The explosion in mechanistic understanding of human physiology in health and disease, exemplified by the Human Genome Project and its successors, has provided a deluge of potential new targets for therapeutic development.  At the same time, evolution of technologies and operational systems for drug discovery has allowed investigators and institutions in the public sector to contribute directly to new therapeutics discovery in a more vigorous way, particularly for rare and neglected diseases.  Over the last decade, the NIH has built a variety of programs which complement drug discovery efforts in the biopharmaceutical sector, principally in two areas: (a) science, technology, tool, and paradigm development to improve scientific understanding and efficiency of the therapeutics discovery process, and (b) early stage drug development programs to de-risk projects particularly for rare and neglected diseases, making them more amenable to biopharmaceutical adoption despite their low expected return on investment.  Most recently, these preclinical programs have been consolidated with other clinical and rare disease efforts into the new National Center for Advancing Translational Sciences at NIH. The mission, accomplishments, and plans of NCATS will be discussed.

NIH Translational Therapeutics Development – FLYER

Dr. Mitsunori Ogihara

April 2012

Overview – Dr. Mitsunori Ogihara is Director of the Data Mining Group in the Center for Computational Science.  He holds the rank of Professor of Computer Science in the College of Arts and Sciences and is a Professor of Electric and Computer Engineering in the College of Engineering.  On January 1, 2012, he began serving as Associate Dean for Digital Library Innovations in the College of Arts and Sciences, a position supported jointly by the Richter Library and the College of Arts and Science.  Prior to joining the University of Miami, he  served as chair of Computer Science at the University of Rochester for eight years.

Dr. Ogihara holds a PhD in Information Sciences from Tokyo Institute of Technology.  His PhD thesis was a theoretical study of computational complexity classes in relation to sets of low density (sparse and tally sets).  In his PhD work, he obtained the definite answer to a decade-long open question about sparse sets derived from the Berman-Hartmanis Conjecture.  Since then he has published more than 100 papers in theoretical computer science.  Currently, he serves on the editorial board for two journals that cover foundational research: Theory of Computing Systems and International Journal of Foundations of Computer Science.

While continuing work on theoretical research, he developed interests in scholarly inquiries that require computation as tools for discovery and has begun publishing in such areas as data mining, bioinformatics, and music information retrieval.   In data mining Dr. Ogihara is interested in developing techniques for high-dimensional data, in particular, those whose attributes are discrete.  In his recent work he developed algorithms for estimating entropy of network flows with respect to the frequencies of IP packet addresses (in collaboration with groups from Georgia Tech., ATT, and Denison University).  A key issue in designing such network traffic monitoring algorithms is that there is not enough space to record all the packets.  The entropy estimation algorithms use sampling packets to obtain a summary that on average gives a close-to-reality representation of the packet distribution.

Research Interests

Document Analysis

Another area of interest in his data mining research is document analysis. Summarizing texts into a few keywords or to a sentence is an important topic where a large volume of poorly annotated texts is being dealt with.

In a recent paper with Zhang et al., he used a parsing technique (a method for computationally identifying parts of speech in given sentences) to identify writer’s sentiment (positive or negative feelings toward the subject of writing) in the collection of technical columns that appeared in The Wall Street Journals. (Fig. 1)

Fig. 1 A parsing example in sentiment analysis

High Dimensional Biological Data

In bioinformatics, Dr. Ogihara’s research has been on algorithms for high dimensional biological data.  His group in the CCS is currently developing methods for estimating abundance of transcriptomes from high-throughput sequencing data using fast solutions to the Least Squares Problem.

Fig. 2 Soft clustering of activation profiles

Also, with Vineet Gupta (Department of Medicine at UM) and a former CCS scientist,Qiong Cheng (University of Illinois), he is developing methods for inferring dynamics of protein-protein interaction through integration of interaction data (Y2H data, for example) and activation data.  In this study an input network is examined to identify sets of interactions that are likely to occur at different times and then using that information activation patterns are grouped into clusters. (Fig. 2)

Music Information Retrieval

In music information retrieval, he is interested in integration of various types of music data (metadata, lyrics, acoustic data, listening patterns, and user social networks) for classification and recommendation.  In a recent paper with a graduate student Yajie Hu he studied the question of modeling transitions from one genre to another in music listening behaviors using freshness in listeners’ memories that decay exponentially in time so as to make meaningful recommendations for the “next piece” to listen to.  Also, with a CCS scientist Dingding Wang he studied the problem of exploring “Twitter follower” networks to improve automatic genre/style classification of artists.  His recent book with Tao Li (Florida International University) and George Tzanetakis (University of British Columbia) “Music Data Mining” surveys a variety of problems and techniques that appear in large-scale music data analysis.

Each directorate may also have specific guidelines that address unique data management issues within the respective community.  Be sure to look at the proposal details and main directorate and/or division website for addition guidelines.

