Predicting of Drug-Disease Associations via Sparse Auto-Encoder-Based Rotation Forest

: Recently, an increasing number of biological and clinical reports have demonstrated that imbalance of microbial community has the ability to play important roles among several complex diseases concerning human health. Having a good knowledge of discovering potential of microbe-disease relationships, which provides the ability to having a better understanding of some issues, including disease pathology, further boosts disease diagnostics and prognostics, has been taken into account. Nevertheless, a few computational approaches can meet the need of huge scale of microbe-disease association discovery. In this work, we proposed the EHAI model, which is Enhanced Human microbe- disease Association Identification. EHAI employed the microbe-disease associations, and then Gaussian interaction profile kernel similarity has been utilized to enhance the basic microbe-disease association. Actually, some known microbe-disease associations and a large amount of associations are still unavailable among the datasets. The ‘super-microbe’ and ‘super-disease’ were employed to enhance the model. Computational results demonstrated that such super-classes have the ability to be helpful to the performance of EHAI. Therefore, it is anticipated that EHAI can be treated as an important biological tool in this field.

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Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

Current Gene Therapy ◽

10.2174/1566523219666190917155959 ◽

2019 ◽

Vol 19 (4) ◽

pp. 232-241 ◽

Cited By ~ 5

Author(s):

Xuegong Chen ◽

Wanwan Shi ◽

Lei Deng

Keyword(s):

Protein Interactions ◽

Experimental Studies ◽

Treatment Strategies ◽

Computational Method ◽

Biological Information ◽

Support Vector ◽

Protein Protein Interactions ◽

Efficient Treatment ◽

Disease Associations ◽

Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

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Social Mixing and Clinical Features Linked With Transmission in a Network of Extensively Drug-resistant Tuberculosis Cases in KwaZulu-Natal, South Africa

Clinical Infectious Diseases ◽

10.1093/cid/ciz636 ◽

2019 ◽

Vol 70 (11) ◽

pp. 2396-2402

Author(s):

Kristin N Nelson ◽

Samuel M Jenness ◽

Barun Mathema ◽

Benjamin A Lopman ◽

Sara C Auld ◽

...

Keyword(s):

Clinical Features ◽

Urban Areas ◽

Sputum Smear ◽

Drug Resistant ◽

Resistant Tuberculosis ◽

Extensively Drug Resistant ◽

Social Mixing ◽

Kwazulu Natal ◽

Disease Associations ◽

Smear Negative

Abstract Background Tuberculosis (TB) is the leading infectious cause of death globally, and drug-resistant TB strains pose a serious threat to controlling the global TB epidemic. The clinical features, locations, and social factors driving transmission in settings with high incidences of drug-resistant TB are poorly understood. Methods We measured a network of genomic links using Mycobacterium tuberculosis whole-genome sequences. Results Patients with 2–3 months of cough or who spent time in urban locations were more likely to be linked in the network, while patients with sputum smear–positive disease were less likely to be linked than those with smear-negative disease. Associations persisted using different thresholds to define genomic links and irrespective of assumptions about the direction of transmission. Conclusions Identifying factors that lead to many transmissions, including contact with urban areas, can suggest settings instrumental in transmission and indicate optimal locations and groups to target with interventions.

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A young man presenting with encephalopathy and seizures secondary to SARS-CoV-2

BMJ Case Reports ◽

10.1136/bcr-2020-240576 ◽

2021 ◽

Vol 14 (3) ◽

pp. e240576

Author(s):

Bilal Athar Jalil ◽

Mohsin Ijaz ◽

Amir Maqbul Khan ◽

Thomas Glenn Ledbetter

Keyword(s):

Respiratory Illness ◽

Viral Meningitis ◽

The Other ◽

New Disease ◽

Unique Case ◽

Multisystem Involvement ◽

Disease Associations ◽

Viral Illness ◽

Structural Brain ◽

New Onset

COVID-19 has now emerged from a respiratory illness to a systemic viral illness with multisystem involvement. There is still a lot to learn about this illness as new disease associations with COVID-19 emerge consistently. We present a unique case of a neurological manifestation of a patient with structural brain disease who was COVID-19 positive and developed mental status changes, new-onset seizures and findings suggestive of viral meningitis on lumbar puncture. We also review the literature and discuss our case in the context of the other cases reported. We highlight the value of considering seizures and encephalopathy as one of the presenting features of COVID-19 disease.

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Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China

Remote Sensing ◽

10.3390/rs13020238 ◽

2021 ◽

Vol 13 (2) ◽

pp. 238

Author(s):

Zhice Fang ◽

Yi Wang ◽

Gonghao Duan ◽

Ling Peng

Keyword(s):

Decision Trees ◽

Landslide Susceptibility ◽

Ensemble Methods ◽

Landslide Susceptibility Mapping ◽

Three Gorges Reservoir Area ◽

Ratio Method ◽

Susceptibility Map ◽

Rotation Forest ◽

Predictive Values ◽

Ensemble Technique

This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique. The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations. Then, landslide conditioning factors are selected and screened by the gain ratio method. Next, several training subsets are produced from the training set and a series of trained DTs are obtained by using a DT as a base classifier couple with different training subsets. Finally, the resultant landslide susceptibility map is produced by combining all the DT classification results using the RF ensemble technique. Experimental results demonstrate that the performance of all the DTs can be effectively improved by integrating them with the RF ensemble technique. Specifically, the proposed ensemble methods achieved the predictive values of 0.012–0.121 higher than the DTs in terms of area under the curve (AUC). Furthermore, the proposed ensemble methods are better than the most popular ensemble methods with the predictive values of 0.005–0.083 in terms of AUC. Therefore, the proposed ensemble framework is effective to further improve the spatial prediction of landslides.

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