AIMC Topic: Algorithms

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A new computational intelligence approach to detect autistic features for autism screening.

International journal of medical informatics
Autism Spectrum Disorder (ASD) is one of the fastest growing developmental disability diagnosis. General practitioners (GPs) and family physicians are typically the first point of contact for patients or family members concerned with ASD traits obser...

Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies.

Journal of chemical information and modeling
Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturba...

Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions.

PloS one
Protein-protein interactions integrated with disease-gene associations represent important information for revealing protein functions under disease conditions to improve the prevention, diagnosis, and treatment of complex diseases. Although several ...

A meta-learning framework using representation learning to predict drug-drug interaction.

Journal of biomedical informatics
MOTIVATION: Predicting Drug-Drug Interaction (DDI) has become a crucial step in the drug discovery and development process, owing to the rise in the number of drugs co-administered with other drugs. Consequently, the usage of computational methods fo...

Optimal dynamic regimens with artificial intelligence: The case of temozolomide.

PloS one
We determine an optimal protocol for temozolomide using population variability and dynamic optimization techniques inspired by artificial intelligence. We use a Pharmacokinetics/Pharmacodynamics (PK/PD) model based on Faivre and coauthors (Faivre, et...

Drug Selection via Joint Push and Learning to Rank.

IEEE/ACM transactions on computational biology and bioinformatics
Selecting the right drugs for the right patients is a primary goal of precision medicine. In this article, we consider the problem of cancer drug selection in a learning-to-rank framework. We have formulated the cancer drug selection problem as to ac...

Multi-Domain Networks Association for Biological Data Using Block Signed Graph Clustering.

IEEE/ACM transactions on computational biology and bioinformatics
Multi-domain biological network association and clustering have attracted a lot of attention in biological data integration and understanding, which can provide a more global and accurate understanding of biological phenomenon. In many problems, diff...

Weakly Supervised Biomedical Image Segmentation by Reiterative Learning.

IEEE journal of biomedical and health informatics
Recent advances in deep learning have produced encouraging results for biomedical image segmentation; however, outcomes rely heavily on comprehensive annotation. In this paper, we propose a neural network architecture and a new algorithm, known as ov...

Natural Language Processing for EHR-Based Computational Phenotyping.

IEEE/ACM transactions on computational biology and bioinformatics
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categorization, no...

Deep Recurrent Neural Networks for Prostate Cancer Detection: Analysis of Temporal Enhanced Ultrasound.

IEEE transactions on medical imaging
Temporal enhanced ultrasound (TeUS), comprising the analysis of variations in backscattered signals from a tissue over a sequence of ultrasound frames, has been previously proposed as a new paradigm for tissue characterization. In this paper, we prop...