43,085 research outputs found
How to understand the cell by breaking it: network analysis of gene perturbation screens
Modern high-throughput gene perturbation screens are key technologies at the
forefront of genetic research. Combined with rich phenotypic descriptors they
enable researchers to observe detailed cellular reactions to experimental
perturbations on a genome-wide scale. This review surveys the current
state-of-the-art in analyzing perturbation screens from a network point of
view. We describe approaches to make the step from the parts list to the wiring
diagram by using phenotypes for network inference and integrating them with
complementary data sources. The first part of the review describes methods to
analyze one- or low-dimensional phenotypes like viability or reporter activity;
the second part concentrates on high-dimensional phenotypes showing global
changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio
End-to-end Neural Coreference Resolution
We introduce the first end-to-end coreference resolution model and show that
it significantly outperforms all previous work without using a syntactic parser
or hand-engineered mention detector. The key idea is to directly consider all
spans in a document as potential mentions and learn distributions over possible
antecedents for each. The model computes span embeddings that combine
context-dependent boundary representations with a head-finding attention
mechanism. It is trained to maximize the marginal likelihood of gold antecedent
spans from coreference clusters and is factored to enable aggressive pruning of
potential mentions. Experiments demonstrate state-of-the-art performance, with
a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model
ensemble, despite the fact that this is the first approach to be successfully
trained with no external resources.Comment: Accepted to EMNLP 201
Research and Applications of the Processes of Performance Appraisal: A Bibliography of Recent Literature, 1981-1989
[Excerpt] There have been several recent reviews of different subtopics within the general performance appraisal literature. The reader of these reviews will find, however, that the accompanying citations may be of limited utility for one or more reasons. For example, the reference sections of these reviews are usually composed of citations which support a specific theory or practical approach to the evaluation of human performance. Consequently, the citation lists for these reviews are, as they must be, highly selective and do not include works that may have only a peripheral relationship to a given reviewer\u27s target concerns. Another problem is that the citations are out of date. That is, review articles frequently contain many citations that are fifteen or more years old. The generation of new studies and knowledge in this field occurs very rapidly. This creates a need for additional reference information solely devoted to identifying the wealth of new research, ideas, and writing that is changing the field
Sociohydrologic Systems Thinking: An Analysis of Undergraduate Studentsâ Operationalization and Modeling of Coupled Human-Water Systems
One of the keys to science and environmental literacy is systems thinking. Learning how to think about the interactions between systems, the far-reaching eďŹects of a system, and the dynamic nature of systems are all critical outcomes of science learning. However, students need support to develop systems thinking skills in undergraduate geoscience classrooms. While systems thinking-focused instruction has the potential to benefit student learning, gaps exist in our understanding of studentsâ use of systems thinking to operationalize and model SHS, as well as their metacognitive evaluation of systems thinking. To address this need, we have designed, implemented, refined, and studied an introductory-level, interdisciplinary course focused on coupled human-water, or sociohydrologic, systems. Data for this study comes from three consecutive iterations of the course and involves student models and explanations for a socio-hydrologic issue (n = 163). To analyze this data, we counted themed features of the drawn models and applied an operationalization rubric to the written responses. Analyses of the written explanations reveal statistically-significant diďŹerences between underlying categories of systems thinking (F(5, 768) = 401.6, p \u3c 0.05). Students were best able to operationalize their systems thinking about problem identification (M = 2.22, SD = 0.73) as compared to unintended consequences (M = 1.43, SD = 1.11). Student-generated systems thinking models revealed statistically significant diďŹerences between system components, patterns, and mechanisms, F(2, 132) = 3.06, p \u3c 0.05. Students focused most strongly on system components (M = 13.54, SD = 7.15) as compared to related processes or mechanisms. Qualitative data demonstrated three types of model limitation including scope/scale, temporal, and specific components/mechanisms/patterns excluded. These findings have implications for supporting systems thinking in undergraduate geoscience classrooms, as well as insight into links between these two skills
Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions
In this work, we explore video lecture interaction in Massive Open Online
Courses (MOOCs), which is central to student learning experience on these
educational platforms. As a research contribution, we operationalize video
lecture clickstreams of students into cognitively plausible higher level
behaviors, and construct a quantitative information processing index, which can
aid instructors to better understand MOOC hurdles and reason about
unsatisfactory learning outcomes. Our results illustrate how such a metric
inspired by cognitive psychology can help answer critical questions regarding
students' engagement, their future click interactions and participation
trajectories that lead to in-video & course dropouts. Implications for research
and practice are discusse
1st INCF Workshop on Genetic Animal Models for Brain Diseases
The INCF Secretariat organized a workshop to focus on the “role of neuroinformatics in the processes of building, evaluating, and using genetic animal models for brain diseases” in Stockholm, December 13–14, 2009. Eight scientists specialized in the fields of neuroinformatics, database, ontologies, and brain disease participated together with two representatives of the National Institutes of Health and the European Union, as well as three observers of the national INCF nodes of Norway, Poland, and the United Kingdom
Improving fairness in machine learning systems: What do industry practitioners need?
The potential for machine learning (ML) systems to amplify social inequities
and unfairness is receiving increasing popular and academic attention. A surge
of recent work has focused on the development of algorithmic tools to assess
and mitigate such unfairness. If these tools are to have a positive impact on
industry practice, however, it is crucial that their design be informed by an
understanding of real-world needs. Through 35 semi-structured interviews and an
anonymous survey of 267 ML practitioners, we conduct the first systematic
investigation of commercial product teams' challenges and needs for support in
developing fairer ML systems. We identify areas of alignment and disconnect
between the challenges faced by industry practitioners and solutions proposed
in the fair ML research literature. Based on these findings, we highlight
directions for future ML and HCI research that will better address industry
practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in
Computing Systems (CHI 2019
- âŚ