scholarly journals High risk glioblastoma cells revealed by machine learning and single cell signaling profiles

2019 ◽  
Author(s):  
Nalin Leelatian ◽  
Justine Sinnaeve ◽  
Akshitkumar M. Mistry ◽  
Sierra M. Barone ◽  
Kirsten E. Diggins ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Clinical Outcomes ◽  
Learning Algorithm ◽  
Cell Mass ◽  
Tumor Biology ◽  
Continuous Variable ◽  
Disease Biology ◽  
Recent Developments ◽  
Cell Phenotypes

AbstractRecent developments in machine learning implemented dimensionality reduction and clustering tools to classify the cellular composition of patient-derived tissue in multi-dimensional, single cell studies. Current approaches, however, require prior knowledge of either categorical clinical outcomes or cell type identities. These algorithms are not well suited for application in tumor biology, where clinical outcomes can be continuous and censored and cell identities may be novel and plastic. Risk Assessment Population IDentification (RAPID) is an unsupervised, machine learning algorithm that identifies single cell phenotypes and assesses clinical risk stratification as a continuous variable. Single cell mass cytometry evaluated 34 different phospho-proteins, transcription factors, and cell identity proteins in tumor tissue resected from patients bearingIDHwild-type glioblastomas. RAPID identified and characterized multiple biologically distinct tumor cell subsets that independently and continuously stratified patient outcome. RAPID is broadly applicable for single cell studies where atypical cancer and immune cells may drive disease biology and treatment responses.

scholarly journals Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning

2019 ◽  
Vol 55 (5) ◽  
pp. 616-619 ◽  
Author(s):  
Renmeng Liu ◽  
Genwei Zhang ◽  
Zhibo Yang
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Single Cell ◽  
Cell Mass ◽  
Resistant Cell ◽  
Drug Resistant ◽  
Rapid Prediction ◽  
Cell Mass Spectrometry ◽  
Cell Phenotypes ◽  
Drug Resistant Cell

The combination of single cell mass spectrometry with machine learning enables prediction of drug-resistant cell phenotypes based on metabolomic profiles.

Insights into therapeutic targets and biomarkers using integrated multi-‘omics’ approaches for dilated and ischemic cardiomyopathies

2021 ◽  
Author(s):  
Austė Kanapeckaitė ◽  
Neringa Burokienė
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Learning Algorithm ◽  
Expression Profiles ◽  
Therapeutic Targets ◽  
Development Stage ◽  
Biological Data ◽  
Specific Gene ◽  
Tissue Remodelling ◽  
Pharmacological Management

Abstract At present, heart failure (HF) treatment only targets the symptoms based on the left ventricle dysfunction severity; however, the lack of systemic ‘omics’ studies and available biological data to uncover the heterogeneous underlying mechanisms signifies the need to shift the analytical paradigm towards network-centric and data mining approaches. This study, for the first time, aimed to investigate how bulk and single cell RNA-sequencing as well as the proteomics analysis of the human heart tissue can be integrated to uncover HF-specific networks and potential therapeutic targets or biomarkers. We also aimed to address the issue of dealing with a limited number of samples and to show how appropriate statistical models, enrichment with other datasets as well as machine learning-guided analysis can aid in such cases. Furthermore, we elucidated specific gene expression profiles using transcriptomic and mined data from public databases. This was achieved using the two-step machine learning algorithm to predict the likelihood of the therapeutic target or biomarker tractability based on a novel scoring system, which has also been introduced in this study. The described methodology could be very useful for the target or biomarker selection and evaluation during the pre-clinical therapeutics development stage as well as disease progression monitoring. In addition, the present study sheds new light into the complex aetiology of HF, differentiating between subtle changes in dilated cardiomyopathies (DCs) and ischemic cardiomyopathies (ICs) on the single cell, proteome and whole transcriptome level, demonstrating that HF might be dependent on the involvement of not only the cardiomyocytes but also on other cell populations. Identified tissue remodelling and inflammatory processes can be beneficial when selecting targeted pharmacological management for DCs or ICs, respectively.

