Search

Rule-based Natural Language Processing (NLP) pipelines depend on robust domain knowledge. Given the long tail of important terminology in radiology reports, it is not uncommon for standard approaches to miss items critical for understanding the image. AI techniques can accelerate the concept expansion and phrasal grouping tasks to efficiently create a domain specific lexicon ontology for structuring reports. Using Chest X-ray (CXR) reports as an example, we demonstrate that with robust vocabulary, even a simple NLP pipeline can extract 83 directly mentioned abnormalities (Ave. recall=93.83%, precision=94.87%) and 47 abnormality/normality descriptions of key anatomies. The richer vocabulary enables identification of additional label mentions in 10 out of 13 labels (compared to baseline methods). Furthermore, it captures expert insight into critical differences between observed and inferred descriptions, and image quality issues in reports. Finally, we show how the CXR ontology can be used to anatomically structure labeled output.

Significance We adapted natural language processing to the biological literature and demonstrated end-to-end automated knowledge discovery by exploring subtle word connections. General text mining scanned 21 million publication abstracts and selected a reliable 130,000 from which hypothesis generation algorithms predicted kinases not known to phosphorylate p53, but likely to do so. Six of these p53 kinase candidates passed experimental validation. Among them NEK2 was examined in depth and shown to repress p53 and promote cell division. This work demonstrates the possibility of integrating a vast corpora of written knowledge to compute valuable hypotheses that will often test true and fuel discovery., Scientific progress depends on formulating testable hypotheses informed by the literature. In many domains, however, this model is strained because the number of research papers exceeds human readability. Here, we developed computational assistance to analyze the biomedical literature by reading PubMed abstracts to suggest new hypotheses. The approach was tested experimentally on the tumor suppressor p53 by ranking its most likely kinases, based on all available abstracts. Many of the best-ranked kinases were found to bind and phosphorylate p53 (P value = 0.005), suggesting six likely p53 kinases so far. One of these, NEK2, was studied in detail. A known mitosis promoter, NEK2 was shown to phosphorylate p53 at Ser315 in vitro and in vivo and to functionally inhibit p53. These bona fide validations of text-based predictions of p53 phosphorylation, and the discovery of an inhibitory p53 kinase of pharmaceutical interest, suggest that automated reasoning using a large body of literature can generate valuable molecular hypotheses and has the potential to accelerate scientific discovery.

Exploratory Analytics is the process of analyzing data for the purpose of forming hypotheses. Patent data sets, because they are relatively large and diverse and because they consist of a mixture of structured and unstructured information present a formidable challenge and a great opportunity in applying exploratory analytics techniques. In this paper we describe methods we have implemented for effective exploratory analytics on patent data sets using an interactive approach and a web based software tool called SIMPLE. We use real-world case studies to demonstrate the effectiveness of our exploratory analytics approach in the discovery of useful information from the patent corpus.

With billions at stake in new product development, acquisitions and alliances, IBM's Strategic IP insight Platform (SIIP) delivers transformative results, helping clients gain strategic insights. Applied to the pharmaceutical industry, SIIP accelerates the discovery of information to more quickly and accurately answer questions such as: which chemical compounds are good for which targets? What's the likelihood this compound will succeed? What diseases could be treated with this target? What are the candidate drugs that can be re-purposed for a given disease? Applied to drug discovery in life sciences, the SIIP platform leverages and integrates a wide range of public and private content, rich set of deep analytics and a massive-scale architecture to improve patient outcomes. SIIP was born prior to the proliferation of the many big data tools available today. We describe what tools and architecture decisions have been helpful in this first phase of solution development, and what tools and architectures we are relying on as we raise our own standard for performance and service delivery.

Helping locate the patents of the right technologies for licensing to prospective clients is more than one billion USD business annually to IBM. However, searching for right technologies from multiple massive data sources for a value presentation to customers is a typical human labor intensive task in the past. In this paper, we design a prospective client driven technology recommendation system to enable the automatic search of technologies for patent licensing. The idea has been to make use of knowledges from the large-scale encyclopedia Wikipedia in conjunction with 11 millions patent documents to develop an online technology recommendation system for prospective clients of IBM. The live system demands not only the fast response time but also a set of highly relevant patent documents which are technically interesting to a query prospective client.

Intellectual Properties (IP), such as patents and trademarks, are one of the most critical assets in today’s enterprises and research organizations. They represent the core innovation and differentiators of an organization. When leveraged effectively, they not only protect freedom of action, but also generate significant opportunities in licensing, execution, long term research and innovation. In this paper, we expand upon a previous paper describing a solution called SIMPLE, which mines large corpus of patents and scientific literature for insights. In this paper we focus on the interactive analytics aspects of SIMPLE, which allow the analyst to explore large unstructured information collections containing mixed information in a dynamic way. We use real-world case studies to demonstrate the effectiveness of interactive analytics in SIMPLE.

Intellectual Properties (IP), such as patents and trademarks, are one of the most critical assets in today’s enterprises and research organizations. They represent the core innovation and differentiators of an organization. When leveraged effectively, they not only protect a business from its competition, but also generate significant opportunities in licensing, execution, long term research and innovation. In certain industries, e. g., Pharmaceutical industry, patents lead to multi-billion dollar revenue per year. In this paper, we present a holistic information mining solution, called SIMPLE, which mines large corpus of patents and scientific literature for insights. Unlike much prior work that deals with specific aspects of analytics, SIMPLE is an integrated and end-to-end IP analytics solution which addresses a wide range of challenges in patent analytics such as the data complexity, scale, and nomenclature issues. It encompasses techniques for patent data processing and modeling, analytics algorithms, web interface and web services for analytics service delivery and end-user interaction. We use real-world case studies to demonstrate the effectiveness of SIMPLE.