Free Help with Your Data Management Plan

To ensure that all NSF proposals emerging of the University of Miami meet the data management plan requirements, researchers are encouraged to consult with staff in the University of Miami’s Center for Computational Science (CCS).

To create your Data Management Plan, download the NSF_Data_Management_Plan Template and follow the instructions provided on page 1.   Upon completion of this exercise, you will have achieved a Data Management Plan.

The HPC team at the CCS will review  your Data Management Plan, make any suggestions or recommendations, and/or help answer questions on items which you may be uncertain. To have your Data Management Plan reviewed by the HPC team at CCS, you may forward your document to hpc@ccs.miami.edu.  Also, please  include contact information, program announcement, and proposal deadline.

Massive changes of expression of hundreds of genes as well as changes in genomic and epigenomic landscapes observed in human diseases often represent just an “echo” of relatively few causative molecular processes in the cells taking place during the transformation into the disease state, e.g. in cancer, during malignant transformation. Non-reversible structural changes in gene regulatory networks may cause transformation of the cell homeostasis switching it from the normal state to the disease state. We call such structural network changes as “walking pathways”. Analysis of this phenomenon helps us to understand the mechanisms of molecular switches (e.g. between programs of cell death and programs of cell survival) and to identify perspective biomarkers and drug targets of cancer.

In order to automatize the causal analysis of high throughput data we have developed a geneXplain™ platform (www.genexplain.com) – an integrated systems biology platform. GeneXplain™ applies a unique upstream analysis approach based on implementation of machine learning and graph topological analysis algorithms in order to identify causality keynodes in the network of gene regulation and signal transduction and combines it with full genome sequence analysis and cheminformatics methods for drug discovery. The power of this approach is that we are trying to identify causal biomarkers – those which are more than just correlating with disease or treatment outcome but which are parts of the disease mechanism, which may differ in patient cohorts. Such personalized networks are analyzed in order to find master regulatory molecules – suggested important nodes triggering the disease. These master regulators and genes/proteins directly influenced by them are considered as promising biomarkers which can help to stratify patients according to the disease mechanism.

In the current study, we analyzed a large scale gene expression and ChIP-seq data from a study of a breast cancer samples treated with antineoplastic agents including the novel drug compounds – RITA and Nutlin, targeting p53 and Mdm2. We analyzed promoters of downregulated pro-survival genes and identified combinations of transcription factors involved in their regulation. Topological modeling of the signal transduction network upstream of these transcription factors revealed key-nodes – potent master-regulators of the cell survival program that prevent efficient apoptosis of cancer cells. We considered these key-node proteins (e.g. PI3K subunits) as causal biomarkers as well as prospective targets for novel anticancer drug combinations. We applied a cheminformatics computer tool PASS to these targets and identified two novel prospective antineoplastic chemical compounds which were experimentally validated in a cellular assay confirming their synergistic potential in highly selective triggering of apoptosis of cancer cells.

GeneXplain – Flyers

www.youtube.com/watch?v=JgUNBRJHrC4

The CCS Resources Seminar offers an overview on the expertise and computational resources available through the Center’s high performance computing (HPC) core as well as an overview of the research and development capabilities of the software engineering team. The seminar is ideal for faculty seeking information on how to utilize these resources on existing and future research projects.
The seminar is split into a two part session:

1. Overview of High Performance Computing Services and Resources
2. Overview of Software Engineering Services and Resources

For information on this seminar, please contact 305-243-4962 or  E-mail ccsadministration@med.miami.edu

This presentation was addressed to a specialized audience of people in Data Mining and Machine Learning. The talk  provided a theory that showed how clustering stability can be used to choose the correct number of clusters, as well as demonstrate the importance of cluster stability and discussed the use of stability for clustering evaluation. B. Clarke (UM), H. Koepke (UM), and U. Washington (UM)

BIRS 2011 PowerPoint Slides

This presentation was aimed at a general statistical audience and showed the importance of cluster stability, a theorem that in the limit of large dimensions can get complete noninformativity of clustering for finite sample sizes, and discussed the use of stability for clustering evaluation. B. Clarke (UM), H. Koepke (UM), and U. Washington (UM)

FSU 2011 PowerPoint Slides

This paper modeled music listeners listening patterns using a “forgetting” factor — a slow exponential decrease in the freshness of music that was listened to.  The effectiveness of the model was confirmed using a simple music jukebox program that makes a recommendation for the next piece to listen to and allows the listener to “skip” to the next song. Y. Hu and M. Ogihara
ISMIR-II-Poster

This paper studied the use of the “follower” information in Twitter to improve computer-generated music style and artist clusters.  A graph-based representation is used in mathematically characterizing the follower relations. D. Wang and M. Ogihara

ISMIR-Poster