Machine learning to analyse omic-data for COVID-19 diagnosis and prognosis.

BACKGROUND: With the global spread of COVID-19, the world has seen many patients, including many severe cases. The rapid development of machine learning (ML) has made significant disease diagnosis and prediction achievements. Current studies have confirmed that omics data at the host level can reflect the development process and prognosis of the disease. Since early diagnosis and effective treatment of severe COVID-19 patients remains challenging, this research aims to use omics data in different ML models for COVID-19 diagnosis and prognosis. We used several ML models on omics data of a large number of individuals to first predict whether patients are COVID-19 positive or negative, followed by the severity of the disease. RESULTS: On the COVID-19 diagnosis task, we got the best AUC of 0.99 with our multilayer perceptron model and the highest F1-score of 0.95 with our logistic regression (LR) model. For the severity prediction task, we achieved the highest accuracy of 0.76 with an LR model. Beyond classification and predictive modeling, our study founds ML models performed better on integrated multi-omics data, rather than single omics. By comparing top features from different omics dataset, we also found the robustness of our model, with a wider range of applicability in diverse dataset related to COVID-19. Additionally, we have found that omics-based models performed better than image or physiological feature-based models, proving the importance of the omics-based dataset for future model development. CONCLUSIONS: This study diagnoses COVID-19 positive cases and predicts accurate severity levels. It lowers the dependence on clinical data and professional judgment, by leveraging the utilization of state-of-the-art models. our model showed wider applicability across different omics dataset, which is highly transferable in other respiratory or similar diseases. Hospital and public health care mechanisms can optimize the distribution of medical resources and improve the robustness of the medical system.

An open set model for pest identification.

Agricultural pest identification is a prerequisite for increasing crop production and meeting global food demands. Generally, numerous phenotypic and genotypic features are widely utilized for species-level pest identification. However, the approaches are time-consuming and require expert knowledge in relevant fields. Numerous image-based machine learning (ML) models also exist to identify insect pests in agricultural fields. The models are significantly rely on a large, manually curated dataset and are close-set in nature. Our study aims to develop an open set pest identification approach by adding the capability of rejecting any irrelevant inputs. Tephritid fruit flies (Diptera:Tephritidae) are considered as an example since they are the most economically important agricultural pests worldwide. Images of the fruit flies were collected from a publicly available database and filtered to exclude uninformative images using a deep learning model (Inception-V3) and an unsupervised k-means clustering method. For the closed-set identification task, our EfficientNet-B2 model classified four major genera of notorious tephritid flies, namely, Anastrepha, Ceratitis, Rhagoletis, and Bactrocera with an accuracy of 89.65%. We further improvise our proposed model for open-set recognition tasks to leverage the identification beyond the trained datasets. The open set model achieved an overall accuracy of 86.48% and a macro F1-score of 94.44% on the four genera and an unknown class. Our proposed model can be a practical and effective pest identification tool for harmful fruit flies. In addition, the model is easy to implement with existing agricultural pest control systems in an open-world scenario.

Complete mitogenome and intra-family comparative mitogenomics showed distinct position of Pama Croaker Otolithoides pama.

The Pama Croaker, Otolithoides pama, is an economically important fish species in Bangladesh. Intra-family similarities in morphology and typical barcode sequences of cox1 create ambiguities in its identification. Therefore, morphology and the complete mitochondrial genome of O. pama, and comparative mitogenomics within the family Sciaenidae have been studied. Extracted genomic DNA was subjected to Illumina-based short read sequencing for De-Novo mitogenome assembly. The complete mitogenome of O. pama (Accession: OQ784575.1) was 16,513 bp, with strong AC biasness and strand asymmetry. Relative synonymous codon usage (RSCU) among 13 protein-coding genes (PCGs) of O. pama was also analyzed. The studied mitogenomes including O. pama exhibited consistent sizes and gene orders, except for the genus Johnius which possessed notably longer mitogenomes with unique gene rearrangements. Different genetic distance metrics across 30 species of Sciaenidae family demonstrated 12S rRNA and the control region (CR) as the most conserved and variable regions, respectively, while most of the PCGs undergone a purifying selection. Different phylogenetic trees were congruent with one another, where O. pama was distinctly placed. This study would contribute to distinguishing closely related fish species of Sciaenidae family and can be instrumental in conserving the genetic diversity of O. pama.

Improved reference quality genome sequence of the plastic-degrading greater wax moth, Galleria mellonella.