scholarly journals Surgical skill assessment using machine learning algorithms

2021 ◽  
Vol 108 (Supplement_4) ◽  
Author(s):  
J L Lavanchy ◽  
J Zindel ◽  
K Kirtac ◽  
I Twick ◽  
E Hosgor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Outcomes ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Movement Patterns ◽  
Surgical Skills ◽  
Machine Learning Algorithms ◽  
Skill Assessment ◽  
Surgical Skill ◽  
Surgical Skill Assessment

Abstract Objective Surgical skill is correlated with clinical outcomes. Therefore, the assessment of surgical skill is of major importance to improve clinical outcomes and increase patient safety. However, surgical skill assessment often lacks objectivity and reproducibility. Furthermore, it is time-consuming and expensive. Therefore, we developed an automated surgical skill assessment using machine learning algorithms. Methods Surgical skills were assessed in videos of laparoscopic cholecystectomy using a three-step machine learning algorithm. First, a three-dimensional convolutional neural network was trained to localize and classify the instruments within the videos. Second, movement patterns of the instruments were recorded over time and extracted. Third, the movement patterns were correlated with human surgical skill ratings using a linear regression model to predict surgical skill ratings automatically. Machine ratings were compared against human ratings of four board certified surgeons using a score ranging from 1 (poor skills) to 5 (excellent skills). Results Human raters and machine learning algorithms assessed surgical skills in 242 videos. Inter-rater reliability for human raters was excellent (79%, 95%CI 72-85%). Instrument detection showed an average precision of 78% and average recall of 82%. Machine learning algorithms showed an 87% accuracy in predicting good or poor surgical skills, when compared to human raters. Conclusion Machine learning algorithms can be trained to distinguish good and poor surgical skills with high accuracy. This work was published in Sci Rep 11, 5197 (2021). https://doi.org/10.1038/s41598-021-84295-6

scholarly journals Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals

2020 ◽  
Vol 27 (1) ◽  
pp. e100109 ◽  
Author(s):  
Hoyt Burdick ◽  
Eduardo Pino ◽  
Denise Gabel-Comeau ◽  
Andrea McCoy ◽  
Carol Gu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Severe Sepsis ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Clinical Outcomes ◽  
Real World ◽  
Learning Algorithm ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Machine Learning Algorithm

BackgroundSevere sepsis and septic shock are among the leading causes of death in the USA. While early prediction of severe sepsis can reduce adverse patient outcomes, sepsis remains one of the most expensive conditions to diagnose and treat.ObjectiveThe purpose of this study was to evaluate the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay and 30-day readmission.DesignProspective clinical outcomes evaluation.SettingEvaluation was performed on a multiyear, multicentre clinical data set of real-world data containing 75 147 patient encounters from nine hospitals across the continental USA, ranging from community hospitals to large academic medical centres.ParticipantsAnalyses were performed for 17 758 adult patients who met two or more systemic inflammatory response syndrome criteria at any point during their stay (‘sepsis-related’ patients).InterventionsMachine learning algorithm for severe sepsis prediction.Outcome measuresIn-hospital mortality, length of stay and 30-day readmission rates.ResultsHospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay and a 22.7% reduction in 30-day readmission rate for sepsis-related patient stays when using the machine learning algorithm in clinical outcomes analysis.ConclusionsReductions of in-hospital mortality, hospital length of stay and 30-day readmissions were observed in real-world clinical use of the machine learning-based algorithm. The predictive algorithm may be successfully used to improve sepsis-related outcomes in live clinical settings.Trial registration numberNCT03960203

scholarly journals A Systematic Evaluation of Methods for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Xiaowen Cao ◽  
Li Xing ◽  
Elham Majd ◽  
Hua He ◽  
Junhua Gu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Rna Sequencing ◽  
Simulated Data ◽  
Supervised Machine Learning ◽  
Cell Phenotype ◽  
Data Sets ◽  
Phenotype Classification ◽  
Single Cell Rna Sequencing ◽  
Cell Phenotypes

