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Identifying disease signatures in order to facilitate accurate diagnosis/treatment has been the focus of research efforts in the last decade. However, the term "disease signature" has not been properly defined, resulting in inconsistencies between studies, as well as limited ability to fully utilize the tools/information available in the evolving field of healthcare big data. Research was conducted according to the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines. The search (in PubMed, Cochrane, and Web of Science) was limited to English articles published up to 31/12/2016. The search string was "disease signature" OR "disease signatures" OR "disease fingerprint" OR "disease fingerprints" OR "subtype signature" OR "subtype signatures" OR "subgroup signature" OR "subgroup signatures." The full text of the articles was reviewed to determine the meaning of the phrase "disease signature" as well as the context of its use. Of 285 articles identified in the search, 129 were included in the final analysis. The term disease signature was first found in an article from 2001. In the last 10 years, the use of the term increased by approximately ninefold, which is double the general increase in the number of published articles. Only one article attempted to define the term. The two major medical fields where the term was used were oncology (31%) and neurology (20%); 71% of the identified articles used a single biomarker to define the term, 13% of the articles used a pair of biomarkers, and 16% used signatures with multiple biomarker; in 42% of the identified articles, genomic biomarkers were used for the signature, in 17% measurements of biochemical compounds in body fluids, and in 10%, changes in imaging studies were used for the signature. Our findings identified a lack of consistency in defining the term disease signature. We suggest a novel hierarchical multidimensional concept for this term that would combine both current approaches for identifying diseases (one focusing on undesired effects of the disease and the other on its causes). This model can improve disease signature definition consistency which will enable to generalize and classify diseases, resulting in more precise treatments and better outcomes. Ultimately, this model could lead to developing a statistical confidence in a disease signature that would allow physicians/patients to estimate the precision of the diagnosis, which, in turn, may have important implications on patients' prognosis and treatment.

The population of adults with Alzheimer’s disease (AD) varies in needs and outcomes. The heterogeneity of current AD diagnostic subgroups impedes the use of data analytics in clinical trial design and translation of findings into improved care. The purpose of this project was to define more clinically-homogeneous groups of AD patients and link clinical characteristics with biological markers. We used an innovative big data analysis strategy, the 3C strategy, that incorporates medical knowledge into the data analysis process. A large set of preprocessed AD Neuroimaging Initiative (ADNI) data was analyzed with 3C. The data analysis yielded 6 new disease subtypes, which differ from the assigned diagnosis types and present different patterns of clinical measures and potential biomarkers. Two of the subtypes, “Anosognosia dementia” and “Insightful dementia”, differentiate between severe participants based on clinical characteristics and biomarkers. The “Uncompensated mild cognitive impairment (MCI)” subtype, demonstrates clinical, demographic and imaging differences from the “Affective MCI” subtype. Differences were also observed between the “Worried Well” and “Healthy” clusters. The use of data-driven analysis yielded sub-phenotypic clinical clusters that go beyond current diagnoses and are associated with biomarkers. Such homogenous sub-groups can potentially form the basis for enhancement of brain medicine research.

Background and aims Although smoking cessation medications have shown effectiveness in increasing abstinence in randomized controlled trials (RCTs), it is unclear to what extent benefits persist over time. This paper assesses whether the benefits of smoking cessation medications decline over the first year. Methods We selected studies from three systematic reviews published by the Cochrane Collaboration. RCTs of first‐line smoking cessation medications, with 6‐ and 12‐month follow‐up, were eligible for inclusion. Meta‐analysis was used to synthesize information on sustained abstinence (SA) at 6 versus 12 months and 3 versus 6 months, using the risk difference (RD) (‘net benefit’) between intervention and control group quit rates, the relative risk (RR) and the odds ratio (OR). Results Sixty‐one studies (27 647 participants) were included. Fewer than 40% of intervention group participants were sustained abstinent at 3 months (bupropion: 37.1%; nicotine replacement therapy (NRT): 34.8%; varenicline: 39.3%); approximately a quarter were sustained abstinent at 6 months (bupropion: 25.9%; NRT: 26.6%; varenicline: 25.4%), and approximately a fifth were sustained abstinent at 12 months (bupropion: 19.9%; NRT: 19.8%%; varenicline: 18.7%). There was only a small decline in RR (3 months: 1.95 [95% confidence interval (CI) = 1.74–2.18, P

