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Keynote Speeches
Computational Psychophysiology Based Research Methodology for Mental
Health
Abstract
Computational psychophysiology
is a new direction that broadens the field of
psychophysiology by allowing for the identification and integration of
multimodal signals to test specific models of mental states and
psychological processes. Additionally, such approaches allows for the
extraction of multiple signals from large-scale multidimensional data, with
a greater ability to differentiate signals embedded in background noise.
Further, these approaches allows for a better understanding of the complex
psychophysiological processes underlying brain disorders such as autism
spectrum disorder, depression, and anxiety. Given the widely acknowledged
limitations of psychiatric nosology and the limited treatment options
available, new computational models may provide the basis for a
multidimensional diagnostic system and potentially new treatment approaches.
Biography
Dr. Bin Hu is currently professor, dean in the School of Information Science
and Engineering, Lanzhou University, adjunct professor in Tsinghua
University, P. R. China. He is also IET Fellow, Chair of IEEE SMC TC on
Computational Psychophysiology, and Chair of ACM China SIGBio, Vice
President of International Society for Social Neuroscience (China committee)
etc. His research interest includes computational theories and pervasive
technologies in cognitive science and psychophysiology. His work has been
funded as a PI by the Ministry of Science and Technology(973 project),
National Science Foundation China, European Framework Programme 7, EPSRC,
and HEFCE UK, etc, also, published more than 200 papers in peer reviewed
journals, conferences, and book chapters.
Whole genome sequencing of disease animal models
Abstract
Whole genome sequencing of disease animal models together with
population genetics methodology is a powerful technology for deciphering new
variations which associated with disease phenotypes. Here we show our
studies on camel, dog, and rabbit. Whole genome sequencing data were
generated from those animals, and then population genetics methodology was
used in dealing with these whole genome sequencing date. Some important
genetic variations were discovered which shown a strong associations with
disease related phenotypes.
Biography
Yi-Xue
Li was born in Xinjiang, China. Currently, he is a Professor and Director of
Big Bio-Medical Data Center in CAS-MPG Partner Institute of computational
Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of
Sciences, aDirector of Department of Bioinformatics and Biostatistics,
Shanghai Jiaotong University, Director of Shanghai Center for Bioinformation
Technology. Dr. Li received his BSc. and Msc. degrees in theoretical physics
from Xinjiang University, China, in 1982 and 1987, respectively, and his
Ph.D. degree in theoretical physics from Heidelberg University, Germany in
1996. After Dr. Li got his Ph.D. degree he worked as a bioinformatics
research staff in Eusropean Molecular Biology Laboratory (EMBL) from
1997-2000, and came back to Shanghai, China in the middle of 2000.
Dr. Li has published more than 260 peer review journal papers in
various international scientific journals, such as Science, Nature, Nature
Genetics, Nature Biotechnology, Nature Communications, Lancet, Genome
Research, PNAS, Bioinformatics, Diabetes, Nucleic Acids Res, Plos
Computational Biology, Molecular Systems Biology, Molecular Biology &
Evolution, Molecular Cellular Proteomics, Oncogene, Genome Biology, Journal
of Molecular Cell Biology, etc., and his research results have been cited by
more than 30000 researchers worldwide in books, theses, journal and
conference papers, reach the H index 66 (by Google). He has served as a
reviewer/panelist for many national research foundations/agencies such as
the Chinese National Science Foundation, the National High-Tech Program(863)
and National Key Basic Research Program(973). Dr. Li has served as an
editorial board member for 5 scientific journals. He has organized several
international conferences and workshops and has also served as a program
committee member for several major national and international conferences
like GIW, IBW, HUPO and National Bioinformatics Conference etc.
