Latest AI and machine learning research in autism for healthcare professionals.
In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness o...
Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artifici...
A robust computational technique for model order reduction (MOR) of multi-time-scale discrete systems (single input single output (SISO) and multi-inp...
Although the prevalence of autism spectrum disorder (ASD) has risen sharply in the last few years reaching 1 in 68, the average age of diagnosis in th...
Using a pretest-posttest design, Srinivasan, et al. (2013 ) found that a period of interaction between children and an Isobot humanoid robot improved ...
We propose an automated framework for predicting gestational age (GA) and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain i...
To facilitate precision medicine and whole-genome annotation, we developed a machine-learning technique that scores how strongly genetic variants affe...
UNLABELLED: Annotating genetic variants, especially non-coding variants, for the purpose of identifying pathogenic variants remains a challenge. Combi...
Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today's explosive dat...
Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an...
Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of ...
Standard polygenic risk scores (PRSs) are constructed based on additive genome-wide association study (GWAS) summary statistics. Nonlinear machine lea...
Noisy annotations pose a significant challenge for supervised deep learning, as neural networks rely on large-scale, high-quality labeled data whose c...
Genetic evidence is a major determinant of clinical success in drug development, yet its aggregation has long relied on laborious human curation. Larg...
Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition defined by differences in social communication and restricted, repetiti...
Deciphering the regulatory consequences of sequence divergence across human evolution is essential to understanding the molecular basis of human-speci...
Acinetobacter baumannii is a high priority Gram negative opportunistic pathogen known for its high rates of multidrug resistance (MDR). Minocycline (M...
Acquiring pixel-level annotations for medical image segmentation is a severe bottleneck. Traditional U-Net architectures, while effective, learn local...
The rising prevalence of autism spectrum disorder (ASD) strains clinical infrastructure. Gold-standard tools like ADOS-2 face high costs, specialized ...
Cell Painting microscopy captures how cells change after a chemical or genetic perturbation. Connecting these images to the perturbations that produce...