MACHINE LEARNING APPROACHES FOR BIOMARKER IDENTIFICATION AND SUBGROUP DISCOVERY FOR POST-TRAUMATIC STRESS DISORDER

Post-traumatic stress disorder (PTSD) is a psychiatric disorder caused by environmental and genetic factors resulting from alterations in genetic variation, epigenetic changes and neuroimaging characteristics. There is a pressing need to identify reliable molecular and physiological biomarkers for accurate diagnosis, prognosis, and treatment, as well to deepen the understanding of PTSD pathophysiology. Machine learning methods are widely used to infer patterns from biological data, identify biomarkers, and make predictions. The objective of this research is to apply machine learning methods for the accurate classification of human diseases from genome-scale datasets, focusing primarily on PTSD.The DoD-funded Systems Biology of PTSD Consortium has recruited combat veterans with and without PTSD for measurement of molecular and physiological data from blood or urine samples with the goal of identifying accurate and specific PTSD biomarkers. As a member of the Consortium with access to these PTSD multiple omics datasets, we first completed a project titled Clinical Subgroup-Specific PTSD Classification and Biomarker Discovery. We applied machine learning approaches to these data to build classification models consisting of molecular and clinical features to predict PTSD status. We also identified candidate biomarkers for diagnosis, which improves our understanding of PTSD pathogenesis. In a second project, entitled Multi-Omic PTSD Subgroup Identification and Clinical Characterization, we applied methods for integrating multiple omics datasets to investigate the complex, multivariate nature of the biological systems underlying PTSD. We identified an optimal 2 PTSD subgroups using two different machine learning approaches from 82 PTSD positive samples, and we found that the subgroups exhibited different remitting behavior as inferred from subjects recalled at a later time point. The results from our association, differential expression, and classification analyses demonstrated the distinct clinical and molecular features characterizing these subgroups.Taken together, our work has advanced our understanding of PTSD biomarkers and subgroups through the use of machine learning approaches. Results from our work should strongly contribute to the precise diagnosis and eventual treatment of PTSD, as well as other diseases. Future work will involve continuing to leverage these results to enable precision medicine for PTSD

Superpixel-based conditional random fields (SuperCRF) : incorporating global and local context for enhanced deep learning in melanoma histopathology

Computational pathology-based cell classification algorithms are revolutionizing the study of the tumor microenvironment and can provide novel predictive/prognosis biomarkers crucial for the delivery of precision oncology. Current algorithms used on hematoxylin and eosin slides are based on individual cell nuclei morphology with limited local context features. Here, we propose a novel multi-resolution hierarchical framework (SuperCRF) inspired by the way pathologists perceive regional tissue architecture to improve cell classification and demonstrate its clinical applications. We develop SuperCRF by training a state-of-art deep learning spatially constrained- convolution neural network (SC-CNN) to detect and classify cells from 105 high-resolution (20×) H&E-stained slides of The Cancer Genome Atlas melanoma dataset and subsequently, a conditional random field (CRF) by combining cellular neighborhood with tumor regional classification from lower resolution images (5, 1.25×) given by a superpixel-based machine learning framework. SuperCRF led to an 11.85% overall improvement in the accuracy of the state-of-art deep learning SC-CNN cell classifier. Consistent with a stroma-mediated immune suppressive microenvironment, SuperCRF demonstrated that (i) a high ratio of lymphocytes to all lymphocytes within the stromal compartment (p = 0.026) and (ii) a high ratio of stromal cells to all cells (p < 0.0001 compared to p = 0.039 for SC-CNN only) are associated with poor survival in patients with melanoma. SuperCRF improves cell classification by introducing global and local context-based information and can be implemented in combination with any single-cell classifier. SuperCRF provides valuable tools to study the tumor microenvironment and identify predictors of survival and response to therapy

A systematic review and meta-analysis of predictive and prognostic models for outcome prediction using positron emission tomography radiomics in head and neck squamous cell carcinoma patients

