学术报告题目：Information Extraction from Large Datasets Consensus Clustering Paradigm

报告时间地点：2018年4月2日，上午10:00-12:00，北五楼319

报告人： Professor Asoke K. Nandi PhD (Cambridge) , Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UB8 3PH, United Kingdom

Abstract:

Clustering algorithms are often used to extract information from large datasets. They represent model-free or data-driven approaches. They have been developed and applied in many areas for several decades. In particular, they have been used for gene clustering over the last two decades in bioinformatics and in brain signal processing. New algorithms are being developed and applied to address many different problems. However, in applications with real data with little a priori knowledge, it is often difficult to select an appropriate clustering algorithm and evaluate the quality of clustering results due to the unknown ground truth. It is also the case that conclusions based on only one specific algorithm might be biased, since each algorithm has its own assumptions of the structure of the data, which might not correspond to the real data.

Another important issue relates to multiple datasets, which may have been generated either in the same laboratory or different laboratories at different times and with different settings yet trying to conduct the similar

experiments. In such a scenario, one has essentially a collection of heterogeneous datasets from similar experiments. The challenge is how to reach consensus conclusions in such scenarios. This presentation will address these issues and report on the results from applying Bi-CoPaM and UNCLES recently to analyse fMRI data and gene data. The following papers form the basis of this presentation.

1. C Liu, E Brattico, B Abu Jamous, C Pereira, T Jacobsen, and A K Nandi, “Effect of explicit evaluation on neural connectivity related to listening to unfamiliar music", Frontiers in Human Neuroscience, DOI: 10.3389/fnhum.2017.00611, vol. 11, (13 pages), 2017.

2. B Abu Jamous, F M Buffa, A L Harris, and A K Nandi, “In vitro downregulated hypoxia transcriptome is associated with poor prognosis in breast cancer", Molecular Cancer, DOI: 10.1186/s12943-017-0673-0, vol. 16, no. 105, (19 pages), 2017.

3. C Liu, B Abu Jamous, E Brattico, and A K Nandi, “Towards tunable consensus clustering for studying functional brain connectivity during affective processing", International Journal of Neural Systems, DOI: 10.1142/S0129065716500428, vol. 27, no. 2, 1650042 (16 pages), 2017.

4. A T Merryweather-Clarke et al., "Distinct gene expression program dynamics during erythropoiesis from human induced pluripotent stem cells compared with adult and cord blood progenitors", BMC Genomics, vol. 17, no. 817, DOI: 10.1186/s12864-016-3134-z (20 pages), 2016.

5. B Abu Jamous, R Fa, D J Roberts, and A K Nandi, “UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets", BMC Bioinformatics, DOI: 10.1186/s12859-015-0614-0, vol. 16, no. 184, 2015.

6. B Abu Jamous, R Fa, D J Roberts, and A K Nandi, “Comprehensive analysis of forty yeast microarray datasets reveals a novel subset of genes (APha-RiB) consistently negatively associated with ribosome biogenesis", BMC Bioinformatics, DOI: 10.1186/1471-2105-15-322, vol. 15, no. 322, 2014.

7. F Cong et al., "Low-rank approximation based non-negative multi-way array decomposition of event-related potentials", International Journal of Neural Systems, DOI: 10.1142/S012906571440005X, vol. 24, 1440005 (19 pages), 2014.

8. V Alluri et al., "From Vivaldi to Beatles and back: predicting lateralized brain responses to music", NeuroImage, vol. 83, pp. 627-636, 2013.

9. F Cong et al., "Linking brain responses to naturalistic and continuous music through analysis of ongoing EEG and stimulus features", IEEE Transactions on Multi-Media, vol. 15, no. 5, pp. 1060-1069, 2013.

10. B Abu Jamous, R Fa, D J Roberts, and A K Nandi, “Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery", PLoS ONE vol. 8, no. 2, Doi:10.1371/journal.pone.0056432, 2013.

11. B Abu Jamous, R Fa, D J Roberts, and A K Nandi, “Yeast gene CMR1/YDL156W is consistently co-expressed with genes participating in DNA-metabolic processes in a variety of stringent clustering experiments", J. R. Soc. Interface, vol. 10, no. 81, doi: 10.1098/rsif.2012.0990, 2013.

Bio-Sketch:

Professor Asoke K. Nandi received the degree of Ph.D. in Physics from the University of Cambridge (Trinity College), Cambridge (UK). He held academic positions in several universities, including Oxford (UK), Imperial College London (UK), Strathclyde (UK), and Liverpool (UK) as well as Finland Distinguished Professorship in Jyvaskyla (Finland). In 2013 he moved to Brunel University (UK), to become the Chair and Head of Electronic and Computer Engineering. Professor Nandi is a Distinguished Visiting Professor at Tongji University (China) and an Adjunct Professor at University of Calgary (Canada). In 1983 Professor Nandi co-discovered the three fundamental particles known as W+, W− and Z0 (by the UA1 team at CERN), providing the evidence for the unification of the electromagnetic and weak forces, for which the Nobel Committee for Physics in 1984 awarded the prize to two of his team leaders for their decisive contributions. His current research interests lie in signal processing and machine learning, with applications to functional magnetic resonance data, gene expression data, communications, and biomedical data. He has made fundamental theoretical and algorithmic contributions to many aspects of signal processing and machine learning. He has much expertise in “Big Data”, dealing with heterogeneous data, and extracting information from multiple datasets. Professor Nandi has authored over 550 technical publications, including 220 journal papers as well as four books, entitled Automatic Modulation Classification: Principles, Algorithms and

Applications (Wiley, 2015), Integrative Cluster Analysis in Bioinformatics (Wiley, 2015), Blind Estimation Using Higher-Order Statistics (Springer, 1999), and Automatic Modulation Recognition of Communications Signals (Springer, 1996). The h-index of his publications is 67 (Google Scholar) and ERDOS number is 2. Professor Nandi is a Fellow of the Royal Academy of Engineering and also a Fellow of seven other institutions including the IEEE. Among the many awards he received are the Institute of Electrical and Electronics Engineers (USA) Heinrich Hertz Award in 2012, the Glory of Bengal Award for his outstanding achievements in scientific research in 2010, the Water Arbitration Prize of the Institution of Mechanical Engineers (UK) in 1999, and the Mountbatten Premium of the Institution of Electrical Engineers (UK) in 1998. Professor Nandi is an IEEE Distinguished Lecturer (EMBS, 2018-2019).