OrthoNets: simultaneous visual analysis of orthologs and their interaction neighborhoods across different organisms.

MOTIVATION: Protein interaction networks contain a wealth of biological information, but their large size often hinders cross-organism comparisons. We present OrthoNets, a Cytoscape plugin that displays protein-protein interaction (PPI) networks from two organisms simultaneously, highlighting orthology relationships and aggregating several types of biomedical annotations. OrthoNets also allows PPI networks derived from experiments to be overlaid on networks extracted from public databases, supporting the identification and verification of new interactors. Any newly identified PPIs can be validated by checking whether their orthologs interact in another organism. AVAILABILITY: OrthoNets is freely available at http://wodaklab.org/orthonets/.

Artificial intelligence for interpretation of segments of whole body MRI in CNO: pilot study comparing radiologists versus machine learning algorithm.

BACKGROUND: To initiate the development of a machine learning algorithm capable of comparing segments of pre and post pamidronate whole body MRI scans to assess treatment response and to compare the results of this algorithm with the analysis of a panel of paediatric radiologists. METHODS: Whole body MRI of patients under the age of 16 diagnosed with CNO and treated with pamidronate at a tertiary referral paediatric hospital in United Kingdom between 2005 and 2017 were reviewed. Pre and post pamidronate images of the commonest sites of involvement (distal femur and proximal tibia) were manually selected (n = 45). A machine learning algorithm was developed and tested to assess treatment effectiveness by comparing pre and post pamidronate scans. The results of this algorithm were compared with the results of a panel of radiologists (ground truth). RESULTS: When tested initially the machine algorithm predicted 4/7 (57.1%) examples correctly in the multi class model, and 5/7 (71.4%) correctly in the binary group. However when compared to the ground truth, the machine model was able to classify only 33.3% of the samples correctly but had a sensitivity of 100% in detecting improvement or worsening of disease. CONCLUSION: The machine learning could detect new lesions or resolution of a lesion with good sensitivity but failed to classify stable disease accurately. However, further validation on larger datasets are required to improve the specificity and accuracy of the machine model.

Development of a high-resolution Y-chromosome microarray for improved male infertility diagnosis.

OBJECTIVE: To develop a novel clinical test using microarray technology as a high-resolution alternative to current methods for detection of known and novel microdeletions on the Y chromosome. DESIGN: Custom Agilent 8x15K array comparative genomic hybridization (aCGH) with 10,162 probes on an average probe spacing of 2.5 kb across the euchromatic region of the Y chromosome. SETTING: Clinical diagnostic laboratory. PATIENT(S): Men with infertility (n = 104) and controls with proven fertility (n = 148). INTERVENTION(S): Microarray genotyping of DNA. MAIN OUTCOME MEASURE(S): Gene copy number variation determined by log ratio of probe signal intensity against a DNA reference. RESULT(S): Our aCGH experiments found all known AZF microdeletions as well as additional unbalanced structural alterations. In addition to complete AZF microdeletions, we found that AZFc partial deletions represent a risk factor for male infertility. In total, aCGH-based detection achieved a diagnostic yield of ∼11% and also revealed additional potentially etiologic copy number variations requiring further characterization. CONCLUSION(S): The aCGH approach is a reliable high-resolution alternative to multiplex polymerase chain reaction for the discovery of pathogenic chromosome Y microdeletions in male infertility.