Bioinspired design uses biological systems to inspire engineering designs. One of bioinspired design’s challenges is identifying relevant information sources in biology for an engineering design task. Currently information can be retrieved by searching biology texts or journals using biology-focused keywords that map to engineering functions. However, this search technique can overwhelm designers with unusable results. This work explores the use of text classification tools to identify relevant biology passages for design. Further, this research examines the effects of using biology passages as stimuli during idea generation. Four human-subjects studies are examined in this work. Two surveys are performed in which participants evaluate sentences from a biology corpus and indicate whether each sentence prompts an idea for solving a specific design problem. The surveys are used to develop and evaluate text classification tools. Two idea generation studies are performed in which participants generate and record solutions for designing a corn shucker using either different sets of biology passages as design stimuli, or no stimuli. Based 286 sentences from the surveys, a k Nearest Neighbor classifier is developed that is able to identify helpful sentences relating to the function “separate” with a precision of 0.62 and recall of 0.48. This classifier could potentially double the number of helpful results found using a keyword search. The developed classifier is specific to the function “separate” and performs poorly when used for another function. Classifiers developed using all sentences and participant responses from the surveys are not able to reliably identify helpful sentences. From the idea generation studies, we determine that using any biology passages as design stimuli increases the quantity and variety of participant solutions. Solution quantity and variety are also significantly increased when biology passages are presented one at a time instead of all at once. Quality and variety are not significantly affected by the presence of design stimuli. Biological stimuli are also found to lead designers to types of solution that are not typically produced otherwise. This work develops a means for designers to find more useful information when searching biology and demonstrates several ways that biology passages can improve ideation.

Experimentally determining the biological function of a protein is a process known as protein characterization. Establishing the role a specific protein plays is a vital step toward fully understanding the biochemical processes that drive life in all its forms. In order for researchers to efficiently locate and benefit from the results of protein characterization experiments, the relevant information is compiled into public databases. To populate such databases, curators, who are experts in the biomedical domain, must search the literature to obtain the relevant information, as the experiment results are typically published in scientific journals. The database curators identify relevant journal articles, read them, and then extract the required information into the database. In recent years the rate of biomedical research has greatly increased, and database curators are unable to keep pace with the number of articles being published. Consequently, maintaining an up-to-date database of characterized proteins, let alone populating a new database, has become a daunting task. In this thesis, we report our work to reduce the effort required from database curators in order to create and maintain a database of characterized proteins. We describe a system we have designed for automatically identifying relevant articles that discuss the results of protein characterization experiments. Classifiers are trained and tested using a large dataset of abstracts, which we collected from articles referenced in public databases, as well as small datasets of manually labeled abstracts. We evaluate both a standard and a modified naïve Bayes classifier and examine several different feature sets for representing articles. Our findings indicate that the resulting classifier performs well enough to be considered useful by the curators of a characterized protein database. / Thesis (Master, Computing) -- Queen's University, 2010-01-28 18:45:17.249

Background: Breast cancer is a complex disease, both phenotypically and etiologically. Accordingly, the responses to various treatments in the adjuvant setting among individuals vary considerably. There is a demand for tools that can distinguish patients who may benefit or may suffer from particular systemic treatments. We hypothesized that combination of data on genetic biomarkers with data from traditional clinical and pathophysiological (clinicopathologic) factors using traditional Cox model or Support Vector Machine (SVM) method, a new machine learning method, may provide a better tool for prediction of benefits to chemotherapy for the treatment of early breast cancer than using either biomarker or clinicopathologic data alone. Methods: This project included 531 patients from NCIC-CTG MA.5 trial who had data on both clinicopathologic factors, such as age, tumor size, ER status, type of surgery, tumor grade and lymph node involvement, and biomarkers assayed on tissue microarrays (TMAs), including HER2, p53, CA9, MEP21, clusterin, pAKT, COX2 and TOP2A. The Cox model and SVM methods were used to develop prognostic indices for relapse-free or overall survival with either data from TMAs and clinicopathologic assessments alone or their combination. The prognostic indices developed were then examined for their value as predictive classifiers for benefits from CEF treatment. The power of the predictive classifiers derived was evaluated and compared using the bootstrap approach. Results: None of the prognostic indices developed were found to have significant predictive value, although the prognostic index developed using SVM method based on only biomarkers yielded a marginal significant p-value (p=0.0527) for the interaction between classifier and treatment. In accordance with results published previously, the interaction between the classifier developed based on HER2 or TOP2A and treatment was significant (p=0.02 and 0.04 respectively). Comparisons based on the bootstrap approach indicate classifiers developed based on SVM performed better than those based on the Cox model method. Conclusions: Combination of data using biomarkers and clinical-pathological factors, and using either the traditional COX model method or the new machine learning method was not shown to perform better than two single previously known biomarkers in prediction of response to CEF treatment for early breast cancer. / Thesis (Master, Community Health & Epidemiology) -- Queen's University, 2010-04-27 10:16:11.811

Emotions play an important role for everyday communication. Different methods allow computers to recognize emotions. Most are trained with acted emotions and it is unknown if such a model would work for recognizing naturally appearing emotions. An experiment was setup to estimate the recognition accuracy of the emotion recognition software SHORE, which could detect the emotions angry, happy, sad, and surprised. Subjects played a casino game while being recorded. The software recognition was correlated with the recognition of ten human observers. The results showed a strong recognition for happy, medium recognition for surprised, and a weak recognition for sad and angry faces. In addition, questionnaires containing self-informed emotions were compared with the computer recognition, but only weak correlations were found. SHORE was able to recognize emotions almost as well as humans were, but if humans had problems to recognize an emotion, then the accuracy of the software was much lower.

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