Stephanie Gonthier, ’15

Project: Using statistical learning to improve word prediction for augmentative and alternative communication
Duration: Summer 2014
Funding: Bucknell University Program for Undergraduate Research, Geisinger BGRI Grant

ABSTRACT

There are a multitude of reasons why people may be unable to communicate effectively through verbal speech, including disorders like ALS, MS, Cerebral Palsy and Autism. Some people use augmentative and alternative communication (AAC), which is simply any mode of communication besides verbal speech, including gestures, writing, facial expressions, pointing to pictures and so on. In recent decades, the field of AAC has been flooded by electronic devices which generate speech for these people based on combinations of pictures, symbols and/or words that are stored on the device. Unfortunately, these devices do present problems; notably, the communication rate with a device is reduced to a fraction of the communication rate of normal speakers. The average user of a device is only able to communicate 10 words per minute, compared to the 130-200 words per minute of an average speaker [ref]. This stark contrast can leave users frustrated, reducing the utility of such devices. The aim of this research is to develop a novel algorithm that would increase the communication rate for users of AAC devices.

ACHIEVEMENTS

  • Oral Presentation – Fourth Annual Susquehanna Valley Undergraduate Research Symposium, SVURS 2014, August 5, Geisinger Research, Danville, PA
    Winner for oral presentation – One of three chosen out of 86 submissions!
  • Poster Presentation – 2014 Sigma Xi Summer Student Research Symposium, July 24, Bucknell University, Lewisburg, PA

POST GRADUATION UPDATES

Elizabeth Dwornik, ’14

Project: Named-Entity Recognition
Duration: Summer 2013 – Spring 2014
Funding: Bucknell University Program for Undergraduate Research

ABSTRACT

Liz is working on a system that can annotate all of the named entities within a text. There are good systems that can identify named entities, however, identifying the type of named entity is a more challenging problem. Many successful systems use simple database lookup techniques and identify entities from a master gazetteer. We are working on a system that can distinguish among different types of named entities without a gazetteer. Our initial efforts will focus on distinguishing entities between location, organization, or person. We plan to start by developing a large set of regular expressions that can be used to classify the different types of entities.

ACHIEVEMENTS

  • Poster: Kalman Research Symposium 2013, April 13, Bucknell University, Lewisburg, PA

POST GRADUATION UPDATES

Liz pursued graduate school studies at Carnegie Mellon University, starting Fall 2014. She enrolled in the Software Management program in the Information Networking Institute. Congratulations, Liz!

Matthew Rogge, ’17

Project: Analysis of Spike Timing Dependent Neural Networks for More Efficient Starting State Learning
Duration: Summer 2014
Funding: Bucknell University Program for Undergraduate Research

ABSTRACT

Artifical Neural Networks (ANN) have been a popular machine learning method for decades. They aim to simulate the behavior of the neurons in the biological brain. One particular type of ANN that is an especially accurate representation of biological neurons is the Spike-Timing Dependent ANN. These ANNs differ from traditional back propagation ANNs in that they rely on the timing and frequency of signals, rather than their strength, to learn and process information. This type of ANN of often ignored for many reasons, mostly due to the computational complexity of learning using these models. On substantial challenge lies in the difficulty of determining the initial configuration of the network. The time required to train the network is also a formidable challenge. My research seeks to eliminate one of these hurdles by deriving an efficient algorithm that can determine the proper starting configuration for the ANN.

ACHIEVEMENTS

  • Poster: 2014 Sigma Xi Summer Student Research Symposium, July 24, Bucknell University, Lewisburg, PA

POST GRADUATION UPDATES

Charles Cole ’14

Project: Using Machine Learning to Predict the Health of HIV-Infected Patients
Duration: Summer 2012 – Spring 2014
Funding: Bucknell University PUR, Biology Dept. and CS Dept. Funding

ABSTRACT

HIV is one of the most devastating viruses to hit mankind in modern history. About half of people infected will acquire AIDS. For some, however, the virus will lay in a stage known as “clinical latency” for 10, perhaps up to 20 years; in this stage, the symptoms are mild, sometimes even non-existant. This study aims to investigate the potential existance of specific patterns in the genome of HIV, and the prognosis of the infected patient. Discovery of such patterns could help aid researchers in improved understanding of the genetics of HIV, assisting in identifying potential patterns that researchers should look for to help infected doctors predict patient prognosis more accurately. Moreover, the identification of specific mutations or recurring patterns that are highly deleterious to the infected patient could aid in the development of drugs to target those genes containing the deleterious mutations.

