This document proposes a family-based framework for quality assurance of biomedical ontologies in BioPortal. It analyzes 186 ontologies from BioPortal and categorizes them into 7 families based on their structural features. For each family, it defines a unified abstraction network and quality assurance methodology. As an example, it applies the framework to the Cancer Chemoprevention Ontology and identifies errors, which were addressed in a newer version. The framework aims to improve the scalability and efficiency of quality assurance for the large number of ontologies in BioPortal.
Provenance abstraction for implementing security: Learning Health System and ...Vasa Curcin
Discussion of provenance usage in the Learning Health System paradigm, as implemented in the TRANSFoRm project, with focus on security requirements and how they can be addressed using provenance graph abstraction.
Towards Digitally Enabled Genomic Medicine: the Patient of The FutureLarry Smarr
12.02.22
Invited Speaker
Hacking Life
TTI/Vanguard Conference
Title: Towards Digitally Enabled Genomic Medicine: the Patient of The Future
San Jose, CA
Provenance abstraction for implementing security: Learning Health System and ...Vasa Curcin
Discussion of provenance usage in the Learning Health System paradigm, as implemented in the TRANSFoRm project, with focus on security requirements and how they can be addressed using provenance graph abstraction.
Towards Digitally Enabled Genomic Medicine: the Patient of The FutureLarry Smarr
12.02.22
Invited Speaker
Hacking Life
TTI/Vanguard Conference
Title: Towards Digitally Enabled Genomic Medicine: the Patient of The Future
San Jose, CA
Application of Microarray Technology and softcomputing in cancer BiologyCSCJournals
DNA microarray technology has emerged as a boon to the scientific community in understanding the growth and development of life as well as in widening their knowledge in exploring the genetic causes of anomalies occurring in the working of the human body. microarray technology makes biologists be capable of monitoring expression of thousands of genes in a single experiment on a small chip. Extracting useful knowledge and info from these microarray has attracted the attention of many biologists and computer scientists. Knowledge engineering has revolutionalized the way in which the medical data is being looked at. Soft computing is a branch of computer science capable of analyzing complex medical data. Advances in the area of microarray –based expression analysis have led to the promise of cancer diagnosis using new molecular based approaches. Many studies and methodologies have come up which analyszes the gene espression data by using the techniques in data mining such as feature selection, classification, clustering etc. emboiding the soft computing methods for more accuracy. This review is an attempt to look at the recent advances in cancer research with DNA microarray technology , data mining and soft computing techniques.
Accelerating the benefits of genomics worldwideJoaquin Dopazo
Grand Challenges in Genomics
A Joint NHGRI and Wellcome Trust Strategic Meeting
25 and 26 February 2019
https://www.wellcomeevents.org/WELLCOME/media/uploaded/EVWELLCOME/event_661/Draft_agenda_for_WT_December_2018.pdf
Join lecture: Nicky Mulder, Han Brunner and Joaquin Dopazo
Advancing Innovation and Convergence in Cancer Research: US Federal Cancer Mo...Jerry Lee
Special Seminar at the 8th Taiwan Biosignatures Workshop to share overall work of NCI's Center for Strategic Scientific Initiatives since 2003 as well as CSSI's influence on select projects initiated by the 2016 WH Cancer Moonshot Task Force that include Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) network, International Cancer Proteogenome Consortium, and the Blood Profiling Atlas in Cancer (BloodPAC) commons.
Digital Pathology: Precision Medicine, Deep Learning and Computer Aided Inter...Joel Saltz
In this presentation, I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe methods, tools and algorithms to extract information from Pathology images. These tools include ability to traverse whole slide images, segment nuclei, carry out deep learning region classification and characterize relationship between extracted features and morphological structures. I will also describe some of the research efforts that motivate development of these tools, the role Pathomics is playing in precision medicine research as well as the impact of Pathology Informatics on clinical practice and health care quality.
