912 research outputs found
Computational Approaches to Drug Profiling and Drug-Protein Interactions
Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a
long period of stagnation in drug approvals. Due to the extreme costs associated with
introducing a drug to the market, locating and understanding the reasons for clinical failure
is key to future productivity. As part of this PhD, three main contributions were made in
this respect. First, the web platform, LigNFam enables users to interactively explore
similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly,
two deep-learning-based binding site comparison tools were developed, competing with
the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the
open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold
relationships and has already been used in multiple projects, including integration into a
virtual screening pipeline to increase the tractability of ultra-large screening experiments.
Together, and with existing tools, the contributions made will aid in the understanding of
drug-protein relationships, particularly in the fields of off-target prediction and drug
repurposing, helping to design better drugs faster
Applying machine learning: a multi-role perspective
Machine (and deep) learning technologies are more and more present in several fields. It is undeniable that many aspects of our society are empowered by such technologies: web searches, content filtering on social networks, recommendations on e-commerce websites, mobile applications, etc., in addition to academic research. Moreover, mobile devices and internet sites, e.g., social networks, support the collection and sharing of information in real time. The pervasive deployment of the aforementioned technological instruments, both hardware and software, has led to the production of huge amounts of data. Such data has become more and more unmanageable, posing challenges to conventional computing platforms, and paving the way to the development and widespread use of the machine and deep learning. Nevertheless, machine learning is not only a technology. Given a task, machine learning is a way of proceeding (a way of thinking), and as such can be approached from different perspectives (points of view). This, in particular, will be the focus of this research. The entire work concentrates on machine learning, starting from different sources of data, e.g., signals and images, applied to different domains, e.g., Sport Science and Social History, and analyzed from different perspectives: from a non-data scientist point of view through tools and platforms; setting a problem stage from scratch; implementing an effective application for classification tasks; improving user interface experience through Data Visualization and eXtended Reality. In essence, not only in a quantitative task, not only in a scientific environment, and not only from a data-scientist perspective, machine (and deep) learning can do the difference
Statistical Anomaly Discovery Through Visualization
Developing a deep understanding of data is a crucial part of decision-making processes.
It often takes substantial time and effort to develop a solid understanding to make well-informed
decisions. Data analysts often perform statistical analyses through visualization
to develop such understanding. However, applicable insight can be difficult due to biases
and anomalies in data. An often overlooked phenomenon is mix effects, in which subgroups
of data exhibit patterns opposite to the data as a whole. This phenomenon is widespread
and often leads inexperienced analysts to draw contradictory conclusions. Discovering such
anomalies in data becomes challenging as data continue to grow in volume, dimensionality,
and cardinality. Effectively designed data visualizations empower data analysts to reveal
and understand patterns in data for studying such paradoxical anomalies.
This research explores several approaches for combining statistical analysis and visualization
to discover and examine anomalies in multidimensional data. It starts with an automatic
anomaly detection method based on correlation comparison and experiments to determine
the running time and complexity of the algorithm. Subsequently, the research investigates
the design, development, and implementation of a series of visualization techniques to fulfill
the needs of analysis through a variety of statistical methods. We create an interactive visual
analysis system, Wiggum, for revealing various forms of mix effects. A user study to evaluate
Wiggum strengthens understanding of the factors that contribute to the comprehension of
statistical concepts. Furthermore, a conceptual model, visual correspondence, is presented
to study how users can determine the identity of items between visual representations by
interpreting the relationships between their respective visual encodings. It is practical to
build visualizations with highly linked views informed by visual correspondence theory. We
present a hybrid tree visualization technique, PatternTree, which applies the visual
correspondence theory. PatternTree supports users to more readily discover statistical anomalies
and explore their relationships. Overall, this dissertation contributes a merging of new visualization
theory and designs for analysis of statistical anomalies, thereby leading the way to
the creation of effective visualizations for statistical analysis
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