Available soon.
Sjaak Brinkkemper, Utrecht University, Netherlands
Crossing Borders: My Research Journey from Theory to Applications in Biomedical Signal Processing
Jordi Solé-Casals, Data and Signal Processing Group, University of Vic - Central University of Catalonia, Spain
Analysis CNVs in Cancer and Normal Tissues with Transcriptomics Data
Peter Kharchenko, Altos Labs San Diego Institute of Science, United States
Data-and Knowledge-driven Medical Image Computing and its Contribution to Precision Medicine
Jun Xu, Nanjing Univ. of Info. Sci., China
Available soon.
Sjaak Brinkkemper
Utrecht University
Netherlands
https://www.uu.nl/staff/SBrinkkemper/0
Brief Bio
Prof. Dr. Sjaak Brinkkemper is full professor of Software Production at the Department of Information and Computing Sciences of Utrecht University, the Netherlands. He leads a group of about twenty-five researchers specialized in large-scale software product development and software entrepreneurship. The main research themes of the group are the methodology of software production, digital ecosystems, IT sustainability, and requirements engineering. Brinkkemper's research interests: software production, requirements engineering, software architecture, and method engineering. In 2018 he started the Care2Report research program on the architecting and development of automated reporting in societal sectors, such as healthcare, police, and business analysis.
Crossing Borders: My Research Journey from Theory to Applications in Biomedical Signal Processing
Jordi Solé-Casals
Data and Signal Processing Group, University of Vic - Central University of Catalonia
Spain
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Brief Bio
Jordi Solé-Casals currently holds a permanent position as a Full Professor of the Department of Engineering of the University of Vic – Central University of Catalonia and is the head of the Data and Signal Processing Research Group (DSP, UVic-UCC). He is also Visiting Scientist (2016 ~) at the Brain Mapping Unit of the Department of Psychiatry of the University of Cambridge (UK) and Visiting Scientist (2020 ~) at the College of Artificial Intelligence, Nankai University (China). He obtained the Ph.D. degree with European label in 2000, and the B.Sc. degree in Telecommunications in 1995, both from the Polytechnic University of Catalonia (UPC), Barcelona; and the B.Hum in 2010 from the Open University of Catalonia (UOC), Barcelona. In 1994 he joined the Department of Engineering of the University of Vic – Central University of Catalonia, where he was the Director (2010-2012). He was Visiting Research/Scientist with the GIPSA Lab. in Grenoble (France), the Lab. for Advanced Brain Signal Processing, BSI-RIKEN in Wako (Japan) and the Tensor Learning Team, at the RIKEN Center for Advanced Intelligence Project (AIP), Tokyo (Japan). Currently he continues the relationships with these laboratories. His research interests include signal processing specially in the biomedical field (EEG, fMRI, speech, handwritten, biometric applications), machine learning/deep learning and statistical modelling for applied sciences.
Abstract
In this talk, I will take the audience on a journey through my 25-year research career, spanning multiple continents, disciplines, and academic roles. Starting in Barcelona (Catalonia), my path led to international collaborations in Grenoble (France), Tokyo (Japan), Cambridge (UK), Paris (France), Tianjin (China), and beyond, exploring the boundaries between theory and application in biomedical signal processing. My early research focused on Blind Source Separation (BSS), Independent Component Analysis (ICA), and their applications to brain signals (EEG, fMRI). Over time, I expanded this work to include indirect measurements of brain activity using speech, handwriting, and gait, while collaborating with leading institutions such as the RIKEN Brain Science Institute (Japan), the Brain Mapping Unit at Cambridge (UK), and Juntendo University (Japan). Currently, my research focuses mainly on biomedical signal processing, including EEG, brain-computer interfaces for neurorehabilitation and identification of neurodegenerative disease markers through voice and handwriting analysis. I will also share recent initiatives in mental health research with companies such as TopDoctors. Combining teaching and research has been a fundamental part of my career. I will discuss strategies for managing both roles, from managing heavy teaching responsibilities to maintaining an active research agenda. Finally, I will outline future directions for my work and the mentoring of new doctoral students. This talk reflects the crossing of multiple boundaries - geographical, disciplinary and academic - as I share how my career has bridged the gap between theoretical research and practical applications.