Abstract Background: Single-cell RNA sequencing (scRNA-seq) yields valuable insights about gene expression and gives critical information about complex tissue cellular composition. In the analysis of single-cell RNA sequencing, the annotations of cell subtypes are often done manually, which is time-consuming and irreproducible. Garnett is a cell-type annotation software based the on elastic net method. Beside cell-type annotation, supervised machine learning methods can also be applied to predict other cell phenotypes from genomic data. Despite the popularity of such applications, there is no existing study to systematically investigate the performance of those supervised algorithms in various sizes of scRNA-seq data sets. Methods and Results: This study evaluates 13 popular supervised machine learning algorithms to classify cell phenotypes, using published real and simulated data sets with diverse cell sizes. The benchmark contained two parts. In the first part, we used real data sets to assess the popular supervised algorithms’ computing speed and cell phenotype classification performance. The classification performances were evaluated using AUC statistics, F1-score, precision, recall, and false-positive rate. In the second part, we evaluated gene selection performance using published simulated data sets with a known list of real genes. Conclusion: The study outcomes showed that ElasticNet with interactions performed best in small and medium data sets. NB was another appropriate method for medium data sets. In large data sets, XGB works excellent. Ensemble algorithms were not significantly superior to individual machine learning methods. Adding interactions to ElasticNet can help, and the improvement was significant in small data sets.

scholarly journals On Similarities between Inference in Game Theory and Machine Learning

2008 ◽  
Vol 33 ◽  
pp. 259-283 ◽  
Author(s):  
I. Rezek ◽  
D. S. Leslie ◽  
S. Reece ◽  
S. J. Roberts ◽  
A. Rogers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Game Theory ◽  
Graphical Models ◽  
Learning Algorithm ◽  
Mean Field ◽  
Mixture Distribution ◽  
Fictitious Play ◽  
Strongly Connected ◽  
Recent Developments ◽  
Variational Learning

In this paper, we elucidate the equivalence between inference in game theory and machine learning. Our aim in so doing is to establish an equivalent vocabulary between the two domains so as to facilitate developments at the intersection of both fields, and as proof of the usefulness of this approach, we use recent developments in each field to make useful improvements to the other. More specifically, we consider the analogies between smooth best responses in fictitious play and Bayesian inference methods. Initially, we use these insights to develop and demonstrate an improved algorithm for learning in games based on probabilistic moderation. That is, by integrating over the distribution of opponent strategies (a Bayesian approach within machine learning) rather than taking a simple empirical average (the approach used in standard fictitious play) we derive a novel moderated fictitious play algorithm and show that it is more likely than standard fictitious play to converge to a payoff-dominant but risk-dominated Nash equilibrium in a simple coordination game. Furthermore we consider the converse case, and show how insights from game theory can be used to derive two improved mean field variational learning algorithms. We first show that the standard update rule of mean field variational learning is analogous to a Cournot adjustment within game theory. By analogy with fictitious play, we then suggest an improved update rule, and show that this results in fictitious variational play, an improved mean field variational learning algorithm that exhibits better convergence in highly or strongly connected graphical models. Second, we use a recent advance in fictitious play, namely dynamic fictitious play, to derive a derivative action variational learning algorithm, that exhibits superior convergence properties on a canonical machine learning problem (clustering a mixture distribution).

scholarly journals Improving personalized tumor growth predictions using a Bayesian combination of mechanistic modeling and machine learning

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Pietro Mascheroni ◽  
Symeon Savvopoulos ◽  
Juan Carlos López Alfonso ◽  
Michael Meyer-Hermann ◽  
Haralampos Hatzikirou
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Tumor Growth ◽  
Clinical Outcomes ◽  
Clinical Presentation ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Mechanistic Modeling ◽  
Lymphocytic Leukemia ◽  
Simulated Patients

