Mercy E. Edoho

Bio: Mercy E. Edoho is an academic researcher from University of Uyo. The author has contributed to research in topics: Human Metabolome Database & Evidence-based practice. The author has an hindex of 1, co-authored 7 publications receiving 5 citations.

A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction.

Abstract: Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database ( https://www.gisaid.org/ ), between December 2019 and January 15, 2021, a total of 8864 human SARS-CoV-2 complete genome sequences processed by gender, across 6 continents (88 countries) of the world, Antarctica exempt, were analyzed. We hypothesized that data speak for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate the emergence of inter- and intra- SARS-CoV-2 sub-strains transmission and sustain an increase in sub-strains within the various continents, with nucleotide mutations dynamically varying between individuals in close association with the virus as it adapts to its host/environment. Interestingly, some viral sub-strain patterns progressively transformed into new sub-strain clusters indicating varying amino acid, and strong nucleotide association derived from same lineage. A novel cognitive approach to knowledge mining helped the discovery of transmission routes and seamless contact tracing protocol. Our classification results were better than state-of-the-art methods, indicating a more robust system for predicting emerging or new viral sub-strain(s). The results therefore offer explanations for the growing concerns about the virus and its next wave(s). A future direction of this work is a defuzzification of confusable pattern clusters for precise intra-country SARS-CoV-2 sub-strains analytics.

Hybrid Collaborative Model for Evidence-Based Healthcare Practice

Abstract: Incorporating evidence-based healthcare practice would improve patients' response and safety and make patients partners in current healthcare practice. This partnership is certain to offer patients the opportunity to guide safety initiatives through data access by clinicians and encourage evidence-based healthcare while alleviating potential medical errors. In this paper, we promote a collaborative model that integrates interrelated concepts for responsive healthcare services that target patient-centred healthcare--with healthcare providers and relevant stakeholders in the loop. The implementation strategies for fulfilling the desired healthcare outcomes as well as design implications are also provided. The model is expected to offer transformative impact that would drive our weak healthcare system for improved healthcare and complement the huge dearth in healthcare services. The outcome is shared prosperity and health, and a mainstream of the people into healthcare decision making for informed policy planning and implementation.

Mining the Human Metabolome for Precision Oncology Research

Abstract: Access to clinical data is critical for advancing translational research; but regulatory constraints and policies surrounding the use of clinical data often challenge data access and sharing. Mixed medical datasets (structured and unstructured) are increasingly dominating the clinical information space, hence, demanding AI-driven techniques such as Natural Language Processing-to reorganize them for effective usage. This paper excavates the HMDB (Human Metabolome Database), for efficient knowledge mining, supported by diversely certified oncology physicians and pharmacists' contributions. We propose a novel taxonomy for knowledge representation and establish a universe of discourse for disease clustering and prediction. Excavated data include metabolites and their respective concentration values, age, gender, as well as gene and protein sequences, of normal and abnormal patients. These data were then merged to form an AI-ready 'Omic' technology datasets. Preliminary results reveal that the proposed AI-ready datasets would aid precision oncology research by adding quality analysis to the present HMDB, and for explaining the variations in concentration values of cancer patients.

Speech Features Analysis for Tone Language Speaker Discrimination Systems

Abstract: In this paper, a speech pattern analysis framework for tone language speaker discrimination systems is proposed. We hold the hypothesis that speech feature variability is an efficient means for discriminating speakers. To achieve this, we exploit prosody-related acoustic features (pitch, intensity and glottal pulse) of corpus recordings obtained from male and female speakers of varying age categories: children (0–15), youths (16–30), adults (31–50), seniors (above 50)—and captured under suboptimal conditions. The speaker dataset was segmented into three sets: train, validation and test set—in the ratio of 70%, 15% and 15%, respectively. A 41 × 14 self-organizing map (SOM) architecture was then used to model the speech features, thereby determining the relationship between the speech features, segments and patterns. Results of a speech pattern analysis indicated wide F0 variability amongst children speakers compared with other speakers. This gap however closes as the speaker ages. Further, the intensity variability among speakers was similar across all speaker classes/categories, while glottal pulse exhibited significant variation among the different speaker classes. Results of SOM feature visualization confirmed high inter-variability—between speakers, and low intra-variability—within speakers.

Transfer learning for non-image data in clinical research: a scoping review

Abstract: Background Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. Methods and Findings We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). Conclusions In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.

Transfer learning for non-image data in clinical research: A scoping review

Abstract: Background Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. Methods and findings We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). Conclusions In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.

A One Health strategy for emerging infectious diseases based on the COVID-19 outbreak

Abstract: Coronavirus disease 2019 (COVID-19) is as an emerging infectious disease (EID) that has caused the worst public health catastrophe of the 21st century thus far. In terms of impact, the COVID-19 pandemic is second only to the Spanish Flu pandemic of 1918 in modern world history. As of 7 September 2021, there have been 220 million confirmed cases of COVID-19 and more than 4.5 million deaths. EIDs pose serious public health and socio-economic risks, and 70% of EIDs originate from wildlife. Preventing development of EIDs such as COVID-19 is a pressing concern. Here, taking the COVID-19 pandemic as an example, we illustrate the disastrous effects of EIDs and assess their emergence and evolution from a One Health perspective. We propose a One Health strategy, centered on 'moving the gates forward', for EID prevention and control at the human-animal-environment interface. This strategy may be instructive and provide early warnings of EIDs in the future.

A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction.

Abstract: Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database ( https://www.gisaid.org/ ), between December 2019 and January 15, 2021, a total of 8864 human SARS-CoV-2 complete genome sequences processed by gender, across 6 continents (88 countries) of the world, Antarctica exempt, were analyzed. We hypothesized that data speak for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate the emergence of inter- and intra- SARS-CoV-2 sub-strains transmission and sustain an increase in sub-strains within the various continents, with nucleotide mutations dynamically varying between individuals in close association with the virus as it adapts to its host/environment. Interestingly, some viral sub-strain patterns progressively transformed into new sub-strain clusters indicating varying amino acid, and strong nucleotide association derived from same lineage. A novel cognitive approach to knowledge mining helped the discovery of transmission routes and seamless contact tracing protocol. Our classification results were better than state-of-the-art methods, indicating a more robust system for predicting emerging or new viral sub-strain(s). The results therefore offer explanations for the growing concerns about the virus and its next wave(s). A future direction of this work is a defuzzification of confusable pattern clusters for precise intra-country SARS-CoV-2 sub-strains analytics.