arXiv research

PepCVAE designs novel antimicrobial peptides using a semi-supervised VAE.

New method designs antimicrobial peptides with high potency and low toxicity.

Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide. Accurately predicting resistance phenotypes from genome sequences shows great promise in promoting better use of antimicrobial agents, by determining which antibiotics are likely to be effective i…

AMP0 predicts antimicrobial peptides targeting specific microbes.

Study predicts antimicrobial resistance in ICU patients quickly.

Bayesian network models are finding success in characterizing enzyme-catalyzed reactions, slow conformational changes, predicting enzyme inhibition, and genomics. In this work, we apply them to statistical modeling of peptides by simultaneously identifying amino acid sequence motifs and using a motif-based model to cla…

The Set Covering Machine (SCM) is a greedy learning algorithm that produces sparse classifiers. We extend the SCM for datasets that contain a huge number of features. The whole genetic material of living organisms is an example of such a case, where the number of feature exceeds 10^7. Three human pathogens were used to…

Biological datasets amenable to applied machine learning are more available today than ever before, yet they lack adequate representation in the Data-for-Good community. Here we present a work in progress case study performing analysis on antimicrobial resistance (AMR) using standard ensemble machine learning technique…

Deep learning identifies 30 common bacterial pathogens from Raman spectra.

Study uses machine learning to optimize antibiotic therapy for MRSA skin infections.

A framework for faster, better infographic design by non-experts and experts alike.

A new RL method helps designers solve complex tasks.

The paper proposes a method to reliably select design algorithms for machine learning-guided design tasks.

Unified approach to experimental design using interlacing polynomials.

Design-by-Morphing creates radical airfoil designs without geometric constraints.

MO-PaDGAN generates diverse, high-performance designs with multiple metrics.

MCD automates counterfactual design searches for multi-modal tasks.

PaDGAN generates diverse, high-quality designs with improved performance.

ANN with GA optimizes flexible disc design for lower mass and stress.

DeepCloud uses machine learning to generate design alternatives without explicit designer input.

New method for mixed-variable GSA improves material design efficiency.

Deep learning models enhance engineering design automation.

Design automation optimizes deep learning models for various hardware.

Enhances scenario approach for certifying design properties post-design.

DAD learns to design experiments quickly, outperforming traditional methods.

MO-PaDGAN improves multi-objective optimization by generating diverse and high-performing designs.

Topology design optimization offers tremendous opportunity in design and manufacturing freedoms by designing and producing a part from the ground-up without a meaningful initial design as required by conventional shape design optimization approaches. Ideally, with adequate problem statements, to formulate and solve the…

Generative thermal design learns optimal shapes using multi-agent reinforcement learning.

Develops a prediction method based on sampling design.

Solving the RNA inverse folding problem is a critical prerequisite to RNA design, an emerging field in bioengineering with a broad range of applications from reaction catalysis to cancer therapy. Although significant progress has been made in developing machine-based inverse RNA folding algorithms, current approaches s…

Paper proposes NASAIC framework for co-designing neural architectures and heterogeneous ASICs.

Adaptive designs achieve strong Neyman regret guarantees for ATE estimation.

iDAD uses neural networks to quickly adapt experiments without likelihoods.

Unified framework for combinatorial and rounding algorithms in experimental design.

New method refines model predictions as design evolves.

A new method achieves optimal uniformity in designs with minimal flexibility.

Optimizes PCB stack-up design for high-speed circuits.

Machine learning improves polymer design accuracy.

RID-Noise improves robust design under noisy conditions using neural networks.

Proposes a new adaptive design strategy for discontinuous regression functions.

The design of multiple experiments is commonly undertaken via suboptimal strategies, such as batch (open-loop) design that omits feedback or greedy (myopic) design that does not account for future effects. This paper introduces new strategies for the optimal design of sequential experiments. First, we rigorously formul…

Dataset of 4500 bicycle designs aids in design analysis and synthesis.

This paper proposes a new approach to construct high quality space-filling sample designs. First, we propose a novel technique to quantify the space-filling property and optimally trade-off uniformity and randomness in sample designs in arbitrary dimensions. Second, we connect the proposed metric (defined in the spatia…

Machine learning predicts and generates appealing car designs.

Layout design with complex constraints is a challenging problem to solve due to the non-uniqueness of the solution and the difficulties in incorporating the constraints into the conventional optimization-based methods. In this paper, we propose a design method based on the recently developed machine learning technique,…

New deep learning method simplifies parameter estimation design.

Neural network ensembles predict design rule violations from early stages of IC design.

Calibrating materials to engineering targets improves wind turbine design.