Analysis CNVs in Cancer and Normal Tissues with Transcriptomics Data
Peter Kharchenko
Altos Labs San Diego Institute of Science
United States
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Brief Bio
Dr. Peter Kharchenko is a Principal Investigator at the Altos Labs San Diego Institute of Science.
Prior to joining Altos, Peter was a Gilbert S. Ommen Associate Professor of Biomedical Informatics at Harvard Medical School. His group has developed key methods for genomic analysis of single cells, enabling statistical separation of distinct cellular states, detection of genomic aberrations in transcriptional data, and inference of cellular dynamics from snapshots of cellular state. His group has also applied these approaches to study the organization of different tissues and the impact of diseases ranging from cancer to schizophrenia. At Altos, Peter’s group studies how cells coordinate their activity within complex biological tissues, how these mechanisms break down in the context of aging or disease, and investigate the potential interventions that may improve tissue function. Much of the effort is focused on development and application of novel statistical methods and computational tools for understanding tissue function, including analysis of multi-omics and spatial assays.
Abstract
Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. However, their relationship and relative contribution to tumor evolution and therapy resistance are not well-understood. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and non-genetic sources of tumor heterogeneity in a single assay. I will describe how incorporation of genetic information about the haplotype configuration of an individual based on population genetics can yield notable improvements in the ability to detect chromosomal aberrations (CAs) in scRNA-seq data. Such an approach is better able to resolve subclonal populations, helping to delineate contributions of genetic and non-genetic mechanisms in cancer. Somatic mutations also accumulate in normal tissues with age. Although widespread somatic mosaicism in the form of single-nucleotide variants has been well characterized, the extent and consequences of aneuploidy in healthy tissues remain largely unknown. Extending sensitivity and specificity of our CA detection approach, we carried a large-scale survey of mosaic chromosomal alterations (mCAs) in the Genotype-Tissue Expression (GTEx) project. We show that prevalence and genome-wide patterns of mCAs vary considerably across tissue types, suggesting tissue-specific mutagenic exposure and selection pressures.
Data-and Knowledge-driven Medical Image Computing and its Contribution to Precision Medicine
Jun Xu
Nanjing Univ. of Info. Sci.
China
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Brief Bio
Jun Xu is the Director of Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC) and the Vice Dean of the School of Future Technology at Nanjing University of Information Science and Technology, China. He had been a postdoctoral scientist and visiting professor at the Department of Biomedical Engineering at Rutgers University and Case Western Reserve University in the United States, respectively. He and his iMIC research group are developing and applying novel quantitative image analysis, natural language processing, signal processing, and machine learning tools for disease prevention, diagnosis, and prognosis in the context of breast, liver, colorectal, prostate, brain disease, and ophthalmology. Our group is also exploring the utility of these methods in studying correlations of disease markers across multiple scales and modalities-- from digital pathology to X-ray, CT, and multi-parametric MR images, electronic health records, and biosignals.
Abstract
Radiological images and conventional histopathological tissue sections contain valuable clinical information for prevention, diagnosis, treatment, and prognosis. With the development of image analysis and machine learning techniques, we can extract image phenotypes or image biomarkers of diseases from radiological images and histopathological sections. These image biomarkers can not only help doctors detect and diagnose diseases more accurately from histological images but also attempt to predict patients' recurrence risk, disease invasiveness, patient survival rate, and patient response to treatment. At the Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC), our team is devoted to exploring advanced data- and knowledge-driven tools for extracting and analyzing "image biomarkers" from radiological images and digital tissue samples. In this talk, I will introduce our group's recent work in the field of medical image computing, including 1) prediction of microvascular invasion of hepatocellular carcinoma based on liver CT images; 2) quantitative analysis and survival prediction of tumor microenvironment in patients with cholangiocarcinoma; 3) single-cell morphology and topological maps reveal the diversity of breast cancer ecosystem.