Person: Gehrmann, Sebastian
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Gehrmann
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Sebastian
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Gehrmann, Sebastian
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Publication LSTM Networks Can Perform Dynamic Counting(2019-06-09) Suzgun, Mirac; Gehrmann, Sebastian; Belinkov, Yonatan; Shieber, StuartIn this paper, we systematically assess the ability of standard recurrent networks to perform dynamic counting and to encode hierarchical representations. All the neural models in our experiments are designed to be small-sized networks both to prevent them from memorizing the training sets and to visualize and interpret their behaviour at test time. Our results demonstrate that the Long Short-Term Memory (LSTM) networks can learn to recognize the well-balanced parenthesis language (Dyck-1) and the shuffles of multiple Dyck-1 languages, each defined over different parenthesis-pairs, by emulating simple real-time k-counter machines. To the best of our knowledge, this work is the first study to introduce the shuffle languages to analyze the computational power of neural networks. We also show that a single-layer LSTM with only one hidden unit is practically sufficient for recognizing the Dyck-1 language. However, none of our recurrent networks was able to yield a good performance on the Dyck-2 language learning task, which requires a model to have a stack-like mechanism for recognition.Publication Accelerating Antimicrobial Discovery With Controllable Deep Generative Models and Molecular Dynamics(2021-03-11) Das, Payel; Sercu, Tom; Wadhawan, Kahini; Padhi, Inkit; Gehrmann, Sebastian; Cipcigan, Flaviu; Chenthamarakshan, Vijil; Strobelt, Hendrik; dos Santos, Cicero; Chen, Pin-Yu; Yang, Yi Yan; Tan, Jeremy P.K.; Hedrick, James; Crain, Jason; Mojsilovic, Aleksandra; MojsilovicDe novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints such as high broad-spectrum potency and low toxicity. We propose CLaSS (Controlled Latent attribute Space Sampling) — an efficient computational method for attribute-controlled generation of molecules, which leverages guidance from classifiers trained on an informative latent space of molecules modeled using a deep generative autoencoder. We further screen the generated molecules for additional key attributes by using a set of deep learning classifiers in conjunction with novel physicochemical features derived from high-throughput molecular simulations. The proposed approach is employed for designing non-toxic antimicrobial peptides (AMPs) with strong broad-spectrum potency, which are emerging drug candidates for tackling antibiotic resistance. Synthesis and wet lab testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including the multidrug-resistant K. pneumoniae, as well as low in vitro and in vivo toxicity.Live-cell confocal imaging revealed that the bactericidal mode of action of the peptides occurs through membrane pore formation. Both antimicrobials mitigate the onset of drug resistance and are effective against antibiotic-resistant strains. The proposed approach thus presents a viable path for faster and efficient discovery of potent and selective broad-spectrum antimicrobials.Publication Deploying AI Methods to Support Collaborative Writing: A Preliminary Investigation(2015) Gehrmann, Sebastian; Urke, Lauren; Amir, Ofra; Grosz, BarbaraMany documents (e.g., academic papers, government reports) are typically written by multiple authors. While existing tools facilitate and support such collaborative efforts (e.g., Dropbox, Google Docs), these tools lack intelligent information sharing mechanisms. Capabilities such as “track changes” and “diff” visualize changes to authors, but do not distinguish between minor and major edits and do not consider the possible effects of edits on other parts of the document. Drawing collaborators’ attention to specific edits and describing them remains the responsibility of authors. This paper presents our initial work toward the development of a collaborative system that supports multi-author writing. We describe methods for tracking paragraphs, identifying significant edits, and predicting parts of the paper that are likely to require changes as a result of previous edits. Preliminary evaluation of these methods shows promising results.Publication Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives(Public Library of Science (PLoS), 2018) Gehrmann, Sebastian; Dernoncourt, Franck; Li, Yeran; Carlson, Eric T.; Wu, Joy T.; Welt, Jonathan; Foote, John; Moseley, Edward T.; Grant, David W.; Tyler, Patrick D.; Celi, Leo A.In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.