ACHIEVEMENTSS

  • Honors thesis defense passed – April 25, 2014
  • Short paper and poster: ACB BCB ’13 – ACM International Conference on Bioinformatics, Computational Biology and Biomedicine, Sept 22-25, Washington DC
  • Oral presentation: Third Annual Susquehanna Valley Undergraduate Research Symposium, SVURS 2013, August 6, Geisinger Research, Danville, PA
    • Winner for oral presentation – One of three chosen out of 67 submissions!
  • Poster: Kalman Research Symposium 2013, April 13, Bucknell University, Lewisburg, PA.

POST GRADUATION UPDATES

Charles was accepted into to a pre-med program at Temple University, and will be starting medical school immediately thereafter.

Brigitte Hofmeister ’14

Project: Modeling the Evolution of Influenza
Duration: Summer 2013 – Fall 2013
Funding: Bucknell University Program for Undergraduate Research, CS Dept. Funding

OVERVIEW

The primary aim of this project was to develop a model of evolution of the Influenza virus. We were interested in learning if there were any model that could predict future variants better than random. This work was far more complex than we initially envisioned. However, some really interesting

The first project (completed Summer 2013) was to develop an alignment-free model that can assess the similarity between protein sequences. The grand objective, however, was to induce a model of evolution among one or two of the gene products of Influenza. To do this, we started with an n-gram model of the protein, and compute a distance between sequences by not only considering n-grams that are identical, but also those that have high biological similarity. To this end, we incorporate a standard substitution matrix (e.g. BLOSUM62) in the distance calculation between n-grams that do not have a 100% match. This work ended up with our first project, and ultimately the primary outcome that had the most utility: Using n-gram protein models with substitution matrices for phylogenetic analysis.

ABSTRACT

Phylogenetic analyses, specifically phylogenetic tree constructions, are important for understanding evolution and species relatedness. Most methods require a multiple sequence alignment (MSA) to be performed prior to inducing the phylogenetic tree. MSAs, however, are computationally expensive and increasingly error prone as the number of sequences increase, as the average sequence length increases, and as the sequences in the set become more divergent. We introduce a new method called ngPhylo, an n-gram based method that addresses many of the limitations of MSA-based phylogenetic methods, and computes alignment-free phylogenetic analyses on large sets of proteins that also have long sequences. Unlike other methods, we incorporate the use of standard substitution matrices to improve similarity measures between sequences. Our results show that highly similar phylogenies are produced to existing MSA-based methods with less computational resources required.

ACHIEVEMENTS

  • Short paper and poster: ACB BCB ’13 – ACM International Conference on Bioinformatics, Computational Biology and Biomedicine, Sept 22-25, Washington DC [link] [PDF]
  • Poster: Kalman Research Symposium 2013, April 13, Bucknell University, Lewisburg, PA.

POST GRADUATION UPDATES

Brigitte is pursuing a doctorate at University of Georgia in Bioinformatics, starting Fall 2014

Matthew Segar, ’12

Project: A probabilistic method for assembly of next generation sequencing instrumentation
Duration: Summer 2011 – Spring 2012
Funding: Bucknell PUR, Provost’s Office, CS. Dept Funds

ABSTRACT

With the advent of cheaper and faster DNA sequencing technologies, assembly methods have greatly changed. Instead of outputting reads that are thousands of base pairs long, new sequencers parallelize the task by producing read lengths between 35 and 400 base pairs. Reconstructing an organism’s genome from these millions of reads is a computationally expensive task. Our algorithm solves this problem by organizing and indexing the reads using n-grams, which are short, fixed-length DNA sequences of length n. These n-grams are used to efficiently locate putative read joins, thereby eliminating the need to perform an exhaustive search over all possible read pairs. Our goal is to develop a novel n-gram method for the assembly of genomes from next-generation sequencers. Specifically, a probabilistic, iterative approach will be utilized to determine the most likely reads to join through development of a new metric that models the probability of any two arbitrary reads being joined together. Tests were run using simulated short read data based on randomly created genomes ranging in lengths from 10,000 to 100,000 nucleotides with 16 to 20x coverage. We have been able to successfully re-assemble entire genomes up to 100,000 nucleotides in length.