Information and Decision-Making in Dynamic Cell Signalling
Abstract
I will discuss a new theoretical approach to information and decisions
in signalling systems and relate this to new experimental results about the
NF-kappaB signalling system. NF-kappaB is an exemplar system that controls
inflammation and in different contexts has varying effects on cell death and
cell division. It is activated by various stress stimuli, including
inflammatory cytokines such as TNFalpha and IL-1beta and is regarded as one
of the most important stress response pathways in the mammalian cell. In a
variety of conditions it displays oscillatory dynamics when stimulated, with
the transcription factor entering the nucleus in a pulsatile fashion with a
period of roughly 100 minutes. It is commonly claimed that it is information
processing hub, taking in signals about the infection and stress status of
the tissue environment and as a consequence of the oscillations,
transmitting higher amounts of information to the hundreds of genes it
controls. My aim is to develop a conceptual and mathematical framework to
enable a rigorous quantifiable discussion of information in this context in
order to follow Francis Crick's counsel that it is better in biology to
follow the flow of information than those of matter or energy. In my
approach the value of the information in the signalling system is defined by
how well it can be used to make the "correct decisions" when those
"decisions" are made by molecular networks. As part of this I will introduce
a new mathematical method for the analysis and simulation of large
stochastic non-linear oscillating systems. This allows an analytic analysis
of the stochastic relationship between input and response and shows that for
tightly-coupled systems like those based on current models for signalling
systems, clocks, and the cell cycle this relationship is highly constrained
and non-generic.
Biography
Dr.
David Rand’s earlier research work in nonlinear dynamics was distinguished
by its breadth and the fact that he made lasting contributions not only to
pure and applied dynamical systems, but also to theoretical physics, fluid
dynamics, and ecology and epidemiology. His current work is on the interface
between mathematics and systems biology where he has developed substantial
collaborations with a number of leading experimental biologists in what are
examples of the approach advocated for systems biology in areas such as
inflammation, immunology, circadian biology, cancer, endocrinology, and gene
regulation. In parallel, in collaboration with Bärbel Finkenstädt and others
he has developed new statistical techniques and mathematical tools for the
analysis of the sort of biological systems models and data found in these
biological areas. He has extensive management and administrative experience
(in particular as a head of Warwick’s Mathematics Institute and its Systems
Biology Centre). His prizes and distinctions include the LMS Whitehead
prize, the UK’s top prize for mathematicians under 40, and a prestigious
EPSRC Senior Research Fellowship.
Trajectory Analysis -- Linking Genomic and Proteomic Data with Disease
Progression
Abstract
Biological networks are dynamic and modular. Identifying dynamic functional
modules is key to elucidating biological insight and disease mechanism. In
recent years, while most researchers have focused on detecting functional
modules from static protein-protein interaction (PPI) networks where the
networks are treated as static graphs derived from aggregated data across
all available experiments or from a single snapshot at a particular time,
temporal nature of context-specific transcriptomic and proteomic data has
been recognized by researchers. Meanwhile, the analysis of dynamic networks
has been a hot topic in data mining and social networks. Dynamic networks
are structures with objects and links between the objects that vary in time.
Temporary information in dynamic networks can be used to reveal many
important phenomena such as bursts of activities in social networks and
evolution of functional modules in protein interaction networks. In this
talk, I will address several critical challenges to construct robust,
dynamic gene interaction networks, and present our computational approaches
to identify disease-relevant functional modules and to track the progression
patterns of modules in dynamic biological networks. Significant modules
which are correlated to phenotypes of interest can be identified, for
example, those functional modules which form and progress across different
stages of a cancer. Through identifying these functional modules in the
progression process, we are able to detect the critical groups of proteins
that are responsible for the transition of different cancer stages. Our
approaches can also discover how the strength of each detected modules
changes over the entire observation period. I will also demonstrate the
application of our approach in a variety of biomedical applications.
Biography
Dr.
Aidong Zhang is currently on leave from the State University of New York
(SUNY) at Buffalo and serving as a program director in the Information &
Intelligent Systems division of the Directorate for Computer & Information
Science & Engineering, National Science Foundation, USA. Dr. Zhang is a SUNY
Distinguished Professor of Computer Science and Engineering. Her research
interests include data mining/data science, bioinformatics, health
Informatics, multimedia and database systems, and content-based image
retrieval. She has authored over 290 research publications in these areas.
She has chaired or served on over 160 program committees of international
conferences and workshops, and currently serves on several journal editorial
boards. She has published two books “Protein Interaction Networks:
Computational Analysis” (Cambridge University Press, 2009) and “Advanced
Analysis of Gene Expression Microarray Data” (World Scientific Publishing
Co., Inc. 2006). Dr. Zhang is an IEEE Fellow.
