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General General

So you think you can PLS-DA?

In BMC bioinformatics

BACKGROUND : Partial Least-Squares Discriminant Analysis (PLS-DA) is a popular machine learning tool that is gaining increasing attention as a useful feature selector and classifier. In an effort to understand its strengths and weaknesses, we performed a series of experiments with synthetic data and compared its performance to its close relative from which it was initially invented, namely Principal Component Analysis (PCA).

RESULTS : We demonstrate that even though PCA ignores the information regarding the class labels of the samples, this unsupervised tool can be remarkably effective as a feature selector. In some cases, it outperforms PLS-DA, which is made aware of the class labels in its input. Our experiments range from looking at the signal-to-noise ratio in the feature selection task, to considering many practical distributions and models encountered when analyzing bioinformatics and clinical data. Other methods were also evaluated. Finally, we analyzed an interesting data set from 396 vaginal microbiome samples where the ground truth for the feature selection was available. All the 3D figures shown in this paper as well as the supplementary ones can be viewed interactively at CONCLUSIONS: Our results highlighted the strengths and weaknesses of PLS-DA in comparison with PCA for different underlying data models.

Ruiz-Perez Daniel, Guan Haibin, Madhivanan Purnima, Mathee Kalai, Narasimhan Giri


Bioinformatics, Dimensionality reduction, Feature selection, PCA, PLS-DA

General General

Coronavirus Disease 2019: Virology and Drug Targets.

In Infectious disorders drug targets

The Coronavirus Disease 2019, a pandemic caused by novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is seriously affecting global health and the economy. As the vaccine development takes time, the current research is focused on repurposing FDA approved drugs against the viral target proteins. This review discusses the current understanding of SARS-CoV-2 virology, its target structural proteins (S- glycoprotein), non-structural proteins (3- chymotrypsin-like protease, papain-like protease, RNA-dependent RNA polymerase, and helicase) and accessory proteins, drug discovery strategies (drug repurposing, artificial intelligence, and high-throughput screening), and the current status of antiviral drug development.

Krishnamurthy Praveen Thaggikuppe


CoVID-19, Drug targets, SARS-CoV-2, Virology

Internal Medicine Internal Medicine

Automated Electronic Phenotyping of Cardioembolic Stroke.

In Stroke ; h5-index 83.0

BACKGROUND AND PURPOSE : Oral anticoagulation is generally indicated for cardioembolic strokes, but not for other stroke causes. Consequently, subtype classification of ischemic stroke is important for risk stratification and secondary prevention. Because manual classification of ischemic stroke is time-intensive, we assessed the accuracy of automated algorithms for performing cardioembolic stroke subtyping using an electronic health record (EHR) database.

METHODS : We adapted TOAST (Trial of ORG 10172 in Acute Stroke Treatment) features associated with cardioembolic stroke for derivation in the EHR. Using administrative codes and echocardiographic reports within Mass General Brigham Biobank (N=13 079), we iteratively developed EHR-based algorithms to define the TOAST cardioembolic stroke features, revising regular expression algorithms until achieving positive predictive value ≥80%. We compared several machine learning-based statistical algorithms for discriminating cardioembolic stroke using the feature algorithms applied to EHR data from 1598 patients with acute ischemic strokes from the Massachusetts General Hospital Ischemic Stroke Registry (2002-2010) with previously adjudicated TOAST and Causative Classification of Stroke subtypes.

RESULTS : Regular expression-based feature extraction algorithms achieved a mean positive predictive value of 95% (range, 88%-100%) across 11 echocardiographic features. Among 1598 patients from the Massachusetts General Hospital Ischemic Stroke Registry, 1068 had any cardioembolic stroke feature within predefined time windows in proximity to the stroke event. Cardioembolic stroke tended to occur at an older age, with more TOAST-based comorbidities, and with atrial fibrillation (82.3%). The best model was a random forest with 92.2% accuracy and area under the receiver operating characteristic curve of 91.1% (95% CI, 87.5%-93.9%). Atrial fibrillation, age, dilated cardiomyopathy, congestive heart failure, patent foramen ovale, mitral annulus calcification, and recent myocardial infarction were the most discriminatory features.

CONCLUSIONS : Machine learning-based identification of cardioembolic stroke using EHR data is feasible. Future work is needed to improve the accuracy of automated cardioembolic stroke identification and assess generalizability of electronic phenotyping algorithms across clinical settings.

Guan Wyliena, Ko Darae, Khurshid Shaan, Trisini Lipsanopoulos Ana T, Ashburner Jeffrey M, Harrington Lia X, Rost Natalia S, Atlas Steven J, Singer Daniel E, McManus David D, Anderson Christopher D, Lubitz Steven A


atrial fibrillation, dilated cardiomyopathy, electronic health records, natural language processing, secondary prevention

General General

Fi-score: a novel approach to characterise protein topology and aid in drug discovery studies.