Presentation at the Department of Biomedical Informatics, University Pittsburgh Medical Center, April 27, 2018
Use of Simulation- based Training for Cancer Education among Nigerian Cliniciansasclepiuspdfs
Background: Among the many limitations of cancer control in Nigeria are lower awareness/competence and poorer training of health-care professionals (HCP). These manifest as deficiencies in advocacy, screening/diagnostic practices, and patient management. Medical simulation (MS) using models is an effective approach for sustainably improving the competence of HCP, especially regarding clinical breast examination (CBE), pelvic examination (PE), and digital rectal examination (DRE). The study evaluates the effect of MS during a Nigerian training course focusing on CBE, PE, and DRE. It answers the question: What is the immediate outcome of MS-based training, as well as the perspectives of HCP on the use of MS for cancer education? Methods: Participants included a convenience sample of Nigerian physicians and nurses who attended the American Society of Clinical Oncology-sponsored Multidisciplinary Cancer Management Course. The intervention was MS using high-fidelity models. The models demonstrated normal anatomic and common pathologic features of the breast, cervical, and prostate. Participants cycled through MS stations (i.e., CBE, PE, and DRE). Pre- and post-training surveys with comments evaluating self-reported comfort levels were the basis for comparison. Data analysis included descriptive statistics, Wilcoxon signed-rank test, Chi-square, and thematic analysis. Results: A total of 51 participants completed course evaluation forms (physicians - 35 and nurses - 16), with an average number of years in practice as 8 (±5.2) years. Pre-training survey showed non-significant differences in practices patterns; 71% (22/35) of physicians rarely performed PE (P=0.92), and 93% (14/16) of nurses rarely performed DRE (P=0.07). According to some participants, “the use of simulation is quite commendable as it gives room for improvement before using a human; it is the best method of learning I have ever enjoyed.” Conclusion: MS-based training significantly improved the comfort levels of participants regarding CBE and PE, as well as their likelihood to perform CBE, PE, and DRE. Participants recommend widespread use of MS for continuing medical education and undergraduate training.
MINING OF IMPORTANT INFORMATIVE GENES AND CLASSIFIER CONSTRUCTION FOR CANCER ...ijsc
Microarray is a useful technique for measuring expression data of thousands or more of genes
simultaneously. One of challenges in classification of cancer using high-dimensional gene expression data
is to select a minimal number of relevant genes which can maximize classification accuracy. Because of the
distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and
robust gene identification methods is extremely fundamental. Many gene selection methods as well as their
corresponding classifiers have been proposed. In the proposed method, a single gene with high classdiscrimination
capability is selected and classification rules are generated for cancer based on gene
expression profiles. The method first computes importance factor of each gene of experimental cancer
dataset by counting number of linguistic terms (defined in terms of different discreet quantity) with high
class discrimination capability according to their depended degree of classes. Then initial important genes
are selected according to high importance factor of each gene and form initial reduct. Then traditional kmeans
clustering algorithm is applied on each selected gene of initial reduct and compute missclassification
errors of individual genes. The final reduct is formed by selecting most important genes with
respect to less miss-classification errors. Then a classifier is constructed based on decision rules induced
by selected important genes (single) from training dataset to classify cancerous and non-cancerous samples
of experimental test dataset. The proposed method test on four publicly available cancerous gene
expression test dataset. In most of cases, accurate classifications outcomes are obtained by just using
important (single) genes that are highly correlated with the pathogenesis cancer are identified. Also to
prove the robustness of proposed method compares the outcomes (correctly classified instances) with some
existing well known classifiers.
Application of Microarray Technology and softcomputing in cancer BiologyCSCJournals
DNA microarray technology has emerged as a boon to the scientific community in understanding the growth and development of life as well as in widening their knowledge in exploring the genetic causes of anomalies occurring in the working of the human body. microarray technology makes biologists be capable of monitoring expression of thousands of genes in a single experiment on a small chip. Extracting useful knowledge and info from these microarray has attracted the attention of many biologists and computer scientists. Knowledge engineering has revolutionalized the way in which the medical data is being looked at. Soft computing is a branch of computer science capable of analyzing complex medical data. Advances in the area of microarray –based expression analysis have led to the promise of cancer diagnosis using new molecular based approaches. Many studies and methodologies have come up which analyszes the gene espression data by using the techniques in data mining such as feature selection, classification, clustering etc. emboiding the soft computing methods for more accuracy. This review is an attempt to look at the recent advances in cancer research with DNA microarray technology , data mining and soft computing techniques.
Accelerating the benefits of genomics worldwideJoaquin Dopazo
Grand Challenges in Genomics
A Joint NHGRI and Wellcome Trust Strategic Meeting
25 and 26 February 2019
https://www.wellcomeevents.org/WELLCOME/media/uploaded/EVWELLCOME/event_661/Draft_agenda_for_WT_December_2018.pdf
Join lecture: Nicky Mulder, Han Brunner and Joaquin Dopazo
Advancing Innovation and Convergence in Cancer Research: US Federal Cancer Mo...Jerry Lee
Special Seminar at the 8th Taiwan Biosignatures Workshop to share overall work of NCI's Center for Strategic Scientific Initiatives since 2003 as well as CSSI's influence on select projects initiated by the 2016 WH Cancer Moonshot Task Force that include Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) network, International Cancer Proteogenome Consortium, and the Blood Profiling Atlas in Cancer (BloodPAC) commons.