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Invited Talks
Invited Talk
1: Deep-Learning: Investigating Feed-Forward Deep Neural Networks for
Modeling High Throughput Chemical Bioactivity Data
Speaker:
Dr. Jun (Luke) Huan
Abstract:
In recent years, research in Artificial Neural Networks (ANNs) has resurged,
now under the Deep-Learning umbrella, and grown extremely popular due to
major breakthroughs in methodological and computing capabilities.
Deep-Learning methods are part of representation-learning algorithms that
attempt to extract and organize discriminative information from the data.
Recently reported success of DL techniques in crowd-sourced QSARs and
predictive toxicology competitions has showcased these methods as powerful
tools for drug-discovery and toxicology research. Nevertheless, reported
applications of Deep Learning techniques for modeling complex bioactivity
data for small molecules remain still limited.
In this talk I will
present our recent work on optimizing feed-forward Deep Neural Nets (DNNs)
hyper-parameters and performance evaluation of these methods as compared to
shallow methods. In our study 48 DNNs, 24 Random Forest, 20 SVM and 6 Naïve
Bayes arbitrary but reasonably selected configurations were compared
employing 7 diverse bioactivity datasets assembled from ChEMBL repository
combined with circular fingerprints as molecular descriptors. The
non-parametric Wilcoxon paired singed-rank test was employed to compare the
performance of DNN to RF, SVM and NB. Overall it was found that DNNs with 2
hidden layers, 2,000 neurons per each hidden layer, ReLU activation function
and Dropout regularization technique achieved strong classification
performance across all tested datasets. Our results demonstrate that DNNs
are powerful modeling techniques for modeling complex bioactivity data.
Short Bio:
Dr.
Jun (Luke) Huan is a Professor in the Department of Electrical Engineering and
Computer Science at the University of Kansas. He directs the Data Science and
Computational Life Sciences Laboratory at KU Information and Telecommunication
Technology Center (ITTC). He holds courtesy appointments at the KU
Bioinformatics Center, the KU Bioengineering Program, and a visiting
professorship from GlaxoSmithKline plc. Dr. Huan received his Ph.D. in
Computer Science from the University of North Carolina. Dr. Huan works on data
science, machine learning, data mining, big data, and interdisciplinary topics
including bioinformatics. He has published more than 100 peer-reviewed papers
in leading conferences and journals and has graduated more than ten graduate
students including seven PhDs. Dr. Huan serves the editorial board of several
international journals including the Springer Journal of Big Data, Elsevier
Journal of Big Data Research, and the International Journal of Data Mining and
Bioinformatics. He regularly serves the program committee of top-tier
international conferences on machine learning, data mining, big data, and
bioinformatics. Dr. Huan's research is recognized internationally. He was a
recipient of the prestigious National Science Foundation Faculty Early Career
Development Award in 2009. His group won the Best Student Paper Award at the
IEEE International Conference on Data Mining in 2011 and the Best Paper Award
(runner-up) at the ACM International Conference on Information and Knowledge
Management in 2009. His work appeared at mass media including Science Daily,
R&D magazine, and EurekAlert (sponsored by AAAS). Dr. Huan's research was
supported by NSF, NIH, DoD, and the University of Kansas. Starting January
2016, Dr. Huan serves as a Program Director in NSF at its Intelligent and
Information Division in the Computer and Information Science and Engineering
Directorate.
