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Found 33 papers, 10 papers with code
LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset
Using SMolInstruct, we fine-tune a set of open-source LLMs, among which, we find that Mistral serves as the best base model for chemistry tasks.
eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data
Both the ECInstruct dataset and the eCeLLM models show great potential in empowering versatile and effective LLMs for e-commerce.
Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific Adaptation
Pretrained Graph Neural Networks have been widely adopted for various molecular property prediction tasks.
Modeling Sequences as Star Graphs to Address Over-smoothing in Self-attentive Sequential Recommendation
As widely demonstrated in the literature, this issue could lead to a loss of information in individual items, and significantly degrade models' scalability and performance.
Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing
Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths.
Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization
In this work, we propose the use of contrastive learning to improve learned drug and cell line representations by preserving relationship structures associated with drug mechanism of action and cell line cancer types.
Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation
In addition, SSNA adapts the top-a layers of LLMs jointly, and integrates adapters sequentially for enhanced effectiveness (i. e., recommendation performance).
Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation
Our experimental results demonstrate that ANT does not suffer from the negative transfer issue on any of the target tasks.
RLSynC: Offline-Online Reinforcement Learning for Synthon Completion
Retrosynthesis is the process of determining the set of reactant molecules that can react to form a desired product.
Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models
Ligand-based drug design aims to identify novel drug candidates of similar shapes with known active molecules.
Precision Anti-Cancer Drug Selection via Neural Ranking
To address this, we developed neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types.
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy
This process is known as TCR recognition and constitutes a key step for immune response.
Recursive Attentive Methods with Reused Item Representations for Sequential Recommendation
Our run-time performance comparison signifies that RAM could also be more efficient on benchmark datasets.
$\mathsf{G^2Retro}$ as a Two-Step Graph Generative Models for Retrosynthesis Prediction
It first predicts the reaction centers in the target molecules (products), identifies the synthons needed to assemble the products, and transforms these synthons into reactants.
Prospective Preference Enhanced Mixed Attentive Model for Session-based Recommendation
In this manuscript, we develop prospective preference enhanced mixed attentive model (P2MAM) to generate session-based recommendations using two important factors: temporal patterns and estimates of users' prospective preferences.
Improving Compound Activity Classification via Deep Transfer and Representation Learning
In order to cope with limited training data for a target task, transfer learning for SAR modeling has been recently adopted to leverage information from data of related tasks.
Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems
Collaborative recommendation approaches based on nearest-neighbors are still highly popular today due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations.
A Deep Generative Model for Molecule Optimization via One Fragment Modification
A pipeline of multiple, identical Modof models is implemented into Modof-pipe to modify an input molecule at multiple disconnection sites.
Ranking-based Convolutional Neural Network Models for Peptide-MHC Binding Prediction
T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response.
A Pipeline to Understand Emerging Illness via Social Media Data Analysis: A Case Study on Breast Implant Illness
Our study provides the first analysis and derived knowledge of BII from social media using NLP techniques, and demonstrates the potential of using social media information to better understand similar emerging illnesses.
Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering
This method recommends relevant information from electronic health records for physicians during patient visits.
Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations
To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs.
Hybrid Collaborative Filtering Models for Clinical Search Recommendation
With increasing and extensive use of electronic health records, clinicians are often under time pressure when they need to retrieve important information efficiently among large amounts of patients' health records in clinics.
M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation
We compared M2 with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket.
HAM: Hybrid Associations Models for Sequential Recommendation
We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings.
Cognitive Biomarker Prioritization in Alzheimer's Disease using Brain Morphometric Data
This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list.
CNN-based Dual-Chain Models for Knowledge Graph Learning
We also present an extension of this model, which incorporates descriptions of entities and learns a second set of entity embeddings from the descriptions.
Drug-drug interaction prediction based on co-medication patterns and graph matching
Background: The problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered in this manuscript.
Drug Recommendation toward Safe Polypharmacy
Adverse drug reactions (ADRs) induced from high-order drug-drug interactions (DDIs) due to polypharmacy represent a significant public health problem.
Drug Selection via Joint Push and Learning to Rank
We have developed a new learning-to-rank method, denoted as pLETORg , that predicts drug ranking structures in each cell line via using drug latent vectors and cell line latent vectors.
ALE: Additive Latent Effect Models for Grade Prediction
Grade prediction methods seek to estimate a grade that a student may achieve in a course that she may take in the future (e. g., next term).
Grade Prediction with Temporal Course-wise Influence
The grade of a student on a course is modeled as the similarity of their latent representation in the "knowledge" space.
Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs
Link prediction, or predicting the likelihood of a link in a knowledge graph based on its existing state is a key research task.
