Search Results for author: Xia Ning

Found 30 papers, 7 papers with code

Modeling Sequences as Star Graphs to Address Over-smoothing in Self-attentive Sequential Recommendation

no code implementations13 Nov 2023 Bo Peng, Ziqi Chen, Srinivasan Parthasarathy, Xia Ning

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.

Sequential Recommendation

Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization

no code implementations20 Oct 2023 Patrick J. Lawrence, Xia Ning

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.

Contrastive Learning

Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation

no code implementations2 Oct 2023 Bo Peng, Ben Burns, Ziqi Chen, Srinivasan Parthasarathy, Xia Ning

In addition, SSNA adapts the top-a layers of LLMs jointly, and integrates adapters sequentially for enhanced effectiveness (i. e., recommendation performance).

Sequential Recommendation

Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation

no code implementations18 Sep 2023 Bo Peng, Srinivasan Parthasarathy, Xia Ning

Our experimental results demonstrate that ANT does not suffer from the negative transfer issue on any of the target tasks.

Sequential Recommendation

Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models

no code implementations23 Aug 2023 Ziqi Chen, Bo Peng, Srinivasan Parthasarathy, Xia Ning

Ligand-based drug design aims to identify novel drug candidates of similar shapes with known active molecules.

3D Molecule Generation

Precision Anti-Cancer Drug Selection via Neural Ranking

1 code implementation30 Jun 2023 Vishal Dey, Xia Ning

To address this, we developed neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types.

Drug Response Prediction

Recursive Attentive Methods with Reused Item Representations for Sequential Recommendation

no code implementations16 Sep 2022 Bo Peng, Srinivasan Parthasarathy, Xia Ning

Our run-time performance comparison signifies that RAM could also be more efficient on benchmark datasets.

Sequential Recommendation

$\mathsf{G^2Retro}$ as a Two-Step Graph Generative Models for Retrosynthesis Prediction

1 code implementation10 Jun 2022 Ziqi Chen, Oluwatosin R. Ayinde, James R. Fuchs, Huan Sun, Xia Ning

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.

Retrosynthesis Vocal Bursts Valence Prediction

Prospective Preference Enhanced Mixed Attentive Model for Session-based Recommendation

no code implementations4 Jun 2022 Bo Peng, Chang-Yu Tai, Srinivasan Parthasarathy, Xia Ning

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.

Session-Based Recommendations

Improving Compound Activity Classification via Deep Transfer and Representation Learning

1 code implementation14 Nov 2021 Vishal Dey, Raghu Machiraju, Xia Ning

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.

Drug Discovery Representation Learning +1

Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems

no code implementations9 Sep 2021 Athanasios N. Nikolakopoulos, Xia Ning, Christian Desrosiers, George Karypis

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.

Recommendation Systems

A Deep Generative Model for Molecule Optimization via One Fragment Modification

2 code implementations8 Dec 2020 Ziqi Chen, Martin Renqiang Min, Srinivasan Parthasarathy, Xia Ning

A pipeline of multiple, identical Modof models is implemented into Modof-pipe to modify an input molecule at multiple disconnection sites.

Drug Discovery

Ranking-based Convolutional Neural Network Models for Peptide-MHC Binding Prediction

1 code implementation4 Dec 2020 Ziqi Chen, Martin Renqiang Min, Xia Ning

T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response.

MHC presentation prediction

A Pipeline to Understand Emerging Illness via Social Media Data Analysis: A Case Study on Breast Implant Illness

no code implementations25 Aug 2020 Vishal Dey, Peter Krasniak, Minh Nguyen, Clara Lee, Xia Ning

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.

Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations

no code implementations12 Aug 2020 Yonghyun Nam, Jae-Seung Yun, Seung Mi Lee, Ji Won Park, Ziqi Chen, Brian Lee, Anurag Verma, Xia Ning, Li Shen, Dokyoon Kim

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

no code implementations19 Jul 2020 Zhiyun Ren, Bo Peng, Titus K. Schleyer, Xia Ning

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.

Collaborative Filtering Recommendation Systems

M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation

3 code implementations3 Apr 2020 Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy, Xia Ning

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.

Next-basket recommendation

HAM: Hybrid Associations Models for Sequential Recommendation

2 code implementations27 Feb 2020 Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy, Xia Ning

We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings.

Sequential Recommendation

Cognitive Biomarker Prioritization in Alzheimer's Disease using Brain Morphometric Data

no code implementations18 Feb 2020 Bo Peng, Xiaohui Yao, Shannon L. Risacher, Andrew J. Saykin, Li Shen, Xia Ning

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

no code implementations15 Nov 2019 Bo Peng, Renqiang Min, Xia Ning

We also present an extension of this model, which incorporates descriptions of entities and learns a second set of entity embeddings from the descriptions.

Entity Embeddings Graph Learning

Drug-drug interaction prediction based on co-medication patterns and graph matching

no code implementations22 Feb 2019 Wen-Hao Chiang, Li Shen, Lang Li, Xia Ning

Background: The problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered in this manuscript.

Graph Matching

Drug Recommendation toward Safe Polypharmacy

no code implementations8 Mar 2018 Wen-Hao Chiang, Li Shen, Lang Li, Xia Ning

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

no code implementations23 Jan 2018 Yicheng He, Junfeng Liu, Xia Ning

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

no code implementations17 Jan 2018 Zhiyun Ren, Xia Ning, Huzefa Rangwala

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

no code implementations15 Sep 2017 Zhiyun Ren, Xia Ning, Huzefa Rangwala

The grade of a student on a course is modeled as the similarity of their latent representation in the "knowledge" space.

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