Clinical diagnosis of amyotrophic lateral sclerosis (ALS) is difficult in the early period. But blood tests are less time consuming and low cost methods compared to other methods for the diagnosis. The ALS researchers have been used machine learning methods to predict the genetic architecture of disease. In this study we take advantages of Bayesian networks and machine learning methods to predict the ALS patients with blood plasma protein level and independent personal features. According to the comparison results, Bayesian Networks produced best results with accuracy (0.887), area under the curve (AUC) (0.970) and other comparison metrics. We confirmed that sex and age are effective variables on the ALS. In addition, we found that the probability of onset involvement in the ALS patients is very high. Also, a person’s other chronic or neurological diseases are associated with the ALS disease. Finally, we confirmed that the Parkin level may also have an effect on the ALS disease. While this protein is at very low levels in Parkinson’s patients, it is higher in the ALS patients than all control groups.

The obligate biotrophic fungus, Puccinia triticina ( Pt ), is a notorious plant pathogen known for wheat leaf rust disease that devastates global crop production annually. Pathogen-host interactions need to be elucidated, especially the identification of pathogen effectors contributing to virulence. ‘Omics’ strategies are an efficient tool set to study these pathogen-host interactions. No proteome data are available on the germlings stage of Pt , which could highlight proteins involved in the first interaction point between Pt and its host. Here, we have used a proteomics approach on germinated urediniospores of Pt to define the germling proteome. Using two-dimensional gel electrophoresis, 167 spots in the pI range of 4–7 were observed, from which 123 proteins were identified by tandem mass spectrometry. Further in silico characterization revealed that they were predominantly involved in metabolic and cellular processes, with six proteins annotated as candidate effectors. The candidate effector PTTG_06852, hereafter named as virulence factor 1 (PtVF1), was silenced by host-induced gene silencing (HIGS) to investigate its biological function. When the expression of PtVF1 was reduced by 78%, the infection severity was suppressed by 70%. The results demonstrate a significant impairment of virulence upon silencing, which validates PtVF1 as a virulence effector of Pt .