Vanessa V. Michelini

Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma.

Objective: To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each. Methods: Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs. Results: More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts. Conclusions: The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible. ClinicalTrials.gov identifier: NCT02725684.

Dynamically adjusting speech menu presentation style

A method for adjusting a speech menu interface in a speech recognition system. The method can include a series of steps which can include identifying one or more menu items from a data structure in memory for presentation using the speech menu interface. The step of retrieving the one or more menu items from the data structure can be included. Also, the step of reading at least one attribute corresponding to the one or more menu items with menu item criteria can be included. Based on the comparing step, the method can include selecting a presentation style for presentation of the one or more menu items using the speech menu interface.

Enhancing Next‐Generation Sequencing‐Guided Cancer Care Through Cognitive Computing

Next‐generation sequencing (NGS) has emerged as an affordable and reproducible means to query tumors for somatic genetic anomalies. To help interpret somatic NGS data, many institutions have created a molecular tumor board to analyze the results of NGS and make recommendations. This article evaluates the utility of cognitive computing systems to analyze data for clinical decision‐making.

Managing sensitive applications in the public cloud

Protecting the security and privacy of data is a paramount concern of enterprises in medical, educational, financial, and other highly regulated industries. While some industries have moved rapidly to take advantage of the cost savings, innovations in data analysis, and many benefits provided by cloud platforms, regulated enterprises with sensitive data have proceeded with caution. In this paper, we explore a fully public cloud-based architecture that is able to handle both service requirements and security requirements. In such a public cloud environment, the traditional notion of static perimeter-based reactive security can leave internal system components vulnerable to accidental data disclosures or malicious attacks originating from within the perimeter. Therefore, ensuring security and compliance of such a solution requires innovation and new approaches in several directions, including proactive log monitoring and analysis of virtually all parts of the cloud-based solution, full end-to-end data encryption from the client through Internet transmission to data storage and analytics in the solution, and robust firewall and network-intrusion detection systems. We discuss many of these techniques as applied to a specific real-world application known as the Watson Genomic Analytics Prototype.