Application of machine learning techniques to biological big data
Creators: name: Auriemma Citarella, Alessia
Publisher: name: Universita degli studi di Salerno
Year: 2022
Subjects: subject: INF/01 INFORMATICA
subject: Machine learning
subject: Biological big data
subject: Deep learning

description: 2020 - 2021
description: To date, has been the primary driver of global innovation, compet- itiveness and cultural development. It is also a powerful engine for creating new job opportunities, expanding market segments and inspiring new horizons where new skills and specialties can compete. From this perspective, we are constantly pushed to investigate the ICT industry and its interconnections with other areas, such as new biomedical technology. In the past twenty years, the development and increase of new diagnostic methods in the medical field has made available a huge amount of data capable of being stored and analyzed in order to extract important new knowledge. In the biological field, the data produced by the sequencing techniques and the available databases provide a lot of informa- tion on multiple levels that can be integrated with each other. The ability to integrate and analyze data from multiple sources is vital in order to collect real benefits and speed up outputs thanks to the high computational possibilities of some tools. Technology has the potential to dramatically change the con- ception of medicine and, at the same time, it plays a critical role in advanced diagnostics systems in making decisions intrinsic to patient care. Developing high-quality, accurate Artificial Intelli- gence (AI) resources improves work of clinicians by intervening on prevention, diagnosis and treatment of many pathologies. Some modern AI and computer science technologies, in general, encompass the power of clinical laboratory devices, allowing diagnostic activity to be carried out even outside of laboratories. Medical aids and new advanced diagnostic equipment are in- creasingly relying on qualified experts in the field to supplement medical evaluations and assist in diagnosis. In this context, the focus of this work was on two main topics. First one, we explored additional Machine Learning and Deep Learning techniques that can guarantee a better classification of melanoma images even on clinical datasets with lower im- age quality. The goal is to improve melanoma early detection, vii which is now a limiting factor for first-line therapies in this tumor pathology. Many of the research in the literature utilize similar strategies but use various approaches: some try to extract infor- mation directly from the image (such as color, plot and pixel density), while others try to extract functions based on guided lines of dermatologists (such as ABCDE and the Seven-Point Checklist). The majority of these researches are conducted us- ing higher-resolution dermoscopic pictures. The purpose of the research is to identify novel features for melanoma classifica- tion that may be applied to less detailed images using advanced learning techniques. The second contribution of this thesis is addressed to the clas- sification of proteins. Researches focused on the possibility of exploring further molecular descriptors in addition to those al- ready present in the literature to classify these proteins and to build new tools able to explore the complex interaction between proteins in a visual and intuitive way. In this line of research, the visualization of biological data was also taken into considera- tion. The work has mostly concentrated on the presentation tools of biological ontologies in order to develop user-friendly sys- tems that allow end users to interact and extrapolate information more easily. This is useful for the complexity of the biological system that can be explored by the integration of omics disci- plines. These sciences attempt to analyze the biological system holistically using biological Big Data, mainly proteomic, genomic, transcriptomic and metabolomic data. The latter are the most important groupings of organic compounds for the study of the functioning of living organisms. [edited by Author]