Abstract Background In clinical practice, a plethora of medical examinations are conducted to assess the state of a patient’s pathology producing a variety of clinical data. However, investigation of these data faces two major challenges. Firstly, we lack the knowledge of the mechanisms involved in regulating these data variables, and secondly, data collection is sparse in time since it relies on patient’s clinical presentation. The former limits the predictive accuracy of clinical outcomes for any mechanistic model. The latter restrains any machine learning algorithm to accurately infer the corresponding disease dynamics. Methods Here, we propose a novel method, based on the Bayesian coupling of mathematical modeling and machine learning, aiming at improving individualized predictions by addressing the aforementioned challenges. Results We evaluate the proposed method on a synthetic dataset for brain tumor growth and analyze its performance in predicting two relevant clinical outputs. The method results in improved predictions in almost all simulated patients, especially for those with a late clinical presentation (>95% patients show improvements compared to standard mathematical modeling). In addition, we test the methodology in two additional settings dealing with real patient cohorts. In both cases, namely cancer growth in chronic lymphocytic leukemia and ovarian cancer, predictions show excellent agreement with reported clinical outcomes (around 60% reduction of mean squared error). Conclusions We show that the combination of machine learning and mathematical modeling approaches can lead to accurate predictions of clinical outputs in the context of data sparsity and limited knowledge of disease mechanisms.

scholarly journals A Phenotyping of Diastolic Function by Machine Learning Improves Prediction of Clinical Outcomes in Heart Failure

2021 ◽  
Vol 8 ◽  
Author(s):  
Haruka Kameshima ◽  
Tokuhisa Uejima ◽  
Alan G. Fraser ◽  
Lisa Takahashi ◽  
Junyi Cho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Diastolic Function ◽  
Clinical Outcomes ◽  
Primary Endpoint ◽  
Learning Algorithm ◽  
Global Longitudinal Strain ◽  
Left Ventricular ◽  
Hazard Ratio ◽  
Homogeneous Groups

Background: Discriminating between different patterns of diastolic dysfunction in heart failure (HF) is still challenging. We tested the hypothesis that an unsupervised machine learning algorithm would detect heterogeneity in diastolic function and improve risk stratification compared with recommended consensus criteria.Methods: This study included 279 consecutive patients aged 24–97 years old with clinically stable HF referred for echocardiographic assessment, in whom diastolic variables were measured according to the current guidelines. Cluster analysis was undertaken to identify homogeneous groups of patients with similar profiles of the variables. Sequential Cox models were used to compare cluster-based classification with guidelines-based classification for predicting clinical outcomes. The primary endpoint was hospitalization for worsening HF.Results: The analysis identified three clusters with distinct properties of diastolic function that shared similarities with guidelines-based classification. The clusters were associated with brain natriuretic peptide level (p < 0.001), hemoglobin concentration (p = 0.017) and estimated glomerular filtration rate (p = 0.001). During a mean follow-up period of 2.6 ± 2.0 years, 62 patients (22%) experienced the primary endpoint. Cluster-based classification predicted events with a hazard ratio 1.68 (p = 0.019) that was independent from and incremental to the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) risk score for HF, and from left ventricular end-diastolic volume and global longitudinal strain, whereas guidelines-based classification did not retain its independent prognostic value (hazard ratio = 1.25, p = 0.202).Conclusion: Machine learning can identify patterns of diastolic function that better stratify the risk for decompensation than the current consensus recommendations in HF. Integrating this data-driven phenotyping may help in refining prognostication and optimizing treatment.

