conditions and goals, opacity, subjectivity and dynamism.”
      原文链接:
    • NETL
    • NSTL

    Data on Cancer Reported by Joaquim Pombo and Colleagues (Detection of senescence using machine learning algorithms based on nuclear features)

    36-36页
    查看更多>>摘要：New research on Cancer is the subject of a report. According to news reporting originating in London, United Kingdom, by NewsRx journalists, research stated, “Cellular senescence is a stress response with broad pathophysiological implications. Senotherapies can induce senescence to treat cancer or eliminate senescent cells to ameliorate ageing and age-related pathologies.” The news reporters obtained a quote from the research, “However, the success of senotherapies is limited by the lack of reliable ways to identify senescence. Here, we use nuclear morphology features of senescent cells to devise machine-learning classifiers that accurately predict senescence induced by diverse stressors in different cell types and tissues. As a proof-of-principle, we use these senescence classifiers to characterise senolytics and to screen for drugs that selectively induce senescence in cancer cells but not normal cells. Moreover, a tissue senescence score served to assess the efficacy of senolytic drugs and identified senescence in mouse models of liver cancer initiation, ageing, and fibrosis, and in patients with fatty liver disease.”
      原文链接:
    • NETL
    • NSTL

    Researcher at University of Science and Technology Beijing Publishes New Study Findings on Robotics (Improved artificial potential field method for mobile robot path planning)

    37-37页
    查看更多>>摘要：New study results on robotics have been published. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Path planning has already been used in areas like robots and unmanned vehicles to prevent collisions in certain environments.” The news correspondents obtained a quote from the research from University of Science and Technology Beijing: “A Path planning algorithm is needed to achieve such tasks and Artificial Potential Field (APF) method is one of the methods. However, APF has limitations facing various situations like being stuck in a local minimum such as a dead-end or a narrow path. To solve the problem, First, a side force is added to the algorithm along with two types of definitions of the force direction. Then a variable is proposed to prevent the dead-end situation. Finally, the variable step size is used to improve the efficiency of the algorithm.”
      原文链接:
    • NETL
    • NSTL

    International University of Catalunya Reports Findings in Alzheimer Disease (Unveiling the sound of the cognitive status: Machine Learning-based speech analysis in the Alzheimer’s disease spectrum)

    37-38页
    查看更多>>摘要：New research on Neurodegenerative Diseases and Conditions - Alzheimer Disease is the subject of a report. According to news reporting originating from Barcelona, Spain, by NewsRx correspondents, research stated, “Advancement in screening tools accessible to the general population for the early detection of Alzheimer’s disease (AD) and prediction of its progression is essential for achieving timely therapeutic interventions and conducting decentralized clinical trials. This study delves into the application of Machine Learning (ML) techniques by leveraging paralinguistic features extracted directly from a brief spontaneous speech (SS) protocol.” Financial support for this research came from Next Generation EU.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature