Dragana Miljkovic

Signalling Network Construction for Modelling Plant Defence Response

Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2) triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be utilised for modelling other biological systems, given that an adequate vocabulary is provided.

PD-Manager: An mHealth platform for Parkinson's disease patient management

PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinsons disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severity of most of the PD motor symptoms. Besides motor symptoms monitoring, the patients mobile application also provides various non-motor self-evaluation tests for assessing cognition, mood and nutrition to motivate them in becoming more active in managing their disease. All data from the mobile application and the sensors is transferred to a cloud infrastructure to allow easy access for clinicians and further processing. Clinicians can access this information using a separate mobile application that is specifically designed for their respective needs to provide faster and more accurate assessment of PD symptoms that facilitate patient evaluation. Machine learning techniques are used to estimate symptoms and disease progression trends to further enhance the provided information. The platform is also complemented with a decision support system (DSS) that notifies clinicians for the detection of new symptoms or the worsening of existing ones. As patients symptoms are progressing, the DSS can also provide specific suggestions regarding appropriate medication changes.

Machine Learning and Data Mining Methods for Managing Parkinson’s Disease

Parkinson’s disease (PD) results primarily from dying of dopaminergic neurons in the Substantia Nigra, a part of the Mesencephalon (midbrain), which is not curable to date. PD medications treat symptoms only, none halt or retard dopaminergic neuron degeneration. Here machine learning methods can be of help since one of the crucial roles in the management and treatment of PD patients is detection and classification of tremors. In the clinical practice, this is one of the most common movement disorders and is typically classified using behavioral or etiological factors. Another important issue is to detect and evaluate PD related gait patterns, gait initiation and freezing of gait, which are typical symptoms of PD. Medical studies have shown that 90% of people with PD suffer from vocal impairment, consequently the analysis of voice data to discriminate healthy people from PD is relevant. This paper provides a quick overview of the state-of-the-art and some directions for future research, motivated by the ongoing PD_manager project.

Analysis of medications change in Parkinson’s disease progression data

Parkinson’s disease is a neurodegenerative disorder that affects people worldwide. Careful management of patient’s condition is crucial to ensure the patient’s independence and quality of life. This is achieved by personalized treatment based on individual patient’s symptoms and medical history. The aim of this study is to determine patient groups with similar disease progression patterns coupled with patterns of medications change that lead to the improvement or decline of patients’ quality of life symptoms. To this end, this paper proposes a new methodology for clustering of short time series of patients’ symptoms and prescribed medications data, and time sequence data analysis using skip-grams to monitor disease progression. The results demonstrate that motor and autonomic symptoms are the most informative for evaluating the quality of life of Parkinson’s disease patients. We show that Parkinson’s disease patients can be divided into clusters ordered in accordance with the severity of their symptoms. By following the evolution of symptoms for each patient separately, we were able to determine patterns of medications change which can lead to the improvement or worsening of the patients’ quality of life.

Constraint-driven optimization of plant defense model parameters

Biologists have been investigating the plant defense response to virus infections for a long time. Nevertheless, its model has still not been developed. One of the reasons is the deficiency in numerical kinetic data that brings up the importance of the expert knowledge. Therefore, we based our work on acquiring domain knowledge of biological pathways which provided a basis for the construction of a dynamic mathematical model. The goal of our work was to model the major pathway of the plant defense response - the salicylic acid pathway - and determine its dynamic parameters that are in correspondence with the knowledge acquired from the biology experts. For this purpose, we first selected the Hybrid Functional Petri Net formalism to represent the model due to its intuitive graph representation important for the biologists and its mathematical capabilities necessary for the simulation. The salicylic acid model was manually constructed and curated. In addition, the knowledge related to the model variables was acquired from the biology scientists and formalized in the form of constraints. This enabled an automatic optimization search for the model parameters that violate the minimal number of constraints. If the simulation results do not match the expert expectations, the network structure and the constraint definition are revised and the optimization parameter search is repeated. The final results of our system are both simulation results and optimized model parameters, which provide an insight into the biological system. Our constraint-driven optimization approach allows for an efficient exploration of the dynamic behavior of the biological models and, at the same time, increases their reliability.

