Developing predictive precision medicine models by exploiting real-world data using machine learning methods.

Computational Medicine encompasses the application of Statistical Machine Learning and Artificial Intelligence methods on several traditional medical approaches, including biochemical testing which is extremely valuable both for early disease prognosis and long-term individual monitoring, as it can provide important information about a person's health status. However, using Statistical Machine Learning and Artificial Intelligence algorithms to analyze biochemical test data from Electronic Health Records requires several preparatory steps, such as data manipulation and standardization. This study presents a novel approach for utilizing Electronic Health Records from large, real-world databases to develop predictive precision medicine models by exploiting Artificial Intelligence. Furthermore, to demonstrate the effectiveness of this approach, we compare the performance of various traditional Statistical Machine Learning and Deep Learning algorithms in predicting individuals' future biochemical test outcomes. Specifically, using data from a large real-world database, we exploit a longitudinal format of the data in order to predict the future values of 15 biochemical tests and identify individuals at high risk. The proposed approach and the extensive model comparison contribute to the personalized approach that modern medicine aims to achieve.

Investigating the overlap of machine learning algorithms in the final results of RNA-seq analysis on gene expression estimation.

Advances in computer science in combination with the next-generation sequencing have introduced a new era in biology, enabling advanced state-of-the-art analysis of complex biological data. Bioinformatics is evolving as a union field between computer Science and biology, enabling the representation, storage, management, analysis and exploration of many types of data with a plethora of machine learning algorithms and computing tools. In this study, we used machine learning algorithms to detect differentially expressed genes between different types of cancer and showing the existence overlap to final results from RNA-sequencing analysis. The datasets were obtained from the National Center for Biotechnology Information resource. Specifically, dataset GSE68086 which corresponds to PMID:200,068,086. This dataset consists of 171 blood platelet samples collected from patients with six different tumors and healthy individuals. All steps for RNA-sequencing analysis (preprocessing, read alignment, transcriptome reconstruction, expression quantification and differential expression analysis) were followed. Machine Learning- based Random Forest and Gradient Boosting algorithms were applied to predict significant genes. The Rstudio statistical tool was used for the analysis.

TarBase-v9.0 extends experimentally supported miRNA-gene interactions to cell-types and virally encoded miRNAs.

TarBase is a reference database dedicated to produce, curate and deliver high quality experimentally-supported microRNA (miRNA) targets on protein-coding transcripts. In its latest version (v9.0, https://dianalab.e-ce.uth.gr/tarbasev9), it pushes the envelope by introducing virally-encoded miRNAs, interactions leading to target-directed miRNA degradation (TDMD) events and the largest collection of miRNA-gene interactions to date in a plethora of experimental settings, tissues and cell-types. It catalogues ∼6 million entries, comprising ∼2 million unique miRNA-gene pairs, supported by 37 experimental (high- and low-yield) protocols in 172 tissues and cell-types. Interactions are annotated with rich metadata including information on genes/transcripts, miRNAs, samples, experimental contexts and publications, while millions of miRNA-binding locations are also provided at cell-type resolution. A completely re-designed interface with state-of-the-art web technologies, incorporates more features, and allows flexible and ingenious use. The new interface provides the capability to design sophisticated queries with numerous filtering criteria including cell lines, experimental conditions, cell types, experimental methods, species and/or tissues of interest. Additionally, a plethora of fine-tuning capacities have been integrated to the platform, offering the refinement of the returned interactions based on miRNA confidence and expression levels, while boundless local retrieval of the offered interactions and metadata is enabled.

Analysing sentiment change detection of Covid-19 tweets.

The Covid-19 pandemic made a significant impact on society, including the widespread implementation of lockdowns to prevent the spread of the virus. This measure led to a decrease in face-to-face social interactions and, as an equivalent, an increase in the use of social media platforms, such as Twitter. As part of Industry 4.0, sentiment analysis can be exploited to study public attitudes toward future pandemics and sociopolitical situations in general. This work presents an analysis framework by applying a combination of natural language processing techniques and machine learning algorithms to classify the sentiment of each tweet as positive, or negative. Through extensive experimentation, we expose the ideal model for this task and, subsequently, utilize sentiment predictions to perform time series analysis over the course of the pandemic. In addition, a change point detection algorithm was applied in order to identify the turning points in public attitudes toward the pandemic, which were validated by cross-referencing the news report at that particular period of time. Finally, we study the relationship between sentiment trends on social media and, news coverage of the pandemic, providing insights into the public's perception of the pandemic and its influence on the news.

A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project.

