A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine

Unsupervised Visual Time-Series Representation Learning and Clustering

Time-series data is generated ubiquitously from Internet-of-Things (IoT) infrastructure, connected and wearable devices, remote sensing, autonomous driving research and, audio-video communications, in enormous volumes. This paper investigates the potential of unsupervised representation learning for these time-series. In this paper, we use a novel data transformation along with novel unsupervised learning regime to transfer the learning from other domains to time-series where the former have extensive models heavily trained on very large labelled datasets. We conduct extensive experiments to demonstrate the potential of the proposed approach through time-series clustering. Source code available at https://github.com/technophyte/LDVR.</p

The Role of Soil Moisture Information in Developing Robust Climate Services for Smallholder Farmers: Evidence from Ghana

In Ghana, most of the farmers are engaged in small-scale rainfed farming where the success is influenced by the prevailing weather conditions. Current Climate Information Services (CISs) only provide information on rainfall conditions to reduce their farming vulnerability to climate extremes. Access to other practical knowledge, such as soil moisture content would benefit farmers further in the decision-making process. This study aims to assess the role of soil moisture information in farmers’ agricultural decision-making and to understand how this information is being perceived, assessed, and applied. Exploratory research, combined with field visits and farmer interviews, was carried out in Gbulung, Napakzoo, and Yapalsi communities in the outskirts of Tamale, northern Ghana in October–December 2021. Results show that soil moisture information is highly important for activities, such as fertilizer application and sowing. Soil moisture information, however, is not readily available to the farmers, causing them to rely solely on their indigenous knowledge to monitor the soil moisture conditions. Our study reveals that developing a CIS embedded with soil moisture advisory module (CIS-SM) will help farmers in conducting strategic and tactical decision-making in their daily farming activities

The Role of Soil Moisture Information in Developing Robust Climate Services for Smallholder Farmers: Evidence from Ghana

In Ghana, most of the farmers are engaged in small-scale rainfed farming where the success is influenced by the prevailing weather conditions. Current Climate Information Services (CISs) only provide information on rainfall conditions to reduce their farming vulnerability to climate extremes. Access to other practical knowledge, such as soil moisture content would benefit farmers further in the decision-making process. This study aims to assess the role of soil moisture information in farmers’ agricultural decision-making and to understand how this information is being perceived, assessed, and applied. Exploratory research, combined with field visits and farmer interviews, was carried out in Gbulung, Napakzoo, and Yapalsi communities in the outskirts of Tamale, northern Ghana in October–December 2021. Results show that soil moisture information is highly important for activities, such as fertilizer application and sowing. Soil moisture information, however, is not readily available to the farmers, causing them to rely solely on their indigenous knowledge to monitor the soil moisture conditions. Our study reveals that developing a CIS embedded with soil moisture advisory module (CIS-SM) will help farmers in conducting strategic and tactical decision-making in their daily farming activities

The Role of Soil Moisture Information in Developing Robust Climate Services for Smallholder Farmers: Evidence from Ghana

In Ghana, most of the farmers are engaged in small-scale rainfed farming where the success is influenced by the prevailing weather conditions. Current Climate Information Services (CISs) only provide information on rainfall conditions to reduce their farming vulnerability to climate extremes. Access to other practical knowledge, such as soil moisture content would benefit farmers further in the decision-making process. This study aims to assess the role of soil moisture information in farmers’ agricultural decision-making and to understand how this information is being perceived, assessed, and applied. Exploratory research, combined with field visits and farmer interviews, was carried out in Gbulung, Napakzoo, and Yapalsi communities in the outskirts of Tamale, northern Ghana in October–December 2021. Results show that soil moisture information is highly important for activities, such as fertilizer application and sowing. Soil moisture information, however, is not readily available to the farmers, causing them to rely solely on their indigenous knowledge to monitor the soil moisture conditions. Our study reveals that developing a CIS embedded with soil moisture advisory module (CIS-SM) will help farmers in conducting strategic and tactical decision-making in their daily farming activities