MIB is excited to host Professor Asoke Nandi from Brunel University who will give a talk on consensus clustering paradigms.

12.12.2017 | Hella Kastbjerg

TITLE:

Consensus Clustering Paradigms and Findings from fMRI Data

ABSTRACT:

Clustering techniques have been developed and applied in many areas for several decades. In particular, they have been used for gene clustering over the last two or three decades in bioinformatics and 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 selection of heterogeneous datasets on similar experiments. The challenge is how to reach consensus conclusions in such scenarios.

This presentation will address both of the aforementioned issues as well as report on the results from applying Bi-CoPaM and UNCLES recently to analyse fMRI image data when listening to music. Most of this presentation will come from the following four papers published in four journals.

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. 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.

3. 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.

4. 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.

Although this presentation will include examples from brain signal processing, these ideas be applied to all applications areas involving clustering.

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 the areas of signal processing and machine learning, with applications to functional magnetic resonance data, gene expression data, communications, and biomedical data. He has made many 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 obtained in different laboratories and different times. 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). Some of the recent journals he published in are Frontiers in Human Neuroscience, Blood, BMC Bioinformatics, IEEE TWC, NeuroImage, PLOS ONE, Royal Society Interface, BMC Genomics, Mechanical Systems and Signal Processing, Molecular Cancer, and International Journal of Neural Systems. 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 and the IET. 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, Division Award of the Electronics and Communications Division, of the Institution of Electrical Engineers (UK) in 1998.