In Journal of biomolecular structure & dynamics

Target evaluation is at the centre of rational drug design and biologics development. In order to successfully engineer antibodies, T-cell receptors or small molecules it is necessary to identify and characterise potential binding or contact sites on therapeutically relevant target proteins. Currently, there are numerous challenges in achieving a better docking precision as well as characterising relevant sites. We devised a first-of-its-kind in silico protein fingerprinting approach based on the dihedral angle and B-factor distribution to probe binding sites and sites of structural importance. Our derived Fi-score can be used to classify protein regions or individual structural subsets of interest and the described scoring system could be integrated into other discovery pipelines, such as protein classification databases, or applied to investigate new targets. We further demonstrated how our method can be integrated into machine learning Gaussian mixture models to predict different structural elements. Fi-score, in combination with other biophysical analytical methods depending on the research goals, could help to classify and systematically analyse not only targets but also drug candidates that bind to specific sites. The described methodology could greatly improve pre-screening stage, target selection and drug repurposing efforts in finding other matching targets. HIGHLIGHTS Description and derivation of a first-of-its-kind in silico protein fingerprinting method using B-factors and dihedral angles. Derived Fi-score allows to characterise the whole protein or selected regions of interest. Demonstration how machine learning using Gaussian mixture models on Fi-scores captures and allows to predict functional protein topology elements. Fi-score is a novel method to help evaluate therapeutic targets and engineer effective biologics. Communicated by Ramaswamy H. Sarma.

KanapeckaitÄ— AustÄ—, Beaurivage Claudia, Hancock Matthew, Verschueren Erik


B-factor, Drug discovery, Gaussian mixture models, conformation distal information, dihedral angles, machine learning, protein site characterisation

General General

Artificial intelligence-based imaging analytics and lung cancer diagnostics: Considerations for health system leaders.

In Healthcare management forum

Lung cancer is a leading cause of cancer death in Canada, and accurate, early diagnosis are critical to improving clinical outcomes. Artificial Intelligence (AI)-based imaging analytics are a promising healthcare innovation that aim to improve the accuracy and efficiency of lung cancer diagnosis. Maximizing their clinical potential while mitigating their risks and limitations will require focused leadership informed by interdisciplinary expertise and system-wide insight. We convened a knowledge exchange workshop with diverse Saskatchewan health system leaders and stakeholders to explore issues surrounding the use of AI in diagnostic imaging for lung cancer, including implementation opportunities, challenges, and priorities. This technology is anticipated to improve patient outcomes, reduce unnecessary healthcare spending, and increase knowledge. However, health system leaders must also address the needs for robust data, financial investment, effective communication and collaboration between healthcare sectors, privacy and data protections, and continued interdisciplinary research to achieve this technology's potential benefits.

Zarzeczny Amy, Babyn Paul, Adams Scott J, Longo Justin


General General

Health-Behaviors Associated With the Growing Risk of Adolescent Suicide Attempts: A Data-Driven Cross-Sectional Study.

In American journal of health promotion : AJHP

PURPOSE : Identify and examine the associations between health behaviors and increased risk of adolescent suicide attempts, while controlling for socio-economic and demographic differences.

DESIGN : A data-driven analysis using cross-sectional data.

SETTING : Communities in the state of Montana from 1999 to 2017. Selected Montana as it persistently ranks among the top 3 vulnerable states in the U.S. over the past years.

SUBJECTS : Selected 22,447 adolescents of whom 1,631 adolescents attempted suicide at least once.

MEASURES : Overall 29 variables (predictors) accounting for psychological behaviors, illegal substances consumption, daily activities at schools and demographic backgrounds were considered.

ANALYSIS : A library of machine learning algorithms along with the traditionally-used logistic regression were used to model and predict suicide attempt risk. Model performances-goodness-of-fit and predictive accuracy-were measured using accuracy, precision, recall and F-score metrics. Additionally, χ2 analysis was used to evaluate the statistical significance of each variable.

RESULTS : The non-parametric Bayesian tree ensemble model outperformed all other models, with 80.0% accuracy in goodness-of-fit (F-score: 0.802) and 78.2% in predictive accuracy (F-score: 0.785). Key health-behaviors identified include: being sad/hopeless (p < 0.0001), followed by safety concerns at school (p < 0.0001), physical fighting (p < 0.0001), inhalant usage (p < 0.0001), illegal drugs consumption at school (p < 0.0001), current cigarette usage (p < 0.0001), and having first sex at an early age (below 15 years of age). Additionally, the minority groups (American Indian/Alaska Natives, Hispanics/Latinos) (p < 0.0001), and females (p < 0.0001) are also found to be highly vulnerable to attempting suicides.

CONCLUSION : Significant contribution of this work is understanding the key health-behaviors and health disparities that lead to higher frequency of suicide attempts among adolescents, while accounting for the non-linearity and complex interactions among the outcome and the exposure variables. Findings provide insights on key health-behaviors that can be viewed as early warning signs/precursors of suicide attempts among adolescents.

Wei Zhiyuan, Mukherjee Sayanti


health behaviors, health policy, mental health, predictive analytics, suicide attempts among adolescents, suicide prevention