Karin Verspoor (FAIDH)

Even without generally intelligent artificial systems, we have powerful tools that give us access to insights of unprecedented scale and complexity into patterns of human behaviour, writes Karin Verspoor.

Rear Admiral Dr Grace Murray Hopper was a significant figure in the history of computing. Her work revolutionised how we program today.

From early childhood, Hopper had a strong interest in how things worked, gaining a reputation for dismantling her family’s alarm clocks.

Her talent for mathematics led her to pursue research at Yale, where she gained her PhD in 1934. She also had a distinguished naval career during World War II, where she was one of the first programmers of the… Show more

Rear Admiral Dr Grace Murray Hopper was a significant figure in the history of computing. Her work revolutionised how we program today.

From early childhood, Hopper had a strong interest in how things worked, gaining a reputation for dismantling her family’s alarm clocks.

Her talent for mathematics led her to pursue research at Yale, where she gained her PhD in 1934. She also had a distinguished naval career during World War II, where she was one of the first programmers of the Navy’s IBM Mark I computer.

She famously coined the term “debugging the machine” after a moth found its way into her computer.

Hopper strongly believed that programs should be written in a language that was close to English, an idea that was rejected by the computing establishment at the time. Yet she and her staff proved them wrong, successfully making the UNIVAC computer “understand” 20 statements in English. Hopper’s work led to the development of the English-based programming language COBOL (Common Business Oriented Language).
Grace Hopper helped to move us from giving computers instructions via cryptic machine code to a human readable “higher level” programming that is closer to natural language instructions. Her work was vital to the field of computing, allowing us to express the logic of a program without having to translate it into numbers. Show less

Media piece on women in IT.

Ontological concepts are useful for many different biomedical tasks. Concepts are difficult to recognize in text due to a disconnect between what is captured in an ontology and how the concepts are expressed in text. There are many recognizers for specific ontologies, but a general approach for concept recognition is an open problem.

Three dictionary-based systems (MetaMap, NCBO Annotator, and ConceptMapper) are evaluated on eight biomedical ontologies in the Colorado Richly Annotated… Show more

Ontological concepts are useful for many different biomedical tasks. Concepts are difficult to recognize in text due to a disconnect between what is captured in an ontology and how the concepts are expressed in text. There are many recognizers for specific ontologies, but a general approach for concept recognition is an open problem.

Three dictionary-based systems (MetaMap, NCBO Annotator, and ConceptMapper) are evaluated on eight biomedical ontologies in the Colorado Richly Annotated Full-Text (CRAFT) Corpus. Over 1,000 parameter combinations are examined, and best-performing parameters for each system-ontology pair are presented.

Baselines for concept recognition by three systems on eight biomedical ontologies are established (F-measures range from 0.14–0.83). Out of the three systems we tested, ConceptMapper is generally the best-performing system; it produces the highest F-measure of seven out of eight ontologies. Default parameters are not ideal for most systems on most ontologies; by changing parameters F-measure can be increased by up to 0.4. Not only are best performing parameters presented, but suggestions for choosing the best parameters based on ontology characteristics are presented. Show less

Article looking at the need to explore supplementary information associated with publications, to find information on genetic variation.

In: Data-Intensive Science, Eds. T. Critchlow and K. Kleese van Dam

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are… Show more

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. Show less