Author name cluster

Kenney Ng

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6 papers

2 author rows

IJCAI Conference 2024 Conference Paper

SPARK: Harnessing Human-Centered Workflows with Biomedical Foundation Models for Drug Discovery

Bum Chul Kwon
Simona Rabinovici-Cohen
Beldine Moturi
Ruth Mwaura
Kezia Wahome
Oliver Njeru
Miguel Shinyenyi
Catherine Wanjiru

Biomedical foundation models, trained on diverse sources of small molecule data, hold great potential for accelerating drug discovery. However, their complex nature often presents a barrier for researchers seeking scientific insights and drug candidate generation. SPARK addresses this challenge by providing a user-friendly, web-based interface that empowers researchers to leverage these powerful models in their scientific workflows. Through SPARK, users can specify target proteins and desired molecule properties, adjust pre-trained models for tailored inferences, generate lists of potential drug candidates, analyze and compare molecules through interactive visualizations, and filter candidates based on key metrics (e. g. , toxicity). By seamlessly integrating human knowledge and biomedical AI models' capabilities through an interactive web-based system, SPARK can improve the efficiency of collaboration between human experts and AI, thereby accelerating drug candidate discovery and ultimately leading to breakthroughs in finding cures for various diseases.

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UAI Conference 2021 Conference Paper

Post-hoc loss-calibration for Bayesian neural networks

Meet P. Vadera
Soumya Ghosh
Kenney Ng
Benjamin M. Marlin

Bayesian decision theory provides an elegant framework for acting optimally under uncertainty when tractable posterior distributions are available. Modern Bayesian models, however, typically involve intractable posteriors that are approximated with, potentially crude, surrogates. This difficulty has engendered loss-calibrated techniques that aim to learn posterior approximations that favor high-utility decisions. In this paper, focusing on Bayesian neural networks, we develop methods for correcting approximate posterior predictive distributions encouraging them to prefer high-utility decisions. In contrast to previous work, our approach is agnostic to the choice of the approximate inference algorithm, allows for efficient test time decision making through amortization, and empirically produces higher quality decisions. We demonstrate the effectiveness of our approach through controlled experiments spanning a diversity of tasks and datasets.

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AAAI Conference 2019 Conference Paper

Unsupervised Learning with Contrastive Latent Variable Models

Kristen A. Severson
Soumya Ghosh
Kenney Ng

In unsupervised learning, dimensionality reduction is an important tool for data exploration and visualization. Because these aims are typically open-ended, it can be useful to frame the problem as looking for patterns that are enriched in one dataset relative to another. These pairs of datasets occur commonly, for instance a population of interest vs. control or signal vs. signal free recordings. However, there are few methods that work on sets of data as opposed to data points or sequences. Here, we present a probabilistic model for dimensionality reduction to discover signal that is enriched in the target dataset relative to the background dataset. The data in these sets do not need to be paired or grouped beyond set membership. By using a probabilistic model where some structure is shared amongst the two datasets and some is unique to the target dataset, we are able to recover interesting structure in the latent space of the target dataset. The method also has the advantages of a probabilistic model, namely that it allows for the incorporation of prior information, handles missing data, and can be generalized to different distributional assumptions. We describe several possible variations of the model and demonstrate the application of the technique to de-noising, feature selection, and subgroup discovery settings.

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AAAI Conference 2018 Conference Paper

Early Prediction of Diabetes Complications from Electronic Health Records: A Multi-Task Survival Analysis Approach

Bin Liu
Ying Li
Zhaonan Sun
Soumya Ghosh
Kenney Ng

Type 2 diabetes mellitus (T2DM) is a chronic disease that usually results in multiple complications. Early identiﬁcation of individuals at risk for complications after being diagnosed with T2DM is of signiﬁcant clinical value. In this paper, we present a new data-driven predictive approach to predict when a patient will develop complications after the initial T2DM diagnosis. We propose a novel survival analysis method to model the time-to-event of T2DM complications designed to simultaneously achieve two important metrics: 1) accurate prediction of event times, and 2) good ranking of the relative risks of two patients. Moreover, to better capture the correlations of time-to-events of the multiple complications, we further develop a multi-task version of the survival model. To assess the performance of these approaches, we perform extensive experiments on patient level data extracted from a large electronic health record claims database. The results show that our new proposed survival analysis approach consistently outperforms traditional survival models and demonstrate the effectiveness of the multi-task framework over modeling each complication independently.

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AAAI Conference 2015 Conference Paper

Towards Cognitive Automation of Data Science

Alain Biem
Maria Butrico
Mark Feblowitz
Tim Klinger
Yuri Malitsky
Kenney Ng
Adam Perer
Chandra Reddy

A Data Scientist typically performs a number of tedious and time-consuming steps to derive insight from a raw data set. The process usually starts with data ingestion, cleaning, and transformation (e. g. outlier removal, missing value imputation), then proceeds to model building, and finally a presentation of predictions that align with the end-users objectives and preferences. It is a long, complex, and sometimes artful process requiring substantial time and effort, especially because of the combinatorial explosion in choices of algorithms (and platforms), their parameters, and their compositions. Tools that can help automate steps in this process have the potential to accelerate the time-to-delivery of useful results, expand the reach of data science to non-experts, and offer a more systematic exploration of the available options. This work presents a step towards this goal.

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NeurIPS Conference 1990 Conference Paper

A Comparative Study of the Practical Characteristics of Neural Network and Conventional Pattern Classifiers

Kenney Ng
Richard Lippmann

Seven different pattern classifiers were implemented on a serial computer and compared using artificial and speech recognition tasks. Two neural network (radial basis function and high order polynomial GMDH network) and five conventional classifiers (Gaussian mixture, linear tree, K nearest neighbor, KD-tree, and condensed K nearest neighbor) were evaluated. Classifiers were chosen to be representative of different approaches to pat(cid: 173) tern classification and to complement and extend those evaluated in a previous study (Lee and Lippmann, 1989). This and the previous study both demonstrate that classification error rates can be equivalent across different classifiers when they are powerful enough to form minimum er(cid: 173) ror decision regions, when they are properly tuned, and when sufficient training data is available. Practical characteristics such as training time, classification time, and memory requirements, however, can differ by or(cid: 173) ders of magnitude. These results suggest that the selection of a classifier for a particular task should be guided not so much by small differences in error rate, but by practical considerations concerning memory usage, com(cid: 173) putational resources, ease of implementation, and restrictions on training and classification times.

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