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.
Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths.
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.
In addition, SSNA adapts the top-a layers of LLMs jointly, and integrates adapters sequentially for enhanced effectiveness (i. e., recommendation performance).
Our experimental results demonstrate that ANT does not suffer from the negative transfer issue on any of the target tasks.
Ligand-based drug design aims to identify novel drug candidates of similar shapes with known active molecules.
To address this, we developed neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types.
This process is known as TCR recognition and constitutes a key step for immune response.
Our run-time performance comparison signifies that RAM could also be more efficient on benchmark datasets.
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.
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.
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.
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 pipeline of multiple, identical Modof models is implemented into Modof-pipe to modify an input molecule at multiple disconnection sites.
T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response.
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.
This method recommends relevant information from electronic health records for physicians during patient visits.
To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs.
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.
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.
We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings.
This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list.
We also present an extension of this model, which incorporates descriptions of entities and learns a second set of entity embeddings from the descriptions.
Background: The problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered in this manuscript.
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.
Link prediction, or predicting the likelihood of a link in a knowledge graph based on its existing state is a key research task.