Digital Pathology: Precision Medicine, Deep Learning and Computer Aided Inter...Joel Saltz
In this presentation, I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe methods, tools and algorithms to extract information from Pathology images. These tools include ability to traverse whole slide images, segment nuclei, carry out deep learning region classification and characterize relationship between extracted features and morphological structures. I will also describe some of the research efforts that motivate development of these tools, the role Pathomics is playing in precision medicine research as well as the impact of Pathology Informatics on clinical practice and health care quality.
Presentation at the Department of Biomedical Informatics, University Pittsburgh Medical Center, April 27, 2018
Use of Simulation- based Training for Cancer Education among Nigerian Cliniciansasclepiuspdfs
Background: Among the many limitations of cancer control in Nigeria are lower awareness/competence and poorer training of health-care professionals (HCP). These manifest as deficiencies in advocacy, screening/diagnostic practices, and patient management. Medical simulation (MS) using models is an effective approach for sustainably improving the competence of HCP, especially regarding clinical breast examination (CBE), pelvic examination (PE), and digital rectal examination (DRE). The study evaluates the effect of MS during a Nigerian training course focusing on CBE, PE, and DRE. It answers the question: What is the immediate outcome of MS-based training, as well as the perspectives of HCP on the use of MS for cancer education? Methods: Participants included a convenience sample of Nigerian physicians and nurses who attended the American Society of Clinical Oncology-sponsored Multidisciplinary Cancer Management Course. The intervention was MS using high-fidelity models. The models demonstrated normal anatomic and common pathologic features of the breast, cervical, and prostate. Participants cycled through MS stations (i.e., CBE, PE, and DRE). Pre- and post-training surveys with comments evaluating self-reported comfort levels were the basis for comparison. Data analysis included descriptive statistics, Wilcoxon signed-rank test, Chi-square, and thematic analysis. Results: A total of 51 participants completed course evaluation forms (physicians - 35 and nurses - 16), with an average number of years in practice as 8 (±5.2) years. Pre-training survey showed non-significant differences in practices patterns; 71% (22/35) of physicians rarely performed PE (P=0.92), and 93% (14/16) of nurses rarely performed DRE (P=0.07). According to some participants, “the use of simulation is quite commendable as it gives room for improvement before using a human; it is the best method of learning I have ever enjoyed.” Conclusion: MS-based training significantly improved the comfort levels of participants regarding CBE and PE, as well as their likelihood to perform CBE, PE, and DRE. Participants recommend widespread use of MS for continuing medical education and undergraduate training.
MINING OF IMPORTANT INFORMATIVE GENES AND CLASSIFIER CONSTRUCTION FOR CANCER ...ijsc
Microarray is a useful technique for measuring expression data of thousands or more of genes
simultaneously. One of challenges in classification of cancer using high-dimensional gene expression data
is to select a minimal number of relevant genes which can maximize classification accuracy. Because of the
distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and
robust gene identification methods is extremely fundamental. Many gene selection methods as well as their
corresponding classifiers have been proposed. In the proposed method, a single gene with high classdiscrimination
capability is selected and classification rules are generated for cancer based on gene
expression profiles. The method first computes importance factor of each gene of experimental cancer
dataset by counting number of linguistic terms (defined in terms of different discreet quantity) with high
class discrimination capability according to their depended degree of classes. Then initial important genes
are selected according to high importance factor of each gene and form initial reduct. Then traditional kmeans
clustering algorithm is applied on each selected gene of initial reduct and compute missclassification
errors of individual genes. The final reduct is formed by selecting most important genes with
respect to less miss-classification errors. Then a classifier is constructed based on decision rules induced
by selected important genes (single) from training dataset to classify cancerous and non-cancerous samples
of experimental test dataset. The proposed method test on four publicly available cancerous gene
expression test dataset. In most of cases, accurate classifications outcomes are obtained by just using
important (single) genes that are highly correlated with the pathogenesis cancer are identified. Also to
prove the robustness of proposed method compares the outcomes (correctly classified instances) with some
existing well known classifiers.