FUNDING INFORMATIONMMP was funded by the University of Aberdeen under the Elphinstone Scholarship. The University of Aberdeen Open Access Fund supported the open access publication.Peer reviewedPublisher PD

Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction

Survival prediction is a complicated ordinal regression task that aims to predict the ranking risk of death, which generally benefits from the integration of histology and genomic data. Despite the progress in joint learning from pathology and genomics, existing methods still suffer from challenging issues: 1) Due to the large size of pathological images, it is difficult to effectively represent the gigapixel whole slide images (WSIs). 2) Interactions within tumor microenvironment (TME) in histology are essential for survival analysis. Although current approaches attempt to model these interactions via co-attention between histology and genomic data, they focus on only dense local similarity across modalities, which fails to capture global consistency between potential structures, i.e. TME-related interactions of histology and co-expression of genomic data. To address these challenges, we propose a Multimodal Optimal Transport-based Co-Attention Transformer framework with global structure consistency, in which optimal transport (OT) is applied to match patches of a WSI and genes embeddings for selecting informative patches to represent the gigapixel WSI. More importantly, OT-based co-attention provides a global awareness to effectively capture structural interactions within TME for survival prediction. To overcome high computational complexity of OT, we propose a robust and efficient implementation over micro-batch of WSI patches by approximating the original OT with unbalanced mini-batch OT. Extensive experiments show the superiority of our method on five benchmark datasets compared to the state-of-the-art methods. The code is released.Comment: 11 pages, 4 figures, accepted by ICCV 202

Deep learning for clinical decision support in oncology

In den letzten Jahrzehnten sind medizinische Bildgebungsverfahren wie die Computertomographie (CT) zu einem unersetzbaren Werkzeug moderner Medizin geworden, welche eine zeitnahe, nicht-invasive Begutachtung von Organen und Geweben ermöglichen. Die Menge an anfallenden Daten ist dabei rapide gestiegen, allein innerhalb der letzten Jahre um den Faktor 15, und aktuell verantwortlich für 30 % des weltweiten Datenvolumens. Die Anzahl ausgebildeter Radiologen ist weitestgehend stabil, wodurch die medizinische Bildanalyse, angesiedelt zwischen Medizin und Ingenieurwissenschaften, zu einem schnell wachsenden Feld geworden ist. Eine erfolgreiche Anwendung verspricht Zeitersparnisse, und kann zu einer höheren diagnostischen Qualität beitragen. Viele Arbeiten fokussieren sich auf „Radiomics“, die Extraktion und Analyse von manuell konstruierten Features. Diese sind jedoch anfällig gegenüber externen Faktoren wie dem Bildgebungsprotokoll, woraus Implikationen für Reproduzierbarkeit und klinische Anwendbarkeit resultieren. In jüngster Zeit sind Methoden des „Deep Learning“ zu einer häufig verwendeten Lösung algorithmischer Problemstellungen geworden. Durch Anwendungen in Bereichen wie Robotik, Physik, Mathematik und Wirtschaft, wurde die Forschung im Bereich maschinellen Lernens wesentlich verändert. Ein Kriterium für den Erfolg stellt die Verfügbarkeit großer Datenmengen dar. Diese sind im medizinischen Bereich rar, da die Bilddaten strengen Anforderungen bezüglich Datenschutz und Datensicherheit unterliegen, und oft heterogene Qualität, sowie ungleichmäßige oder fehlerhafte Annotationen aufweisen, wodurch ein bedeutender Teil der Methoden keine Anwendung finden kann. Angesiedelt im Bereich onkologischer Bildgebung zeigt diese Arbeit Wege zur erfolgreichen Nutzung von Deep Learning für medizinische Bilddaten auf. Mittels neuer Methoden für klinisch relevante Anwendungen wie die Schätzung von Läsionswachtum, Überleben, und Entscheidungkonfidenz, sowie Meta-Learning, Klassifikator-Ensembling, und Entscheidungsvisualisierung, werden Wege zur Verbesserungen gegenüber State-of-the-Art-Algorithmen aufgezeigt, welche ein breites Anwendungsfeld haben. Hierdurch leistet die Arbeit einen wesentlichen Beitrag in Richtung einer klinischen Anwendung von Deep Learning, zielt auf eine verbesserte Diagnose, und damit letztlich eine verbesserte Gesundheitsversorgung insgesamt.Over the last decades, medical imaging methods, such as computed tomography (CT), have become an indispensable tool of modern medicine, allowing for a fast, non-invasive inspection of organs and tissue. Thus, the amount of acquired healthcare data has rapidly grown, increased 15-fold within the last years, and accounts for more than 30 % of the world's generated data volume. In contrast, the number of trained radiologists remains largely stable. Thus, medical image analysis, settled between medicine and engineering, has become a rapidly growing research field. Its successful application may result in remarkable time savings and lead to a significantly improved diagnostic performance. Many of the work within medical image analysis focuses on radiomics, i. e. the extraction and analysis of hand-crafted imaging features. Radiomics, however, has been shown to be highly sensitive to external factors, such as the acquisition protocol, having major implications for reproducibility and clinical applicability. Lately, deep learning has become one of the most employed methods for solving computational problems. With successful applications in diverse fields, such as robotics, physics, mathematics, and economy, deep learning has revolutionized the process of machine learning research. Having large amounts of training data is a key criterion for its successful application. These data, however, are rare within medicine, as medical imaging is subject to a variety of data security and data privacy regulations. Moreover, medical imaging data often suffer from heterogeneous quality, label imbalance, and label noise, rendering a considerable fraction of deep learning-based algorithms inapplicable. Settled in the field of CT oncology, this work addresses these issues, showing up ways to successfully handle medical imaging data using deep learning. It proposes novel methods for clinically relevant tasks, such as lesion growth and patient survival prediction, confidence estimation, meta-learning and classifier ensembling, and finally deep decision explanation, yielding superior performance in comparison to state-of-the-art approaches, and being applicable to a wide variety of applications. With this, the work contributes towards a clinical translation of deep learning-based algorithms, aiming for an improved diagnosis, and ultimately overall improved patient healthcare

