PyKale: Knowledge-aware machine learning from multiple sources in Python

PyKale is a Python library for Knowledge-aware machine learning from multiple sources of data to enable/accelerate interdisciplinary research. It embodies green machine learning principles to reduce repetitions/redundancy, reuse existing resources, and recycle learning models across areas. We propose a pipeline-based application programming interface (API) so all machine learning workflows follow a standardized six-step pipeline. PyKale focuses on leveraging knowledge from multiple sources for accurate and interpretable prediction, particularly multimodal learning and transfer learning. To be more accessible, it separates code and configurations to enable non-programmers to configure systems without coding. PyKale is officially part of the PyTorch ecosystem and includes interdisciplinary examples in bioinformatics, knowledge graph, image/video recognition, and medical imaging: https://pykale.github.io/

PyKale: knowledge-aware machine learning from multiple sources in Python

Machine learning is a general-purpose technology holding promises for many interdisciplinary research problems. However, significant barriers exist in crossing disciplinary boundaries when most machine learning tools are developed in different areas separately. We present Pykale - a Python library for knowledge-aware machine learning on graphs, images, texts, and videos to enable and accelerate interdisciplinary research. We formulate new green machine learning guidelines based on standard software engineering practices and propose a novel pipeline-based application programming interface (API). PyKale focuses on leveraging knowledge from multiple sources for accurate and interpretable prediction, thus supporting multimodal learning and transfer learning (particularly domain adaptation) with latest deep learning and dimensionality reduction models. We build PyKale on PyTorch and leverage the rich PyTorch ecosystem. Our pipeline-based API design enforces standardization and minimalism, embracing green machine learning concepts via reducing repetitions and redundancy, reusing existing resources, and recycling learning models across areas. We demonstrate its interdisciplinary nature via examples in bioinformatics, knowledge graph, image/video recognition, and medical imaging

Multiple Determinants of Externalizing Behavior in 5-Year-Olds: A Longitudinal Model

In a community sample of 116 children, assessments of parent-child interaction, parent-child attachment, and various parental, child, and contextual characteristics at 15 and 28 months and at age 5 were used to predict externalizing behavior at age 5, as rated by parents and teachers. Hierarchical multiple regression analysis and path analysis yielded a significant longitudinal model for the prediction of age 5 externalizing behavior, with independent contributions from the following predictors: child sex, partner support reported by the caregiver, disorganized infant-parent attachment at 15 months, child anger proneness at 28 months, and one of the two parent-child interaction factors observed at 28 months, namely negative parent-child interactions. The other, i.e., a lack of effective guidance, predicted externalizing problems only in highly anger-prone children. Furthermore, mediated pathways of influence were found for the parent-child interaction at 15 months (via disorganized attachment) and parental ego-resiliency (via negative parent-child interaction at 28 months)

Kindergarten children’s genetic vulnerabilities interact with friends’ aggression to promote children’s own aggression

OBJECTIVE: To examine whether kindergarten children's genetic liability to physically aggress moderates the contribution of friends' aggression to their aggressive behaviors. METHOD: Teacher and peer reports of aggression were available for 359 6-year-old twin pairs (145 MZ, 212 DZ) as well as teacher and peer reports of aggression of the two best friends of each twin. Children's genetic risk for aggression was based on their cotwin's aggression status and the pair's zygosity. RESULTS: Children's aggression was highly heritable. Unique environment accounted for most of the variance in friends' aggression, although there was also a small genetic contribution (15%). Both genetic liability to aggression and having aggressive friends predicted twins' aggression. However, the contribution of aggressive friends to children's aggression was strongest among genetically vulnerable children. This result was similar for boys and girls, despite sex differences in both aggression and the level of aggression of friends. CONCLUSIONS: Affiliation with aggressive friends at school entry is a significant environmental risk factor for aggression, especially for children genetically at risk for aggressive behaviors. Developmental models of aggression need to take into account both genetic liability and environmental factors in multiple settings, such as the peer context, to more precisely describe and understand the various developmental pathways to aggression. The implications for early prevention programs are discussed. Copyright 2007 © American Academy of Child and Adolescent Psychiatry