Presentazione Introduttiva all'Arduino Day 2015 tenutosi in Ludoteca Archimedea: "Arduino Uno nessuno e Centomila" quale e' la vera personalita' di Arduino? Scopriamo solo una piccola parte dei diversi impieghi e ambiti di potenziale applicabilita' che ha la schedina elettronica in ambito prototipazione, educational e hobby.
Sono illustrati anche i progetti svolti in collaborazione con il Politecnico di Milano Dipartimento di Design del Prodotto in cui Arduino ha reso "smart", interattive, le idee dei designer.
Using real-world evidence to investigate clinical research questionsKarin Verspoor
Adoption of electronic health records to document extensive clinical information brings with it the opportunity to utilise that information to support clinical research, and ultimately to support clinical decision making. In this talk, I discuss both these opportunities and the challenges that we face when working with real-world clinical data, and introduce some of the strategies that we are adopting to make this data more usable, and to extract more value from it. I specifically discuss the use of natural language processing to transform clinical documentation into structured data for this purpose.
The Learning Health System: Thinking and Acting Across ScalesPhilip Payne
A Learning Health System (LHS) can be defined as an environment in which knowledge generation processes are embedded into daily clinical practice in order to continually improve the quality, safety, and outcomes of healthcare delivery. While still largely an aspirational goal, the promise of the LHS is a future in which every patient encounter is an opportunity to learn and improve that patient’s care, as well as the care their family and broader community receives. The foundation for building such an LHS can and should be the Electronic Health Record (EHR), which provides the basis for the comprehensive instrumentation and measurement of clinical phenotypes, as well as a means of delivering new evidence at the patient- and population levels. In this presentation, we will explore the ways in which such EHR-derived phenotypes can be combined with complementary data across a spectrum from biomolecules to population level trends, to both generate insights and deliver such knowledge in the right time, place, and format, ultimately improving clinical outcomes and value.
Application of adverse outcome pathways in chemical risk assessment, Dan Vill...OECD Environment
On 30 April 2019, the OECD organised a webinar on the Adverse Outcome Pathway (AOP) framework. The AOP framework is a collaborative tool that applies an innovative approach for collecting mechanistic knowledge from various sources that can eventually support chemical safety assessment.
The following questions were addressed:
What is the AOP framework and why should you care?
Why are we developing AOPs?
Why collaborations are encouraged and why should scientific societies be brought in?
What are the opportunities for collaboration in AOP development?
Professor Martin Wiseman presentation on The Continuous Update Project: Novel approach to reviewing mechanistic evidence on diet, nutrition, physical activity and cancer at FENS European Nutrition Conference, 20-23 October 2015 Berlin (Germany).
Quality and safety in global surgery and healthcare conference presentationDr Edward Fitzgerald
Quality and safety in global surgery and healthcare conference presentation including Lifebox Foundation - presented at the International Student Surgical Network
Integrative Everything, Deep Learning and Streaming DataJoel Saltz
Workshop on Clusters, Clouds, and Data for Scientific Computing, September 6, 2018
The need to create to label information and segment regions in individual sensor data sources and to create synthesizes from multiple disparate data sources span many areas of science, biomedicine and technology. The rapid evolution in sensor technologies – from digital microscopes to UAVs drive requirements in this area. I will describe a variety of use cases, describe technical challenges as well as tools, algorithms and techniques developed by our group and collaborators.
18. Seven Families of Ontologies
Family Structural Condition # Ontologies Samples
1 All object properties are
instantiated
2 SNOMED CT, NCIt
2 With only domain‐defined object
properties
19 CanCo, ICF, PMR
3 With only restriction‐defined
object properties
69 GO, GRO_CPD, HPIO
4 With either domain‐defined
object properties or restriction‐
defined object properties
62 SDO, IDO
5 DAG, no object properties 9 APO, HP, OGMD
6 Tree, no object properties, with
data properties
3 CBO, CareLex
7 Tree, no object properties,
without data properties
22 OGMS, REPO, SEP
18
22. Errors Found in Canco Taxonomy
Error Suggestions Outcome
max_inhibitory_
concentration is
identical to an object
property
Remove the class
max_inhibitory_concentr
ation
Change made
in Version 0.3
Class name of
“Target” is not
appropriate
“Target”
“BiologicalTarget”
Change made
in Version 0.3
Redundant BFO
classes in Entity node
20 BFO classes should be
hidden
Future plan of
BioPortal for
hiding classes
22
35. Renaming of “Target”
• Another “large” partial area Molecule (7)
• The child of Molecule – Target should be renamed “Biological
target” according to its definition.
• A class Macromolecule should be introduced as child of Molecule.
CanCo
Version 0.2
35