Browsing by Author "Jaakkola, Tommi S."
Now showing items 1-14 of 14
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A Deep Learning Approach to Antibiotic Discovery
(Elsevier BV, 2020-02)Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial ... -
Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape
(Nature Publishing Group, 2014-01)We describe protein interaction quantitation (PIQ), a computational method for modeling the magnitude and shape of genome-wide DNase I hypersensitivity profiles to identify transcription factor (TF) binding sites. Through ... -
A graph-convolutional neural network model for the prediction of chemical reactivity
(Royal Society of Chemistry (RSC), 2019-01)© 2019 The Royal Society of Chemistry. We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s). The prediction task is factored into two stages ... -
Hierarchical Dirichlet Process-Based Models For Discovery of Cross-species Mammalian Gene Expression
(2007-07-06)An important research problem in computational biology is theidentification of expression programs, sets of co-activatedgenes orchestrating physiological processes, and thecharacterization of the functional breadth of these ... -
Molding CNNs for text: Non-linear, non-consecutive convolutions
(Association for Computational Linguistics, 2015-09)The success of deep learning often derives from well-chosen operational building blocks. In this work, we revise the temporal convolution operation in CNNs to better adapt it to text processing. Instead ... -
On the Dirichlet Prior and Bayesian Regularization
(2002-09-01)A common objective in learning a model from data is to recover its network structure, while the model parameters are of minor interest. For example, we may wish to recover regulatory networks from high-throughput data ... -
Prediction of Organic Reaction Outcomes Using Machine Learning
(American Chemical Society (ACS), 2017-04)Computer assistance in synthesis design has existed for over 40 years, yet retrosynthesis planning software has struggled to achieve widespread adoption. One critical challenge in developing high-quality pathway suggestions ... -
(Semi-)Predictive Discretization During Model Selection
(2003-02-25)In this paper, we present an approach to discretizing multivariate continuous data while learning the structure of a graphical model. We derive the joint scoring function from the principle of predictive accuracy, which ... -
Semi-supervised question retrieval with gated convolutions
(North American Chapter of the Association for Computational Linguistics, 2016-06)Question answering forums are rapidly growing in size with no effective automated ability to refer to and reuse answers already available for previous posted questions. In this paper, we develop a methodology for finding ... -
A synergistic DNA logic predicts genome-wide chromatin accessibility
(Cold Spring Harbor Laboratory Press, 206-08)Enhancers and promoters commonly occur in accessible chromatin characterized by depleted nucleosome contact; however, it is unclear how chromatin accessibility is governed. We show that log-additive cis-acting DNA sequence ... -
Table 1 (Supplemental): Summary of expression programs discovered by GeneProgram from Novartis Tissue Atlas v2 data
(2007-06-25)Table 1 (Supplemental): Summary of recurrent expression programs (EPs) discovered by GeneProgram. The columns are: (1) the EP identifier (an arbitrarily assigned number), (2) the number of genes in the EP, (3) the number ... -
Table 2 (Supplemental): Complete data for all 100 expression programs discovered by GeneProgram from the Novartis Gene Atlas v2
(2007-06-25)Table 2 (Supplemental): Complete data for all 100 recurrent expression programs (EPs) discovered by GeneProgram. Each EP has two identifying rows, a list of meta-genes, and a list of significantly enriched GO categories. ... -
Ten pairs to tag - Multilingual POS tagging via coarse mapping between embeddings
(Association for Computational Linguistics, 2016-06)In the absence of annotations in the target language, multilingual models typically draw on extensive parallel resources. In this paper, we demonstrate that accurate multilingual partof-speech (POS) tagging can be done ... -
Tight bounds for the expected risk of linear classifiers and PAC-bayes finite-sample guarantees
(Journal of Machine Learning Research, 2014-04)We analyze the expected risk of linear classifiers for a fixed weight vector in the “minimax” setting. That is, we analyze the worst-case risk among all data distributions with a given mean and covariance. We provide a ...