Smoking cessation medications (SCMs) are an evidence-based cornerstone of comprehensive tobacco control programs globally. However, the impact of SCMs on population smoking prevalence is controversial, with inconsistencies between randomized controlled trials (RCTs) and population-based observational studies. We estimated SCM impact on permanent cessation and population smoking prevalence by extrapolating efficacy estimates from meta-analyses of RCTs, using the standard population impact formula: efficacy*reach. We calculated the potential SCM impact under a range of assumptions for permanent cessation (20%,14%), behavioral support (yes/no), reach (40%-2%), and underlying smoking prevalence. Assuming behavioral support for all, depending on reach, 8%-0.3% of smokers are expected to quit permanently. Without behavioral support, permanent cessation is estimated to be 6.4%-0.2%. Assuming an underlying population smoking prevalence of 14%, (current U.S. prevalence), the maximum impact on population smoking prevalence is 1.12%. Impact on prevalence increases with increasing underlying country-specific levels of prevalence. With current U.S. levels of reach, behavioral support and smoking prevalence, we estimate that, based on a single course of treatment, 2.3% of smokers would quit permanently, contributing to a 0.3% decrease in population level smoking prevalence. Even under ideal conditions, the potential of current first-line SCMs to increase cessation in a substantial proportion of smokers, and reduce population smoking prevalence, is limited. In order to avert the predicted billion tobacco-caused deaths in this century, "safe and effective" medications are not sufficient: SCMs with high population impact are urgently needed. Policies to ensure the availability and accessibility of highly efficacious SCMs, with behavioral support, are crucial.

Mutations in the LRRK2 and GBA genes are the most common inherited causes of Parkinson's disease (PD). Studies exploring phenotypic differences based on genetic status used hypothesis-driven data-gathering and statistical-analyses focusing on specific symptoms, which may influence the validity of the results. We aimed to explore phenotypic expression in idiopathic PD (iPD) patients, G2019S-LRRK2-PD, and GBA-PD using a data-driven approach, allowing screening of large numbers of features while controlling selection bias. Data was collected from 1525 Ashkenazi Jews diagnosed with PD from the Tel-Aviv Medical center; 161 G2019S-LRRK2-PD, 222 GBA-PD, and 1142 iPD (no G2019S-LRRK2 or any of the 7 AJ GBA mutations tested). Data included 771 measures: demographics, cognitive, physical and neurological functions, performance-based measures, and non-motor symptoms. The association of the genotypes with each of the measures was tested while accounting for age at motor symptoms onset, gender, and disease duration; p-values were reported and corrected in a hierarchical approach for an average over the selected measures false discovery rate control, resulting in 32 measures. GBA-PD presented with more severe symptoms expression while LRRK2-PD had more benign symptoms compared to iPD. GBA-PD presented greater cognitive and autonomic involvement, more frequent hyposmia and REM sleep behavior symptoms while these were less frequent among LRRK2-PD compared to iPD. Using a data-driven analytical approach strengthens earlier studies and extends them to portray a possible unique disease phenotype based on genotype among AJ PD. Such findings could help direct a more personalized therapeutic approach.