Symptoms and medications change patterns for Parkinson's disease patients stratification

Quality of life of patients with Parkinsons disease degrades significantly with disease progression. This paper presents a step towards personalized management of Parkinsons disease patients, based on discovering groups of similar patients. Similarity is based on patients medical conditions and changes in the prescribed therapy when the medical conditions change. We present two novel approaches. The first algorithm discovers symptoms impact on Parkinsons disease progression. Experiments on the Parkinson Progression Markers Initiative (PPMI) data reveal a subset of symptoms influencing disease progression which are already established in Parkinsons disease literature, as well as symptoms that are considered only recently as possible indicators of disease progression by clinicians. The second novelty is a methodology for detecting patterns of medications dosage changes based on the patient status. The methodology combines multitask learning using predictive clustering trees and short time series analysis to better understand when a change in medications is required. The experiments on PPMI data demonstrate that, using the proposed methodology, we can identify some clinically confirmed patients symptoms suggesting medications change. In terms of predictive performance, our multitask predictive clustering tree approach is mostly comparable to the random forest multitask model, but has the advantage of model interpretability.

Combining Multitask Learning and Short Time Series Analysis in Parkinson’s Disease Patients Stratification

Quality of life of patients with Parkinson’s disease degrades significantly with disease progression. This paper presents a step towards personalized medicine management of Parkinson’s disease patients, based on discovering groups of similar patients. Similarity is based on patients’ medical conditions and changes in the prescribed therapy when the medical conditions change. The presented methodology combines multitask learning using predictive clustering trees and short time series analysis to better understand when a change in medications is required. The experiments on PPMI (Parkinson Progression Markers Initiative) data demonstrate that using the proposed methodology we can identify some clinically confirmed patients’ symptoms suggesting medications change.

Multi-view Approach to Parkinson’s Disease Quality of Life Data Analysis

Parkinson’s disease is a neurodegenerative disorder that affects people worldwide. While the motor symptoms such as tremor, rigidity, bradykinesia and postural instability are predominant, patients experience also non-motor symptoms, such as decline of cognitive abilities, behavioural problems, sleep disturbances, and other symptoms that greatly affect their quality of life. Careful management of patient’s condition is crucial to ensure the patient’s independence and the best possible quality of life. This is achieved by personalized medication treatment based on individual patient’s symptoms and medical history. This paper explores the utility of machine learning to help development of decision models, aimed to support clinicians’ decisions regarding patients’ therapies. We propose a new multi-view methodology for determining groups of patients with similar symptoms and detecting patterns of medications changes that lead to the improvement or decline of patients’ quality of life. We identify groups of patients ordered in accordance to their quality of life assessment and find examples of therapy modifications which induce positive or negative change of patients’ symptoms. The results demonstrate that motor and autonomic symptoms are the most informative for evaluating the quality of life of Parkinson’s disease patients.

Integrating semantic transcriptomic data analysis and knowledge extraction from biological literature

The paper presents an approach to the holistic analysis of transcriptomic data which integrates two state-of-the-art methodologies into a coherent framework. The aim of the proposed approach is to give insight into the discovered patterns, help explaining the observed phenomena, enable the creation of new research hypotheses and assist in design of new experiments. We have integrated a methodology for semantic analysis of transcriptomic data, a system for automated extraction of biological relations from the literature, and a number of supporting components. The approach is demonstrated and evaluated on a publicly available dataset from a clinical trial in acute lymphoblastic leukaemia and a document corpus of full-text articles from the PubMed Open Access Subset.

Modelling a biological system: network creation by triplet extraction from biological literature

The chapter proposes an approach to support modelling of plant defence response to pathogen attacks. Such models are currently built manually from expert knowledge, experimental results, and literature search, which is a very time consuming process. Manual model construction can be effectively complemented by automated model extraction from biological literature. This work focuses on the construction of triplets in the form of subject-predicate-object extracted from scientific papers, which are used by the Biomine automated graph construction and visualisation engine to create the biological model. The approach was evaluated by comparing the automatically generated graph with a manually developed Petri net model of plant defence. This approach to automated model creation was explored also in a bisociative setting. The emphasis is not on creative knowledge discovery, but rather on specifying and crossing the boundaries of knowledge of individual scientists. This could be used to model the expertise of virtual scientific consortia.