Galleria mellonella is a pest of honeybees in many countries because its larvae feed on beeswax. However, G. mellonella larvae can also eat various plastics, including polyethylene, polystyrene, and polypropylene, and therefore, the species is garnering increasing interest as a tool for plastic biodegradation research. This paper presents an improved genome (99.3% completed lepidoptera_odb10 BUSCO; genome mode) for G. mellonella. This 472 Mb genome is in 221 contigs with an N50 of 6.4 Mb and contains 13,604 protein-coding genes. Genes that code for known and putative polyethylene-degrading enzymes and their similarity to proteins found in other Lepidoptera are highlighted. An analysis of secretory proteins more likely to be involved in the plastic catabolic process has also been carried out.

Machine learning for detecting DNA attachment on SPR biosensor.

Optoelectric biosensors measure the conformational changes of biomolecules and their molecular interactions, allowing researchers to use them in different biomedical diagnostics and analysis activities. Among different biosensors, surface plasmon resonance (SPR)-based biosensors utilize label-free and gold-based plasmonic principles with high precision and accuracy, allowing these gold-based biosensors as one of the preferred methods. The dataset generated from these biosensors are being used in different machine learning (ML) models for disease diagnosis and prognosis, but there is a scarcity of models to develop or assess the accuracy of SPR-based biosensors and ensure a reliable dataset for downstream model development. Current study proposed innovative ML-based DNA detection and classification models from the reflective light angles on different gold surfaces of biosensors and associated properties. We have conducted several statistical analyses and different visualization techniques to evaluate the SPR-based dataset and applied t-SNE feature extraction and min-max normalization to differentiate classifiers of low-variances. We experimented with several ML classifiers, namely support vector machine (SVM), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbors (KNN), logistic regression (LR) and random forest (RF) and evaluated our findings in terms of different evaluation metrics. Our analysis showed the best accuracy of 0.94 by RF, DT and KNN for DNA classification and 0.96 by RF and KNN for DNA detection tasks. Considering area under the receiver operating characteristic curve (AUC) (0.97), precision (0.96) and F1-score (0.97), we found RF performed best for both tasks. Our research shows the potentiality of ML models in the field of biosensor development, which can be expanded to develop novel disease diagnosis and prognosis tools in the future.

A ResNet attention model for classifying mosquitoes from wing-beating sounds.

Mosquitoes are vectors of numerous deadly diseases, and mosquito classification task is vital for their control programs. To ease manual labor and time-consuming classification tasks, numerous image-based machine-learning (ML) models have been developed to classify different mosquito species. Mosquito wing-beating sounds can serve as a unique classifier for mosquito classification tasks, which can be adopted easily in field applications. The current study aims to develop a deep neural network model to identify six mosquito species of three different genera, based on their wing-beating sounds. While existing models focused on raw audios, we developed a comprehensive pre-processing step to convert raw audios into more informative Mel-spectrograms, resulting in more robust and noise-free extracted features. Our model, namely 'Wing-beating Network' or 'WbNet', combines the state-of-art residual neural network (ResNet) model as a baseline, with self-attention mechanism and data-augmentation technique, and outperformed other existing models. The WbNet achieved the highest performance of 89.9% and 98.9% for WINGBEATS and ABUZZ data respectively. For species of Aedes and Culex genera, our model achieved 100% precision, recall and F1-scores, whereas, for Anopheles, the WbNet reached above 95%. We also compared two existing wing-beating datasets, namely WINGBEATS and ABUZZ, and found our model does not need sophisticated audio devices, hence performed better on ABUZZ audios, captured on usual mobile devices. Overall, our model has potential to serve in mosquito monitoring and prevalence studies in mosquito eradication programs, along with potential implementation in classification tasks of insect pests or other sound-based classifications.

Population differences and domestication effects on mating and remating frequencies in Queensland fruit fly.

Females of many insect species are unreceptive to remating for a period following their first mating. This inhibitory effect may be mediated by either the female or her first mate, or both, and often reflects the complex interplay of reproductive strategies between the sexes. Natural variation in remating inhibition and how this phenotype responds to captive breeding are largely unexplored in insects, including many pest species. We investigated genetic variation in remating propensity in the Queensland fruit fly, Bactrocera tryoni, using strains differing in source locality and degree of domestication. We found up to threefold inherited variation between strains from different localities in the level of intra-strain remating inhibition. The level of inhibition also declined significantly during domestication, which implied the existence of genetic variation for this trait within the starting populations as well. Inter-strain mating and remating trials showed that the strain differences were mainly due to the genotypes of the female and, to a lesser extent, the second male, with little effect of the initial male genotype. Implications for our understanding of fruit fly reproductive biology and population genetics and the design of Sterile Insect Technique pest management programs are discussed.