Summary: heatmaply is an R package for easily creating interactive cluster heatmaps that can be shared online as a stand-alone HTML file. Interactivity includes a tooltip display of values when hovering over cells, as well as the ability to zoom in to specific sections of the figure from the data matrix, the side dendrograms, or annotated labels. Thanks to the synergistic relationship between heatmaply and other R packages, the user is empowered by a refined control over the statistical and visual aspects of the heatmap layout. Availability and implementation: The heatmaply package is available under the GPL-2 Open Source license. It comes with a detailed vignette, and is freely available from: http://cran.r-project.org/package=heatmaply. Supplementary information: Supplementary data are available at Bioinformatics online., Comment: 3 pages

Disability scales do not enable the transmission of concise, meaningful and daily function description for clinical purposes. Cross-sectional statistical analysis of 328 patients’ Spinal Cord Independence Measure (SCIM) III item scores (SIS). To develop a concise and clinically interpretable data-based characterization of daily task accomplishment for patients with spinal cord lesions (SCLs). Multi-center study at 13 spinal units in 6 countries. Patients were grouped into clusters characterized by smaller differences between the patients’ SIS within the clusters than between their centers, using the k-medoides algorithm. The number of clusters (k) was chosen according to the percent of SIS variation they explained and the clinical distinction between them. Analysis showed that k=8 SIS clusters offer a good description of the patient population. The eight functional clusters were designated as A–H, each cluster (grade) representing a combination of task accomplishments. Higher grades were usually (but not always) associated with patients implementing more difficult tasks. Throughout rehabilitation, the patients’ functional grade improved and the distribution of patients with similar functional grades within the total SCIM III score deciles remained stable. A new classification based on SIS clusters enables a concise description of overall functioning and task accomplishment distribution in patients with SCL. A software tool is used to identify the patients’ functional grade. Findings support the stability and utility of the grades for characterizing the patients’ functional status.

To obtain a perspective on an animal's own functional world, we study its behavior in situations that allow the animal to regulate the growth rate of its behavior and provide us with the opportunity to quantify its moment-by-moment developmental dynamics. Thus, we are able to show that mouse exploratory behavior consists of sequences of repeated motion: iterative processes that increase in extent and complexity, whose presumed function is a systematic active management of input acquired during the exploration of a novel environment. We use this study to demonstrate our approach to quantifying behavior: targeting aspects of behavior that are shown to be actively managed by the animal, and using measures that are discriminative across strains and treatments and replicable across laboratories.

Health informatics is facing many challenges these days, in analysing current medical data and especially hospital data towards understanding disease mechanisms, predicting the course of a disease or assist in targeting potential therapeutic options. Alongside the promises, many challenges emerge. Among the major ones we identify: current diagnosis criteria that are too vague to capture disease manifestation; the irrelevance of personalized medicine when only heterogeneous classes of patients are available, and how to properly process big data to avoid false claims. We offer a 3C strategy that starts from the medical knowledge, categorizing the available set of features into three types: the patients' assigned disease diagnosis, clinical measurements and potential biological markers, proceeds to an unsupervised learning process targeted to create new disease diagnosis classes, and finally, classifying the newly proposed diagnosis classes utilizing the potential biological markers. In order to allow the evaluation and comparison of different algorithmic components of the 3C strategy a simulation model was built and put to use. Our strategy, developed as part of the medical informatics work package at the EU Human Brain flagship Project strives to connect between potential biomarkers, and more homogeneous classes of disease manifestation that are expressed by meaningful features. We demonstrate this strategy using data from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI).

Background. A quadratic formula of the Spinal Cord Injury Ability Realization Measurement Index (SCI-ARMI) has previously been published. This formula was based on a model of Spinal Cord Independence Measure (SCIM95), the 95th percentile of the SCIM III values, which correspond with the American Spinal Injury Association Motor Scores (AMS) of SCI patients. Objective. To further develop the original formula. Setting. Spinal cord injury centers from 6 countries and the Statistical Laboratory, Tel-Aviv University, Israel. Methods. SCIM95 of 661 SCI patients was modeled, using a quantile regression with or without adjustment for age and gender, to calculate SCI-ARMI values. SCI-ARMI gain during rehabilitation and its correlations were examined. Results. A new quadratic SCIM95 model was created. This resembled the previously published model, which yielded similar SCIM95 values in all the countries, after adjustment for age and gender. Without this adjustment, however, only 86% of the non-Israeli SCIM III observations were lower than those SCIM95 values ( P < .0001). Adding the variables age and gender to the new model affected the SCIM95 value significantly ( P < .04). Adding country information did not add a significant effect ( P > .1). SCI-ARMI gain was positive (38.8 ± 22 points, P < .0001) and correlated weakly with admission age and AMS. Conclusions. The original quadratic SCI-ARMI formula is valid for an international population after adjustment for age and gender. The new formula considers more factors that affect functional ability following SCI.

