Microplastic predictive modelling with the integration of Artificial Neural Networks and Hidden Markov Models (ANN-HMM).

Microplastic pollution poses a significant threat to our environment, necessitating effective predictive modelling approaches for better management and mitigation. In this study, we introduce a pioneering methodology that fuses the power of Artificial Neural Networks (ANN) and Hidden Markov Models (HMM) for microplastic predictive modelling. Leveraging a comprehensive dataset, our integrated model exhibits exceptional performance, with an Accuracy of 0.

Discordance in Recommendation Between Next-Generation Sequencing Test Reports and Molecular Tumor Boards in India.

Purpose: Accurate understanding of the genomic and transcriptomic data provided by next-generation sequencing (NGS) is essential for the effective utilization of precision oncology. Molecular tumor boards (MTBs) aim to translate the complex data in NGS reports into effective clinical interventions. Often, MTB treatment recommendations differ from those in the NGS reports.

Multi-omics Analysis Classifies Colorectal Cancer into Distinct Methylated Immunogenic and Angiogenic Subtypes Based on Anatomical Laterality.

Unlabelled: We employed supervised machine learning algorithms to a cohort of colorectal cancer patients from the NCI to differentiate and classify the heterogenous disease based on anatomical laterality and multi-omics stratification, in a first of its kind. Multi-omics integrative analysis shows distinct clustering of left and right colorectal cancer with disentangled representation of methylome and delineation of transcriptome and genome. We present novel multi-omics findings consistent with augmented hypermethylation of genes in right CRC, epigenomic biomarkers on the right in conjunction with immune-mediated pathway signatures, and lymphocytic invasion which unlocks unique therapeutic avenues.