level-one heading

プロフィール詳細

プロジェクトを作成

★★★★★

☆☆☆☆☆

★★★★★

☆☆☆☆☆

Jordan G.に依頼

United States

プロフィール概要

As a graduate student I worked alongside Dr. Lisa Gandy as her graduate research assistant. I also worked in Dr. Jesse Eickholt's MLiD lab in pursuit of a state of the art model for predicting disordered protein regions.

専門分野

• ☆ Signal Processing
• ☆ Machine Learning
• ☆ Neural Networks
• ☆ Bioinformatics

サービス

• Writing
• Data & AI

職務経験

Research Computer Specialist; Bioinformatician

University of Michigan

5月 2017 - 現在

Bioinformatics Scientist

Progenity Inc

1月 2017 - 5月 2017

Bioinformatics Associate

Progenity Inc

9月 2015 - 1月 2017

Graduate Research Assistant

Central Michigan University

9月 2013 - 9月 2015

Research Associate

Central Michigan University

4月 2013 - 10月 2013

Research Assistant

Central Michigan University

10月 2011 - 9月 2012

iOS Developer

Central Michigan University

4月 2010 - 10月 2011

学歴

Masters of Science (Computer Science)

Central Michigan University

8月 2013 - 現在

Bachelors of Science (Computer Science)

Central Michigan University

8月 2007 - 5月 2013

Bachelors of Science (Philosophy)

Central Michigan University

8月 2007 - 5月 2013

認定資格

• 認定資格の詳細は未入力です。

出版物

JOURNAL ARTICLE

A Software Application for Mining and Presenting Relevant Cancer Clinical Trials per Cancer Mutation @article{10.1177/1176935117711940, author= {Lisa M Gandy and Jordan Gumm and Amanda L Blackford and Elana J Fertig and Luis A Diaz}, journal= {Cancer Informatics}, publisher= {SAGE Publishing}, title= {A Software Application for Mining and Presenting Relevant Cancer Clinical Trials per Cancer Mutation}, year= {2017}, month= {06}, volume= {16}, url= {http://insights.sagepub.com/a-software-application-for-mining-and-presenting-relevant-cancer-clini-article-a6428}, pages= {1176935117711940}, abstract= {ClinicalTrials.org is a popular portal which physicians use to find clinical trials for their patients. However, the current setup of ClinicalTrials.org makes it difficult for oncologists to locate clinical trials for patients based on mutational status. We present CTMine, a system that mines ClinicalTrials.org for clinical trials per cancer mutation and displays the trials in a user-friendly Web application. The system currently lists clinical trials for 6 common genes (ALK, BRAF, ERBB2, EGFR, KIT, and KRAS). The current machine learning model used to identify relevant clinical trials focusing on the above gene mutations had an average 88% precision/recall. As part of this analysis, we compared human versus machine and found that oncologists were unable to reach a consensus on whether a clinical trial mined by CTMine was “relevant” per gene mutation, a finding that highlights an important topic which deems future exploration. }, doi= {10.1177/1176935117711940}} . Cancer Informatics.

Synthesizer: Expediting synthesis studies from context-free data with information retrieval techniques @article{10.1371/journal.pone.0175860, author= {Gandy, Lisa M. AND Gumm, Jordan AND Fertig, Benjamin AND Thessen, Anne AND Kennish, Michael J. AND Chavan, Sameer AND Marchionni, Luigi AND Xia, Xiaoxin AND Shankrit, Shambhavi AND Fertig, Elana J.}, journal= {PLOS ONE}, publisher= {Public Library of Science}, title= {Synthesizer: Expediting synthesis studies from context-free data with information retrieval techniques}, year= {2017}, month= {04}, volume= {12}, url= {https://doi.org/10.1371/journal.pone.0175860}, pages= {1-15}, abstract= {Scientists have unprecedented access to a wide variety of high-quality datasets. These datasets, which are often independently curated, commonly use unstructured spreadsheets to store their data. Standardized annotations are essential to perform synthesis studies across investigators, but are often not used in practice. Therefore, accurately combining records in spreadsheets from differing studies requires tedious and error-prone human curation. These efforts result in a significant time and cost barrier to synthesis research. We propose an information retrieval inspired algorithm, Synthesize, that merges unstructured data automatically based on both column labels and values. Application of the Synthesize algorithm to cancer and ecological datasets had high accuracy (on the order of 85–100%). We further implement Synthesize in an open source web application, Synthesizer (https://github.com/lisagandy/synthesizer). The software accepts input as spreadsheets in comma separated value (CSV) format, visualizes the merged data, and outputs the results as a new spreadsheet. Synthesizer includes an easy to use graphical user interface, which enables the user to finish combining data and obtain perfect accuracy. Future work will allow detection of units to automatically merge continuous data and application of the algorithm to other data formats, including databases.}, number= {4}, doi= {10.1371/journal.pone.0175860}} . PLOS ONE.

(2015). An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions . International Journal of Molecular Sciences.