Analysis of an immune focused targeted genetic association study in intermediate-risk Melanoma

The Centers for Disease Control and Prevention (CDC) estimates that about 8,000 deaths in the United States are caused by melanoma skin cancer each year. Melanoma has become the most lethal skin cancer over the past three decades. Immunotherapies were introduced to Melanoma patients in the 60’s, and Interferon Alpha (IFN α) is one of the mostly used drugs for immunotherapy. Previous studies showed that using IFN α-2b might increase the survival rate of patients with high-risk melanoma skin cancer. However, not all patients respond to immunotherapies. So ECOG 1697 (E1697) trial was performed to compare the effect of patients obtained four-week high-dose IFN-a2b and the control group. This project utilizes a subset of the E1697 patients to search for potential immune-related genes that are associated with the prognosis of patients with localized melanoma. Both SNP and gene level analysis were conducted. This study has important public health significance because it identifies genetic factors associated with prognosis of local melanoma, which may be used to guide the treatment of this subgroup of melanoma patients in the future

Emergency Diesel-Electric Generator Set Maintenance and Test Periodicity

Manufacturer and industry recommendations vary considerably for maintenance and tests of emergency diesel-electric generator sets in emergency standby duty. There is little consistency among generator sets of similar technology, and manufacturers and their representatives often provide contradictory guidance. As a result, periodicity of emergency diesel-electric generator set maintenance and tests varies considerably in practice. Utilizing the framework proposed and tested by Fehr (2014), this research developed a parametric regression survival model of the reliability of modern diesel-electric generator sets in emergency standby duty as a function of maintenance, age, and cumulative run hours. A survival regression technique leveraging Cox’s (1972) methods was developed to combine multiple exponential and Weibull (1951) distributions into a single model to represent emergency diesel-electric generator sets and other complex machinery exhibiting multiple independent failure distributions. A generalized model and reliability tables derived from that model are presented along with maintenance and test recommendations to assist managers in determining the optimal maintenance program for a diesel-electric generator set