Invited Talk
2: Networks and Models for the Integrated Analysis of Multi Omics
data
Speaker:
Dr. Sun Kim
Abstract:
These days, genome-wide measurements of genetic and epigenetics events,
a.k.a omics data, are routinely produced; epigenetics is control mechanisms
of genetics events as epi- means `on’ or `upon’. As a result, a huge amount
of omics data measured from different genetic and epigenetic events are
available. For example, the amount of data at The Cancer Genome Atlas(TCGA)
alone exceeds 2.5 peta byte as of October 2016. Unfortunately, the
dimensions of omics data is huge, typically tens to hundreds or even
millions of thousands while the number of samples are limited typically a
few to thousands. Thus mining genetic and epigenetic data measured in
different phenotype conditions is a very challenging problem, that is, small
data sets on extremely high dimensions. Furthermore, all genetic and
epigenetic events are inter-related. Thus it is necessary to perform
integrated analysis of omics data sets of different types, which is even
more challenging. To address these technical challenges, the bioinformatics
community has used virtually all known network based analysis techniques,
including recently developed deep neural networks. My group has been trying
the network based integrated analysis of omics data at three different
levels. First, we have been investigating on computational methods for
associating different genetic and epigenetic events, which can be viewed as
methods for defining edges in the network. Second, we have been developing
mining sub-networks on the phenotype and time dimensions. Third, we have
recently begun to investigate on the use of deep learning techniques for the
integrated analysis of omics data. An important goal of our research is to
combine network analysis and deep learning techniques to construct models or
draw maps of cancer cells at multiple levels such as genomic mutations, gene
activation/suppressions, epigenetic events including DNA methylation,
histone modifications, and miRNA interference, biological pathways, and
finally at the whole cell level including tumor heterogeneity and clonal
evolution.
Short Bio:
Dr. Sun Kin is a professor
and the director of Bioinformatics Institute of Seoul National University. Click
here for his short CV.
Invited Talk
3: High Performance Computational Biology and Drug Design on TianHe
Supercomputers
Speaker:
Dr. Shaoliang Peng
Abstract:
Extremely powerful computers are needed to help scientists to handle high
performance computational biology and drug design problems. The world’s
largest genomics institute BGI currently generates 6 TB data each day. The
European Bioinformatics Institute (EBI) in Hinxton currently stores 20
petabytes (1 petabyte is 1015 bytes) of data and back-ups about genes,
proteins and small molecules. TianHe supercomputers can speed up
computational biology and drug design processing. In 2013, 2014, and 2015,
Tianhe-2 topped the TOP500 list of fastest supercomputers in the world. Many
well-known bioinformatics and drug design softwares (BWA, DOCK, SOAP3-dp,
SOAPdenovo, SOAPsnp etc.) are developed and running on TH-2. The talk
focuses on two main areas: 1. Drug Design: mD3DOCKxb is a largest high
throughput molecular docking platform and finishes the docking of all the
purchasable molecules (about 42 million) on earth within 24 hours.It has a
parallel efficiency of over 70% using 192,000 CPU cores and 1,368,000 MIC
cores. It gains the Gold Award of PAC 2015 (Parallel Application Challenge
Competition) and is reported by CCTV 1, ScienceNet, China Science and
Technology News, and 2015 Top 10 News of Hunan Province of China. 2. Genetic
Engineering: The “Human Whole Genome Re-sequencing Analysis Software
Pipeline” is firstly designed by applicant. The whole analyzing procedure
takes 4 hours to finish the analysis of a 300 TB dataset of whole genome
sequences from 2,000 human beings. The speedup is about 1200X.
TianHe
Supercomputers can handle 3 kinds of computational biology and drug design
problems: computation intensive, memory intensive, and communication
intensive. In future, TH-2 will be open online to all the scientists not
only in China but also all over the world.
Short Bio:
Dr. Shaoliang Peng is a professor in National
University of Defense Technology (NUDT, Changsha, China) and an adjunct
professor of BGI. He was a visiting scholar at CS Department of City
University of Hong Kong (CityU) from 2007 to 2008 and at BGI Hong Kong from
2013 to 2014. His research interests are high performance computing,
bioinformatics, virtual screening, and biology simulation. He has participated
in many keystone projects in China such as TianHe supercomputers. He gains the
Gold Award twice of PAC 2014 and 2015 (Parallel Application Challenge
Competition) and is reported by CCTV 1, ScienceNet, China Science and
Technology News, and 2015 Top 10 News of Hunan Province of China (1. Human
Whole Genome Re-sequencing Analysis Software Pipeline, 2. mD3DOCKxb: largest
high throughput molecular docking platform). He also gains the Finalist Awards
of 2015 IEEE International Scalable Computing Challenge, SCALE 2015. He has
published 3 books and over 50 papers in ISC 2015, ACM/IEEE Transactions,
Nature Communications, Cell AJHG, BMC Bioinformatics, IPDPS, and SCIENCE
CHINA. The downloading times of software are over 50000. He is Executive
Editor and Associate Editors of several international journals
(Interdisciplinary Sciences: Computational Life Sciences, IJCSE, IJHPCN, and
IJES). Moreover, he is the PI of several key projects including 973, 863 and
National Natural Science Foundation of China (NSFC).