ACHIEVEMENTS

  • Honor’s Thesis: A probabilistic method for assembly of next generation sequencing instrumentation
    • Matt was awarded the Harold W. Miller prize — a competitive university-wide award given to one or two students at graduation that complete a highly successful honors thesis. CONGRATULATIONS, MATT! The award was well-deserved.
  • Poster (International Conference) – Presented at 20th Annual International Conference on Intelligent Systems for Molecular Biology, ISMB 2012, July 15-17, Long Beach, CA
  • Poster – Susquehanna Valley Undergraduate Research Symposium, August 9, 2011 Geisinger Research, Danville, PA
  • Poster – Sigma Xi Summer Research Symposium, July 27, 2011, Bucknell University, Lewisburg, PA

POST GRADUATION UPDATES

Matt completed a masters in bioinformatics at Indiana University – Purdue University Indianapolis in Spring, 2014. He has now been accepted into the School of Medicine at Indiana University.

Alex Barteau, ’13

Project: Development of protein sequence analysis software
Duration: Summer 2011 – Spring 2012
Funding: University of Nebraska Medical Center
Collaboration: Dr. Chittibabu Guda

ABSTRACT

Proteins are the essence of every living organism. Every protein has a well-defined function, and must localize in the cell in order to carry out its function. This information about the protein is encoded in the protein sequence itself.  Alex will be working on a project started by Professor King that analyzes protein sequences to look for recurring patterns that are related to protein localization and their function. These observed patterns can then be used to suggest information about new protein sequences. The aim of this project is to make the software publicly available for the biological and biomedical research community.

ACHIEVEMENTS

  • Poster – Sigma Xi Summer Research Symposium, July 27, 2011, Bucknell University, Lewisburg, PA
  • King BR, Vural S, Pandey S, Barteau A, Guda C. ngLOC: software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes. BMC Research Notes; 2012; 5(351) [link] [PDF]

Marc Burian ’12

Project: Twitter sentiment and stock market performance
Duration: Spring 2012 – Summer 2012
Funding: None

ABSTRACT

This is a project that has continued from Marc’s excellent work in data mining. We are investigating an algorithm we developed that clusters similar Tweets in real-time and generates sentiment related to a specific company of interest from incoming Tweets. Using this information and plotting it against the performance of the company’s stock price, we are gauging the potential use of Twitter as a descriptor and predictor of the stock market.

ACHIEVEMENTS

Marc started this project toward the end of my data mining class. There was insufficient time to bring the project into a publishable / presentable form. However, Marc obtained satisfaction of learning the Twitter API, and learning first-hand that “tweets” are extremely noisy sources of information for stock prediction! We were able to uncover several instances that had minute predictive power, but not statistically significant. More work would need to be completed to filter and process tweets to discard meaningless and irrelevant data.

Phil Stahlfeld ’13

Project: Installation and deployment of caBIG – an information management network for cancer research
Duration: Summer 2011 – Spring 2012
Funding: Geisinger Research
Collaboration: Dr. Gerardus Tromp

ABSTRACT

Phil is working with Dr. Gerardus Tromp, a collaborator at Geisinger Medical Research Center in Danville, on deploying a complex software system called the Cancer Biomedical Informatics Grid, or caBIG.  As a product of the National Cancer Instutute, caBIG was conceived for the purpose of sharing data and knowledge among researchers, clinicians, and patients in order to simplify collaboration and speed research to get diagnostics and therapeutocs from bench to bedside faster and more cost-effectively. The system is multi-tiered, with the front end mostly implemented using JBoss and Tomcat applications in Java, and the backend data management provided by PostgreSQL and MySQL. The entire suite is open software and will require some modification to meet the local needs of Geisinger. Phil will be an integral part of the installation, configuration, modification and and test of the system.