One of the goals of the European Flagship Human Brain Project is to create a platform that will enable scientists to search for new biologically and clinically meaningful discoveries by making use of a large database of neurological data enlisted from many hospitals. While the patients whose data will be available have been diagnosed, there is a widespread concern that their diagnosis, which relies on current medical classification, may be too wide and ambiguous and thus hides important scientific information.

The spinal cord injury ability realization measurement index (SCI-ARMI) assesses rehabilitation potential and efficacy based on the linear relationship between the Spinal Cord Independence Measure (SCIM) and the American Spinal Injury Association impairment scale (AIS) motor scores (AMS). The objective of this study is to develop new SCI-ARMI formulas using a more flexible approach that is less sensitive to the distribution of the AMS score among spinal cord lesion (SCL) patients. Loewenstein Rehabilitation Hospital, Raanana, and the Statistical Laboratory, School of Mathematics, Faculty of Exact Sciences, Tel-Aviv University, Israel. SCIM III and AMS of 226 Israeli SCL patients were monitored. Linear formulas, quadratic formulas and non-parametric formulas were estimated to express the relationship between the 95th percentile of the SCIM III values for patients with given AMS at discharge from rehabilitation (SCIM95) and the corresponding AMS value. This relationship was used to calculate SCI-ARMI values, defined as the ratio of the observed SCIM score and the respective SCIM95 for a given patient's AMS score. The estimated quadratic formula for the relationship between the 95th percentile of the SCIM III and the AMS score was found to be most appropriate, and formulas are provided for SCIM95 calculation in the various areas of function. The use of these formulas to calculate SCI-ARMI values is presented. The new formulas improve the accuracy of calculated ability realization for any AMS. The new statistical procedure will be used for the upcoming data analysis of a larger-scale international SCI-ARMI study.

Background: The accumulation of data and its accessibility through easier-to-use platforms will allow data scientists and practitioners who are less sophisticated data analysts to get answers by using big data for many purposes in multiple ways. Data scientists working with medical data are aware of the importance of preprocessing, yet in many cases, the potential benefits of using nonlinear transformations is overlooked. Objective: Our aim is to present a semi-automated approach of symmetry-aiming transformations tailored for medical data analysis and its advantages. Methods: We describe 10 commonly encountered data types used in the medical field and the relevant transformations for each data type. Data from the Alzheimer’s Disease Neuroimaging Initiative study, Parkinson’s disease hospital cohort, and disease-simulating data were used to demonstrate the approach and its benefits. Results: Symmetry-targeted monotone transformations were applied, and the advantages gained in variance, stability, linearity, and clustering are demonstrated. An open source application implementing the described methods was developed. Both linearity of relationships and increase of stability of variability improved after applying proper nonlinear transformation. Clustering simulated nonsymmetric data gave low agreement to the generating clusters (Rand value=0.681), while capturing the original structure after applying nonlinear transformation to symmetry (Rand value=0.986). Conclusions: This work presents the use of nonlinear transformations for medical data and the importance of their semi-automated choice. Using the described approach, the data analyst increases the ability to create simpler, more robust and translational models, thereby facilitating the interpretation and implementation of the analysis by medical practitioners. Applying nonlinear transformations as part of the preprocessing is essential to the quality and interpretability of results.