Further supporting evidence for the SATB2-associated syndrome found through whole exome sequencing.

The SATB2-associated syndrome (SAS) was recently proposed as a clinically recognizable syndrome that results from deleterious alterations of the SATB2 gene in humans. Although interstitial deletions at 2q33 encompassing SATB2, either alone or contiguously with other genes, have been reported before, there is limited literature regarding intragenic mutations of this gene and the resulting phenotype. We describe five patients in whom whole exome sequencing identified five unique de novo mutations in the SATB2 gene (one splice site, one frameshift, and three nonsense mutations). The five patients had overlapping features that support the characteristic features of the SAS: intellectual disability with limited speech development and craniofacial abnormalities including cleft palate, dysmorphic features, and dental abnormalities. Furthermore, Patient 1 also had features not previously described that represent an expansion of the phenotype. Osteopenia was seen in two of the patients, suggesting that this finding could be added to the list of distinctive findings. We provide supporting evidence that analysis for deletions or point mutations in SATB2 should be considered in children with intellectual disability and severely impaired speech, cleft or high palate, teeth abnormalities, and osteopenia.

Practical outcomes of applying ensemble machine learning classifiers to High-Throughput Screening (HTS) data analysis and screening.

Over the years numerous papers have presented the effectiveness of various machine learning methods in analyzing drug discovery biological screening data. The predictive performance of models developed using these methods has traditionally been evaluated by assessing performance of the developed models against a portion of the data randomly selected for holdout. It has been our experience that such assessments, while widely practiced, result in an optimistic assessment. This paper describes the development of a series of ensemble-based decision tree models, shares our experience at various stages in the model development process, and presents the impact of such models when they are applied to vendor offerings and the forecasted compounds are acquired and screened in the relevant assays. We have seen that well developed models can significantly increase the hit-rates observed in HTS campaigns.

Comparative study of machine-learning and chemometric tools for analysis of in-vivo high-throughput screening data.

High-throughput screening (HTS) has become a central tool of many pharmaceutical and crop-protection discovery operations. If HTS screening is carried out at the level of the intact organism, as is commonly done in crop protection, this strategy has the potential of uncovering a completely new mechanism of actions. The challenge in running a cost-effective HTS operation is to identify ways in which to improve the overall success rate in discovering new biologically active compounds. To this end, we describe our efforts directed at making full use of the data stream arising from HTS. This paper describes a comparative study in which several machine learning and chemometric methodologies were used to develop classifiers on the same data sets derived from in vivo HTS campaigns and their predictive performances compared in terms of false negative and false positive error profiles.