scholarly journals Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252585
Author(s):  
Emily J. MacKay ◽  
Michael D. Stubna ◽  
Corey Chivers ◽  
Michael E. Draugelis ◽  
William J. Hanson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Adverse Events ◽  
Clinical Outcomes ◽  
Risk Model ◽  
Learning Algorithm ◽  
Brier Score ◽  
Surgical Patient ◽  
Machine Learning Algorithm ◽  
Boosted Tree ◽  
Score Range

Objective This study aimed to develop and validate a claims-based, machine learning algorithm to predict clinical outcomes across both medical and surgical patient populations. Methods This retrospective, observational cohort study, used a random 5% sample of 770,777 fee-for-service Medicare beneficiaries with an inpatient hospitalization between 2009–2011. The machine learning algorithms tested included: support vector machine, random forest, multilayer perceptron, extreme gradient boosted tree, and logistic regression. The extreme gradient boosted tree algorithm outperformed the alternatives and was the machine learning method used for the final risk model. Primary outcome was 30-day mortality. Secondary outcomes were: rehospitalization, and any of 23 adverse clinical events occurring within 30 days of the index admission date. Results The machine learning algorithm performance was evaluated by both the area under the receiver operating curve (AUROC) and Brier Score. The risk model demonstrated high performance for prediction of: 30-day mortality (AUROC = 0.88; Brier Score = 0.06), and 17 of the 23 adverse events (AUROC range: 0.80–0.86; Brier Score range: 0.01–0.05). The risk model demonstrated moderate performance for prediction of: rehospitalization within 30 days (AUROC = 0.73; Brier Score: = 0.07) and six of the 23 adverse events (AUROC range: 0.74–0.79; Brier Score range: 0.01–0.02). The machine learning risk model performed comparably on a second, independent validation dataset, confirming that the risk model was not overfit. Conclusions and relevance We have developed and validated a robust, claims-based, machine learning risk model that is applicable to both medical and surgical patient populations and demonstrates comparable predictive accuracy to existing risk models.

scholarly journals Phenotypic Characterization by Single-Cell Mass Cytometry of Human Intrahepatic and Peripheral NK Cells in Patients with Hepatocellular Carcinoma

Cells ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1495
Author(s):  
Yuichi Yoshida ◽  
Sachiyo Yoshio ◽  
Taiji Yamazoe ◽  
Taizo Mori ◽  
Yuriko Tsustui ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Nk Cells ◽  
Single Cell ◽  
Nk Cell ◽  
Checkpoint Inhibitors ◽  
Cell Mass ◽  
Phenotypic Characterization ◽  
Advanced Hepatocellular Carcinoma ◽  
Mass Cytometry ◽  
Cell Phenotypes

Overall response rates of systemic therapies against advanced hepatocellular carcinoma (HCC) remain unsatisfactory. Thus, searching for new immunotherapy targets is indispensable. NK cells are crucial effectors and regulators in the tumor microenvironment and a determinant of responsiveness to checkpoint inhibitors. We revealed the landscape of NK cell phenotypes in HCC patients to find potential immunotherapy targets. Using single cell mass cytometry, we analyzed 32 surface markers on CD56dim and CD56bright NK cells, which included Sialic acid-binding immunoglobulin-type lectins (Siglecs). We compared peripheral NK cells between HCC patients and healthy volunteers. We also compared NK cells, in terms of their localizations, on an individual patient bases between peripheral and intrahepatic NK cells from cancerous and noncancerous liver tissues. In the HCC patient periphery, CD160+CD56dim NK cells that expressed Siglec-7, NKp46, and NKp30 were reduced, while CD49a+CD56dim NK cells that expressed Siglec-10 were increased. CD160 and CD49a on CD56dim NK cells were significantly correlated to other NK-related markers in HCC patients, which suggested that CD160 and CD49a were signature molecules. CD49a+ CX3CR1+ Siglec-10+ NK cells had accumulated in HCC tissues. Considering further functional analyses, CD160, CD49a, CX3CR1, and Siglec-10 on CD56dim NK cells may be targets for immunotherapies of HCC patients.

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