AIMC Topic: Machine Learning

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A machine learning strategy with clustering under sampling of majority instances for predicting drug target interactions.

Molecular informatics
Drug Target Interactions (DTIs) are crucial in drug discovery as it reduces the range of candidate searches, speeding up the drug screening process. Considering in vitro and in vivo experimentations are time and cost-expensive, there has been a surge...

Artificial intelligence in multi-objective drug design.

Current opinion in structural biology
The factors determining a drug's success are manifold, making de novo drug design an inherently multi-objective optimisation (MOO) problem. With the advent of machine learning and optimisation methods, the field of multi-objective compound design has...

MGREL: A multi-graph representation learning-based ensemble learning method for gene-disease association prediction.

Computers in biology and medicine
The identification of gene-disease associations plays an important role in the exploration of pathogenic mechanisms and therapeutic targets. Computational methods have been regarded as an effective way to discover the potential gene-disease associati...

Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review.

Computers in biology and medicine
BACKGROUND: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. V...

Machine learning-based colorectal cancer prediction using global dietary data.

BMC cancer
BACKGROUND: Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Active health screening for CRC yielded detection of an increasingly younger adults. However, current machine learning algorithms that are trained using older ...

Developing a machine learning algorithm to predict probability of retear and functional outcomes in patients undergoing rotator cuff repair surgery: protocol for a retrospective, multicentre study.

BMJ open
INTRODUCTION: The effectiveness of rotator cuff tear repair surgery is influenced by multiple patient-related, pathology-centred and technical factors, which is thought to contribute to the reported retear rates between 17% and 94%. Adequate patient ...

PTGAC Model: A machine learning approach for constructing phylogenetic tree to compare protein sequences.

Journal of bioinformatics and computational biology
This work proposes a machine learning-based phylogenetic tree generation model based on agglomerative clustering (PTGAC) that compares protein sequences considering all known chemical properties of amino acids. The proposed model can serve as a suita...

Insights and trends review: artificial intelligence in hand surgery.

The Journal of hand surgery, European volume
Artificial intelligence (AI) in hand surgery is an emerging and evolving field that will likely play a large role in the future care of our patients. However, there remain several challenges to makes this technology meaningful, acceptable and usable ...

Neural network model for imprecise regression with interval dependent variables.

Neural networks : the official journal of the International Neural Network Society
This paper presents a computationally feasible method to compute rigorous bounds on the interval-generalization of regression analysis to account for epistemic uncertainty in the output variables. The new iterative method uses machine learning algori...