The ATHLOS cohort is composed of several harmonized datasets of international groups related to health and aging. As a result, the Healthy Aging index has been constructed based on a selection of variables from 16 individual studies. In this paper, we consider additional variables found in ATHLOS and investigate their utilization for predicting the Healthy Aging index. For this purpose, motivated by the volume and diversity of the dataset, we focus our attention upon data clustering, where unsupervised learning is utilized to enhance prediction power. Thus we show the predictive utility of exploiting hidden data structures. In addition, we demonstrate that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering, within a clustering for ensemble classification scheme, while retaining prediction benefits. We propose a complete methodology that is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A straightforward open source implementation for the R project is also provided (https://github.com/Petros-Barmpas/HCEP). Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-022-00171-1.

Temporal trends in pulmonary embolism prevalence in Greece during 2013-2017.

BACKGROUND: Pulmonary embolism (PE) epidemiological data about the disease prevalence in the general population are unclear. The present study aims to investigate the prevalence of PE in Greece and the associated temporal trends for the years 2013-2017. METHODS: Data on medical prescriptions for PE in the years 2013-2017 were provided by the Greek National Health Service Organization (EOPYY). Data on age, gender, specialty of the prescribing physician and prescription unit were provided as well. RESULTS: The total number of medical prescriptions for PE for the study period was 101,426. Of the total prescriptions, 51% were issued by the Public Sector and 48% by the Private Sector. In 2013 the prevalence of PE was 5.43 cases per 100,000 citizens and increased constantly until 2017 with 23.79 cases per 100,000 population. Prevalence was higher in all years studied in the age group of 70-80 years. For the year 2017, we observed 69.35 cases per 100,000 population for subjects 70-80 years, followed by the ages 80-90 (60.58/100,000) and 60-70 years (56.47 /100,000). Females displayed higher PE prevalence than males and higher increasing trend. CONCLUSION: PE prevalence has an increasing trend throughout the years 2013-2017 while prevalence in females is higher than males and displays a higher increasing trend. Our results may be used to appropriately organize nationwide health care campaigns aiming at the diagnosis, treatment and prevention of PE.

Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification.

This paper proposes a novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequences. Training is performed with weakly annotated images, using only image-level, semantic labels instead of detailed, and pixel-level annotations. This makes it a cost-effective approach for the analysis of large videoendoscopy repositories. Other advantages of the proposed methodology include its capability to suggest possible locations of GI anomalies within the video frames, and its generality, in the sense that abnormal frame detection is based on automatically derived image features. It is implemented in three phases: 1) it classifies the video frames into abnormal or normal using a weakly supervised convolutional neural network (WCNN) architecture; 2) detects salient points from deeper WCNN layers, using a deep saliency detection algorithm; and 3) localizes GI anomalies using an iterative cluster unification (ICU) algorithm. ICU is based on a pointwise cross-feature-map (PCFM) descriptor extracted locally from the detected salient points using information derived from the WCNN. Results, from extensive experimentation using publicly available collections of gastrointestinal endoscopy video frames, are presented. The data sets used include a variety of GI anomalies. Both anomaly detection and localization performance achieved, in terms of the area under receiver operating characteristic (AUC), were >80%. The highest AUC for anomaly detection was obtained on conventional gastroscopy images, reaching 96%, and the highest AUC for anomaly localization was obtained on wireless capsule endoscopy images, reaching 88%.

Cell-nuclear data reduction and prognostic model selection in bladder tumor recurrence.

OBJECTIVE: The paper aims at improving the prediction of superficial bladder recurrence. To this end, feedforward neural networks (FNNs) and a feature selection method based on unsupervised clustering, were employed. MATERIAL AND METHODS: A retrospective prognostic study of 127 patients diagnosed with superficial urinary bladder cancer was performed. Images from biopsies were digitized and cell nuclei features were extracted. To design FNN classifiers, different training methods and architectures were investigated. The unsupervised k-windows (UKW) and the fuzzy c-means clustering algorithms were applied on the feature set to identify the most informative feature subsets. RESULTS: UKW managed to reduce the dimensionality of the feature space significantly, and yielded prediction rates 87.95% and 91.41%, for non-recurrent and recurrent cases, respectively. The prediction rates achieved with the reduced feature set were marginally lower compared to the ones attained with the complete feature set. The training algorithm that exhibited the best performance in all cases was the adaptive on-line backpropagation algorithm. CONCLUSIONS: FNNs can contribute to the accurate prognosis of bladder cancer recurrence. The proposed feature selection method can remove redundant information without a significant loss in predictive accuracy, and thereby render the prognostic model less complex, more robust, and hence suitable for clinical use.