AIMC Topic: Machine Learning

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Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding.

Current computer-aided drug design
BACKGROUND: In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topol...

Predicting Antitumor Activity of Anthrapyrazole Derivatives using Advanced Machine Learning Techniques.

Current computer-aided drug design
BACKGROUND: Anthrapyrazoles are a new class of antitumor agents and successors to anthracyclines possessing a broad range of antitumor activity in various model tumors.

Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-learning Approaches.

Combinatorial chemistry & high throughput screening
INTRODUCTION: To understand the risk factors of asthma, we combined genome-wide association study (GWAS) risk loci and clinical data in predicting asthma using machine-learning approaches.

Computational Protein Design - Where it goes?

Current medicinal chemistry
Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an imp...

Exploring Scoring Function Space: Developing Computational Models for Drug Discovery.

Current medicinal chemistry
BACKGROUND: The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery.

Challenges involved in the application of artificial intelligence in gastroenterology: The race is on!

World journal of gastroenterology
Gastroenterology is a particularly data-rich field, generating vast repositories of data that are a fruitful ground for artificial intelligence (AI) and machine learning (ML) applications. In this opinion review, we initially elaborate on the current...

Optimizing platelet transfusion through a personalized deep learning risk assessment system for demand management.

Blood
Platelet demand management (PDM) is a resource-consuming task for physicians and transfusion managers of large hospitals. Inpatient numbers and institutional standards play significant roles in PDM. However, reliance on these factors alone commonly r...

Research Progress of Metabolomics Techniques Combined with Machine Learning Algorithm in Wound Age Estimation.

Fa yi xue za zhi
Wound age estimation is the core content in the practice of forensic medicine. Accurate estimation of wound age is a scientific question that needs to be urgently solved by forensic scientists at home and abroad. Metabolomics techniques can effective...

[Establishment of comprehensive evaluation models of physical fitness of the elderly based on machine learning].

Sheng li xue bao : [Acta physiologica Sinica]
The present study aims to establish comprehensive evaluation models of physical fitness of the elderly based on machine learning, and provide an important basis to monitor the elderly's physique. Through stratified sampling, the elderly aged 60 years...

[Application and prospect of machine learning in orthopaedic trauma].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery
OBJECTIVE: To review the current applications of machine learning in orthopaedic trauma and anticipate its future role in clinical practice.