Invited Talk
4: Multi-Omic Approaches for Liver Cancer Biomarker Discovery
Speaker:
Dr. Habtom W. Resson
Abstract:
Omic technologies offer the opportunity to characterize liver
cancer at various molecular levels. In particular, characterizing the
association of biomolecules such as metabolites and glycoproteins with liver
cancer is a promising strategy to discover clinically relevant biomarkers.
Metabolites are molecular fingerprints of what cells do at a particular
point in time; they can reveal early signs of cancers when the chances for
cure are highest. Also, the analysis of protein glycosylation is relevant to
liver pathology because of the major influence of this organ on the
homeostasis of blood glycoproteins. This talk will focus on the application
of multi-omic approaches to identify biomarkers for early detection of liver
cancer in patients with liver cirrhosis. Specifically, I will present
transcriptomic, proteomic, glycomic/ glycoproteomic, and metabolomic (TPGM)
studies we conducted by analysis of samples from HCC cases and cirrhotic
controls using multiple omic platforms such as next generation sequencing,
liquid chromatography-mass spectrometry (LC-MS), and gas chromatography-mass
spectrometry (GC-MS). In addition to candidate biomarkers discovered by
evaluating the changes in the levels of transcripts, proteins, glycans, and
metabolites between HCC cases and cirrhotic controls, I will present
network-based methods we developed for integrative analysis of multi-omic
data to identify aberrant pathways/network activities and biomarkers for
early detection of liver cancer.
Short Bio:
Dr.
Ressom is a Professor of Oncology at Georgetown University Medical Center
(GUMC). His research focuses on using multi-omic approaches for liver cancer
biomarker discovery. His laboratory collects biospecimens from human research
participants, designs workflows for multi-omic studies, and develops
computational methods for omic data analysis. Dr. Ressom is the Director of
GUMC’s Genomics and Epigenomics Shared Resource (GESR), which provides various
services including next generation sequencing, SNP genotyping, copy number
variation analysis, DNA methylation analysis, and mRNA/miRNA expression
analysis.
Invited Talk
5: Semi-Hypothesis Guided Exploratory Analysis for Biomedical Applications
Speaker:
Dr.
Chi-Ren Shyu
Abstract:
Medical research and clinical trials are often based on hypotheses that were
observed from clinical practice with noticeable evidence. Forming clinically
significant hypotheses will greatly benefit the success of clinical research
and ensure both external and internal validity of the trial. In this talk, I
will introduce a knowledge discovery approach to automatically identify
populations of subjects with commonly occurred comorbidities, genotypes, and
phenotypes that present statistically high contract between populations. To
focus on a confined set of medical problems as most of medical researchers
would like to target (hypertension and diabetes versus all chronic
diseases), this approach is able to take a set of selected attributes of
interest and expand knowledge discoveries from the initial set. The
computational approach consists of a forward floating search method for
population selection, a hierarchical frequent pattern mining tree to
efficiently handle dense associations, contrast mining for identifying
actionable plans, and accumulated contrast (ac-)index for ranking mining
results for biomedical researchers. I will present exploratory analysis
process and results from the Simon’s Simplex Collection (SSC) by the Simons
Foundation Autism Research Initiative (SFARI) which comprises data
representing 11,560 individuals from 2,591 families. Putative autism
subtypes were explored by partitioning families based on demographics and
autism phenotypes. An extended contrast mining procedure identified genetic
combinations showing preferential association for one of the contrasted
subgroups, emphasizing combinations novel to the autistic proband within
each family tree. Potentials for other biomedical applications will also be
discussed.
Short Bio:
Dr.
