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Found 67 papers, 34 papers with code
Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation
Collectively, we envision our method will become an important benchmark for evaluating Graph2Text methods and advance biomedical research for complex diseases.
Textomics: A Dataset for Genomics Data Summary Generation
We further illustrate how Textomics can be used to advance other applications, including evaluating scientific paper embeddings and generating masked templates for scientific paper understanding.
BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Prediction
Instead of obtaining features from language models, we propose BLIAM, a literature-based data synthesis approach to directly generate training data points that are interpretable and model-agnostic to downstream applications.
ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models
Large language models (LLMs) have recently demonstrated their potential in clinical applications, providing valuable medical knowledge and advice.
RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation
However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches.
Robust One-shot Segmentation of Brain Tissues via Image-aligned Style Transformation
In this paper, we propose a novel image-aligned style transformation to reinforce the dual-model iterative learning for robust one-shot segmentation of brain tissues.
CELLS: A Parallel Corpus for Biomedical Lay Language Generation
Recent lay language generation systems have used Transformer models trained on a parallel corpus to increase health information accessibility.
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks
Meta-path, a sequence of node types and edge types, is the core technique to embed HINs.
Modern Tontine with Transaction Costs
The wealth of the retiree is divided into a bequest account and a tontine account.
POPDx: An Automated Framework for Patient Phenotyping across 392,246 Individuals in the UK Biobank Study
We describe a method for phenotype recognition that imputes phenotype codes for all UK Biobank participants.
TBI-GAN: An Adversarial Learning Approach for Data Synthesis on Traumatic Brain Segmentation
To address these issues, we propose a novel medical image inpainting model named TBI-GAN to synthesize TBI MR scans with paired brain label maps.
Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer's Disease
On top of BAR, we propose using a soft-label-capable supervised contrastive loss, aiming to learn the relative similarity of representations that reflect how mixed are the synthetic MRIs using our soft labels.
E2Efold-3D: End-to-End Deep Learning Method for accurate de novo RNA 3D Structure Prediction
When E2Efold-3D is coupled with the experimental techniques, the RNA structure prediction field can be greatly advanced.
A fully differentiable ligand pose optimization framework guided by deep learning and traditional scoring functions
In this work, we propose a fully differentiable framework for ligand pose optimization based on a hybrid scoring function (SF) combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF.
Graph-in-Graph Network for Automatic Gene Ontology Description Generation
This task aims to automatically generate a sentence that describes the function of a GO term belonging to one of the three categories, i. e., molecular function, biological process, and cellular component.
Hedging option books using neural-SDE market models
We study the capability of arbitrage-free neural-SDE market models to yield effective strategies for hedging options.
Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning
To address this problem, we propose to infuse human experts' intelligence and domain knowledge into the training of deep neural networks.
Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing
Magnetic resonance (MR) images collected in 2D clinical protocols typically have large inter-slice spacing, resulting in high in-plane resolution and reduced through-plane resolution.
Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds
Discovering latent topics from text corpora has been studied for decades.
ProTranslator: zero-shot protein function prediction using textual description
Here, we tackle this problem by annotating proteins to a function only based on its textual description so that we do not need to know any associated proteins for this function.
Follow My Eye: Using Gaze to Supervise Computer-Aided Diagnosis
In this paper, we demonstrate that the eye movement of radiologists reading medical images can be a new form of supervision to train the DNN-based computer-aided diagnosis (CAD) system.
Estimating risks of option books using neural-SDE market models
In this paper, we examine the capacity of an arbitrage-free neural-SDE market model to produce realistic scenarios for the joint dynamics of multiple European options on a single underlying.
Adaptive Transfer Learning for Plant Phenotyping
Plant phenotyping (Guo et al. 2021; Pieruschka et al. 2019) focuses on studying the diverse traits of plants related to the plants' growth.
Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution
Knee osteoarthritis (OA) is the most common osteoarthritis and a leading cause of disability.
SurvODE: Extrapolating Gene Expression Distribution for Early Cancer Identification
With the increasingly available large-scale cancer genomics datasets, machine learning approaches have played an important role in revealing novel insights into cancer development.
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning
Specifically, the backbone network is firstly trained with a multi-style and multi-view unsupervised self-learning scheme for the embedding of invariant features to various vendor-styles.
GraphPrompt: Biomedical Entity Normalization Using Graph-based Prompt Templates
Biomedical entity normalization unifies the language across biomedical experiments and studies, and further enables us to obtain a holistic view of life sciences.
Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation
KGAE consists of a pre-constructed knowledge graph, a knowledge-driven encoder and a knowledge-driven decoder.
Joint Progressive and Coarse-to-fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion
Registration of brain MRI images requires to solve a deformation field, which is extremely difficult in aligning intricate brain tissues, e. g., subcortical nuclei, etc.
Leveraging the Cell Ontology to classify unseen cell types
Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution.
Graphine: A Dataset for Graph-aware Terminology Definition Generation
Unfortunately, the lack of large-scale terminology definition dataset hinders the process toward definition generation.
Combined Trojan Y Chromosome Strategy and Sterile Insect Technique to Eliminate Mosquitoes: Modelling and Analysis
Sterile insect technique has been successfully applied in the control of agricultural pests, however, it has a limited ability to control mosquitoes.
UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing
Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.
Adaptive Residue-wise Profile Fusion for Low Homologous Protein SecondaryStructure Prediction Using External Knowledge
Protein secondary structure prediction (PSSP) is essential for protein function analysis.
Shallow Feature Matters for Weakly Supervised Object Localization
In practice, our SPOL model first generates the CAMs through a novel element-wise multiplication of shallow and deep feature maps, which filters the background noise and generates sharper boundaries robustly.
DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation
In this paper, we propose a novel disentangled heterogeneous graph attention network DisenHAN for top-$N$ recommendation, which learns disentangled user/item representations from different aspects in a heterogeneous information network.
Arbitrage-free neural-SDE market models
Modelling joint dynamics of liquid vanilla options is crucial for arbitrage-free pricing of illiquid derivatives and managing risks of option trade books.
EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models
In this paper, we propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network.
tFold-TR: Combining Deep Learning Enhanced Hybrid Potential Energy for Template-Based Modeling Structure Refinement
Protein structure prediction has been a grand challenge for over 50 years, owing to its broad scientific and application interests.
A Reinforcement Learning Based R-Tree for Spatial Data Indexing in Dynamic Environments
Learned indices have been proposed to replace classic index structures like B-Tree with machine learning (ML) models.
InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring
Compared with the visual grounding on 2D images, the natural-language-guided 3D object localization on point clouds is more challenging.
Towards Accurate Active Camera Localization
These approaches localize the camera in the discrete pose space and are agnostic to the localization-driven scene property, which restricts the camera pose accuracy in the coarse scale.
On the Efficiency of K-Means Clustering: Evaluation, Optimization, and Algorithm Selection
This paper presents a thorough evaluation of the existing methods that accelerate Lloyd's algorithm for fast k-means clustering.
High-Fidelity 3D Digital Human Head Creation from RGB-D Selfies
We present a fully automatic system that can produce high-fidelity, photo-realistic 3D digital human heads with a consumer RGB-D selfie camera.
Detecting and repairing arbitrage in traded option prices
In addition, we show that removing arbitrage from prices data by our repair method can improve model calibration with enhanced robustness and reduced calibration error.
Boosting Ant Colony Optimization via Solution Prediction and Machine Learning
In the first phase of our ML-ACO algorithm, an ML model is trained using a set of small problem instances where the optimal solution is known.
STADB: A Self-Thresholding Attention Guided ADB Network for Person Re-identification
Specifically, STADB first obtains an attention map by channel-wise pooling and returns a drop mask by thresholding the attention map.
Task-agnostic Temporally Consistent Facial Video Editing
Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.
mr2NST: Multi-Resolution and Multi-Reference Neural Style Transfer for Mammography
However, the image styles of different vendors are very distinctive, and there may exist domain gap among different vendors that could potentially compromise the universal applicability of one deep learning model.
Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation
Unsupervised domain adaptation enables to alleviate the need for pixel-wise annotation in the semantic segmentation.
Ranked #22 on
Domain Adaptation
on SYNTHIA-to-Cityscapes
Analysis of Indexing Structures for Immutable Data
We establish the worst-case guarantees of each index in terms of these five metrics, and we experimentally evaluate all indexes in a large variety of settings.
Databases
PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling
Extensive experiments verify the robustness and superiority of our approach in point clouds processing tasks regardless of synthesis data, indoor data, and outdoor data with or without noise.
Ranked #20 on
Semantic Segmentation
on S3DIS
Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation
We investigate the posterior collapse problem of current RNN-based VAEs for molecule sequence generation.
PANDA: Facilitating Usable AI Development
In many complex applications such as healthcare, subject matter experts (e. g. Clinicians) are the ones who appreciate the importance of features that affect health, and their knowledge together with existing knowledge bases are critical to the end results.
Rafiki: Machine Learning as an Analytics Service System
Second, expertise knowledge is required to optimize the training and inference procedures in terms of efficiency and effectiveness, which imposes heavy burden on the system users.
ForkBase: An Efficient Storage Engine for Blockchain and Forkable Applications
By integrating the core application properties into the storage, ForkBase not only delivers high performance but also reduces development effort.
Databases Cryptography and Security Distributed, Parallel, and Cluster Computing
Adaptive Graph Convolutional Neural Networks
Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks.
Folding membrane proteins by deep transfer learning
Computational elucidation of membrane protein (MP) structures is challenging partially due to lack of sufficient solved structures for homology modeling.
Robust Contextual Bandit via the Capped-$\ell_{2}$ norm
In the critic updating, the capped-$\ell_{2}$ norm is used to measure the approximation error, which prevents outliers from dominating our objective.
Predicting membrane protein contacts from non-membrane proteins by deep transfer learning
Tested on 510 non-redundant MPs, our deep model (learned from only non-MPs) has top L/10 long-range contact prediction accuracy 0. 69, better than our deep model trained by only MPs (0. 63) and much better than a representative DCA method CCMpred (0. 47) and the CASP11 winner MetaPSICOV (0. 55).
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0. 5.
Deep Learning At Scale and At Ease
The other is scalability, that is the deep learning system must be able to provision for a huge demand of computing resources for training large models with massive datasets.
Protein secondary structure prediction using deep convolutional neural fields
Protein secondary structure (SS) prediction is important for studying protein structure and function.
Ranked #1 on
Protein Secondary Structure Prediction
on CullPDB
AUC-maximized Deep Convolutional Neural Fields for Sequence Labeling
Our experimental results confirm that maximum-AUC greatly outperforms the other two training methods on 8-state secondary structure prediction and disorder prediction since their label distributions are highly imbalanced and also have similar performance as the other two training methods on the solvent accessibility prediction problem which has three equally-distributed labels.
Feature Learning based Deep Supervised Hashing with Pairwise Labels
For another common application scenario with pairwise labels, there have not existed methods for simultaneous feature learning and hash-code learning.
MRFalign: Protein Homology Detection through Alignment of Markov Random Fields
A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection.
Protein Contact Prediction by Integrating Joint Evolutionary Coupling Analysis and Supervised Learning
To further improve the accuracy of the estimated precision matrices, we employ a supervised learning method to predict contact probability from a variety of evolutionary and non-evolutionary information and then incorporate the predicted probability as prior into our GGL framework.