Chi-Ren Shyu is the director of the University of Missouri Informatics
Institute. He holds the Paul K. and Dianne Shumaker Endowed Professorship of
Biomedical Informatics. He received his Ph.D. in Electrical and Computer
Engineering from Purdue University. Since joining University of
Missouri-Columbia in 2000, Shyu has received several awards including the
National Science Foundation CAREER award, Engineering Faculty Research Award,
Engineering Teaching Excellence Award, the 2014 University of Missouri Faculty
Interdisciplinary Entrepreneurial Award, the 2016 UM System President’s
Leadership Award. He actively serves the international research community,
which includes organizing the IEEE HealthCom 2011 conference in Columbia as
general chair, co-chairing technical program committee of the Second IEEE
BigMM2016 and IEEE BIBE2016. He will serve as the general chair for the IEEE
BIBM 2017 in Kansas City, Missouri, USA. Dr. Shyu also leads an
interdisciplinary team of 22 researchers from veterinary medicine,
engineering, human medicine to train doctoral students through the NIH BD2K’s
T32 Biomedical Big Data Science program (2016-2021) to tackle One Health Big
Data challenges – translating discoveries from animal model to human health.
His research interests include massive data analytics, biomedical informatics,
mHealth and eHealth, visual knowledge reasoning and search engine design.
Project sponsors, in addition to the NSF, include the National Institutes of
Health, National Library of Medicine, the U.S. Department of Education and
other for-profit and nonprofit organizations.
Invited Talk 6: Computational tools for studying gene regulation in the
3-dimensional genome
Speaker: Dr. Kai Tan
Abstract:
Determining the 3-dimensional structure of the genome and its impact on gene
expression has been a long-standing question in cell biology. Recent
development in mapping technologies for chromatin interactions has led to a
rapid increase in this kind of interaction data, revealing a hierarchical
organization of the 3D genome, from large compartments spanning multiple
chromosomes, to mega-base-sized topological associated chromatin domains, to
individual long-range chromatin loops mediating enhancer-promoter
interactions. With the explosion of chromatin interaction data, there is a
pressing need for analytical tools. In this talk, I will describe two
computational algorithms for analyzing chromatin interaction data at
different scales. I will first present a fast algorithm for identifying
large-scale, hierarchical chromatin domains. I will demonstrate how the
algorithm enables studies of chromatin subdomains in gene regulation.
Accurate knowledge of enhancer-promoter interactions is a pre-requisite to
understanding regulatory output of enhancers. I will present an algorithm
for predicting enhancer-promoter interactions by integrating genomic,
transcriptomic, and epigenomic data. Using data from multiple human cell
types, I will demonstrate how the algorithm can help decipher the mechanisms
underlying enhancer-promoter communication.
Short Bio:
Dr.
Kai Tan is an associate professor in the Departments of Pediatrics, Genetics,
Cell and Developmental Biology at the University of Pennsylvania and
Children’s Hospital of Philadelphia. He received his PhD degree in
computational biology from Washington University in Saint Louis, followed by
postdoctoral training in systems biology at the University of California San
Diego. Dr. Tan’s research focuses on understanding gene regulatory networks in
normal and disease development. His laboratory has developed a number of
algorithms for modeling and analyzing gene regulatory networks. He serves on
the editorial board of PLos Computational Biology and BMC Genomics. He is a
member of Genomics, Computational Biology and Technology study section of NIH.
He has served on the organization and program committees of international
conferences including BIBM, ISMB, and RECOMB.
Invited Talk 7:
Clinical application of precision medicine: Zhongshan Hospital Strategy
Speaker: Dr. Xiangdong
Wang
Abstract:
Tomorrow’s genome medicine in lung cancer should focus more on the
homogeneity and heterogeneity of lung cancer which play an important role in
the development of drug resistance, genetic complexity, as well as confusion
and difficulty of early diagnosis and therapy. Chromosome positioning and
repositioning may contribute to the sensitivity of lung cancer cells to
therapy, the heterogeneity associated with drug resistance, and the
mechanism of lung carcinogenesis. The CCCTC-binding factor plays critical
roles in genome topology and function, increased risk of carcinogenicity,
and potential of lung cancer-specific mediations. Chromosome reposition in
lung cancer can be regulated by CCCTC binding factor. Single-cell gene
sequencing, as part of genome medicine, was paid special attention in lung
cancer to understand mechanical phenotypes, single-cell biology,
heterogeneity, and chromosome positioning and function of single lung cancer
cells. We at first propose to develop an intelligent single-cell robot of
human cells to integrate together systems information of molecules, genes,
proteins, organelles, membranes, architectures, signals, and functions. It
can be a powerful automatic system to assist clinicians in the
decision-making, molecular understanding, risk analyzing, and prognosis
predicting.
Short Bio:
Xiangdong
Wang, MD, PhD, is a
Distinguished
Professor of
Medicine
at Fudan University,
Director of Shanghai Institute of Clinical Bioinformatics, Executive
Director of Clinical Science Institute of
Fudan University Zhongshan Hospital,
Director of Fudan University Center of Clinical Bioinformatics, Deputy
Director of Shanghai Respiratory Research Institute, and visiting professor
of King’s College of London. His main
research is focused on clinical bioinformatics,
disease-specific biomarkers, lung chronic
diseases, cancer immunology, and molecular & cellular therapies.
In addition,
Dr Wang serves
as the Executive Vice President of International Society for Translational
Medicine, Chairman of Executive Committee of International Society for
Translational Medicine, Deputy President of Chinese National Professional
Society of Insurance & Health and a senior
advisor of Chinese Medical Doctor Association, and Director of National
Program of Doctor-Pharmaceutist Communication. Dr Wang was appointed as the
Principal Scientist,
Global Disease Advisor, Medical Monitor and
Director, and Chairman of Director Board in a number of pharmaceutical
companies, e.g. Astra
Draco, AstraZeneca, PPT, and CatheWill. He
was the professor of Molecular Bioscience at North Carolina State University,
professor of Clinical Bioinformatics at Lund University, and
the active member of American Thoracic Society
International Health Committee, USA.
He serves as an Editor-in-Chief of Cell Biology and Toxicology (IF=2.84) and
co-Editor-in-Chief of Clinical &
Translational Medicine; Editor of
Serial Book: Translational Bioinformatics; Asian Editor of
Journal of Cellular Molecular
Medicine (IF=4.99); Section Editor of Disease Biomarkers of
Journal of Translational Medicine
(IF=3.68); Associate Editor of
Expert Review of Clinical Pharmacology (IF=2.48);
and the editorial member of international journals, e.g.
American Journal of Pulmonary
Critical Care Medicine
(IF=13),
American Journal of Cellular &
Molecular Biology
(IF=5).
He is the author of more than 200 scientific publications with the impact
factor about 600, citation number about 5372, h-index 41, i10-index 138, and
cited journal impact factor about 5000.
Invited Talk
8: An
Algorithmic-Information Calculus for Reprogramming Biological Networks
Speaker:
Dr.
Hector Zenil
Abstract:
Despite extensive attempts to characterize systems and networks based upon
metrics drawn from traditional statistics, Shannon entropy, and graph theory
to understand systems and networks to reveal their causal mechanisms without
making too many unjustified assumptions remains still as one of the greatest
challenges in complexity science and science in general, specially beyond
traditional statistics and so-called machine learning. Knowing the causal
mechanisms that govern a system allows not only the prediction of the
system’s behaviour but the manipulation and controlled reprogramming of the
system. Here we introduce a formal interventional calculus based upon
universal principles drawn from the theory of computability and algorithmic
probability, thereby enabling better approaches to the question of causal
discovery. By performing sequences of fully controlled perturbations,
changes in the algorithmic content of a system can be classified into the
effects they have according to their shift towards or away from algorithmic
randomness, thereby inducing a ranking of system’s elements. This spectral
dimension unmasks an algorithmic separation between components conditioned
upon the perturbations and endowing us with a suite of powerful
parameter-free algorithms to reprogram the system’s underlying program. The
predictive and explanatory power of these novel conceptual tools are
introduced and numerical experiments are illustrated on various types of
networks. We show how the algorithmic content of a network is connected to
its possible dynamics and how the instant variation of the sensitivity,
depth, and the number of attractors in a network is accessible by an
analysis of its algorithmic information landscape. The results demonstrate
how to unveil causal mechanisms to infer essential properties, including the
dynamics of evolving networks. We introduce measures and methods for system
reprogrammability even with no, or limited, access to the system kinetic
equations or probability distributions. We expect this interventional
calculus to be broadly applicable for predictive causal interventions and we
anticipate it to be instrumental in the challenge of causality discovery in
science from complex data.
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