53,997 research outputs found
Inductive queries for a drug designing robot scientist
It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments
Cross-screening in observational studies that test many hypotheses
We discuss observational studies that test many causal hypotheses, either
hypotheses about many outcomes or many treatments. To be credible an
observational study that tests many causal hypotheses must demonstrate that its
conclusions are neither artifacts of multiple testing nor of small biases from
nonrandom treatment assignment. In a sense that needs to be defined carefully,
hidden within a sensitivity analysis for nonrandom assignment is an enormous
correction for multiple testing: in the absence of bias, it is extremely
improbable that multiple testing alone would create an association insensitive
to moderate biases. We propose a new strategy called "cross-screening",
different from but motivated by recent work of Bogomolov and Heller on
replicability. Cross-screening splits the data in half at random, uses the
first half to plan a study carried out on the second half, then uses the second
half to plan a study carried out on the first half, and reports the more
favorable conclusions of the two studies correcting using the Bonferroni
inequality for having done two studies. If the two studies happen to concur,
then they achieve Bogomolov-Heller replicability; however, importantly,
replicability is not required for strong control of the family-wise error rate,
and either study alone suffices for firm conclusions. In randomized studies
with a few hypotheses, cross-split screening is not an attractive method when
compared with conventional methods of multiplicity control, but it can become
attractive when hundreds or thousands of hypotheses are subjected to
sensitivity analyses in an observational study. We illustrate the technique by
comparing 46 biomarkers in individuals who consume large quantities of fish
versus little or no fish.Comment: 33 pages, 2 figures, 5 table
Modeling Epistemological Principles for Bias Mitigation in AI Systems: An Illustration in Hiring Decisions
Artificial Intelligence (AI) has been used extensively in automatic decision
making in a broad variety of scenarios, ranging from credit ratings for loans
to recommendations of movies. Traditional design guidelines for AI models focus
essentially on accuracy maximization, but recent work has shown that
economically irrational and socially unacceptable scenarios of discrimination
and unfairness are likely to arise unless these issues are explicitly
addressed. This undesirable behavior has several possible sources, such as
biased datasets used for training that may not be detected in black-box models.
After pointing out connections between such bias of AI and the problem of
induction, we focus on Popper's contributions after Hume's, which offer a
logical theory of preferences. An AI model can be preferred over others on
purely rational grounds after one or more attempts at refutation based on
accuracy and fairness. Inspired by such epistemological principles, this paper
proposes a structured approach to mitigate discrimination and unfairness caused
by bias in AI systems. In the proposed computational framework, models are
selected and enhanced after attempts at refutation. To illustrate our
discussion, we focus on hiring decision scenarios where an AI system filters in
which job applicants should go to the interview phase
On null hypotheses in survival analysis
The conventional nonparametric tests in survival analysis, such as the
log-rank test, assess the null hypothesis that the hazards are equal at all
times. However, hazards are hard to interpret causally, and other null
hypotheses are more relevant in many scenarios with survival outcomes. To allow
for a wider range of null hypotheses, we present a generic approach to define
test statistics. This approach utilizes the fact that a wide range of common
parameters in survival analysis can be expressed as solutions of differential
equations. Thereby we can test hypotheses based on survival parameters that
solve differential equations driven by cumulative hazards, and it is easy to
implement the tests on a computer. We present simulations, suggesting that our
tests perform well for several hypotheses in a range of scenarios. Finally, we
use our tests to evaluate the effect of adjuvant chemotherapies in patients
with colon cancer, using data from a randomised controlled trial
Neural correlates of sexual cue reactivity in individuals with and without compulsive sexual behaviours
Although compulsive sexual behaviour (CSB) has been conceptualized as a "behavioural" addiction and common or overlapping neural circuits may govern the processing of natural and drug rewards, little is known regarding the responses to sexually explicit materials in individuals with and without CSB. Here, the processing of cues of varying sexual content was assessed in individuals with and without CSB, focusing on neural regions identified in prior studies of drug-cue reactivity. 19 CSB subjects and 19 healthy volunteers were assessed using functional MRI comparing sexually explicit videos with non-sexual exciting videos. Ratings of sexual desire and liking were obtained. Relative to healthy volunteers, CSB subjects had greater desire but similar liking scores in response to the sexually explicit videos. Exposure to sexually explicit cues in CSB compared to non-CSB subjects was associated with activation of the dorsal anterior cingulate, ventral striatum and amygdala. Functional connectivity of the dorsal anterior cingulate-ventral striatum-amygdala network was associated with subjective sexual desire (but not liking) to a greater degree in CSB relative to non-CSB subjects. The dissociation between desire or wanting and liking is consistent with theories of incentive motivation underlying CSB as in drug addictions. Neural differences in the processing of sexual-cue reactivity were identified in CSB subjects in regions previously implicated in drug-cue reactivity studies. The greater engagement of corticostriatal limbic circuitry in CSB following exposure to sexual cues suggests neural mechanisms underlying CSB and potential biological targets for interventions
Collective Implicit Attitudes: A Stakeholder Conception of Implicit Bias
Psychologists and philosophers have not yet resolved what they take implicit attitudes to be; and, some, concerned about limitations in the psychometric evidence, have even challenged the predictive and theoretical value of positing implicit attitudes in explanations for social behavior. In the midst of this debate, prominent stakeholders in science have called for scientific communities to recognize and countenance implicit bias in STEM fields. In this paper, I stake out a stakeholder conception of implicit bias that responds to these challenges in ways that are responsive to the psychometric evidence, while also being resilient to the sorts of disagreements and scientific progress that would not undermine the soundness of this call. Along the way, my account advocates for attributing collective (group-level) implicit attitudes rather than individual-level implicit attitudes. This position raises new puzzles for future research on the relationship (metaphysical, epistemic, and ethical) between collective implicit attitudes and individual-level attitudes
The influence of the Ratio Bias phenomenon on the elicitation of Standard Gamble utilities
This paper tests whether logically equivalent risk formats can lead to different health state utilities elicited by means of the standard gamble (SG) method. We compare SG utilities elicited when probabilities are framed in terms of frequencies with respect to 100 people in the population (i.e., X out of 100) with SG utilities elicited for frequencies with respect to 1,000 people in the population (i.e., Y out of 1,000). We found that utilities were significant higher when success and failure probabilities were framed as frequencies type “Y out of 1,000” rather than as frequencies type “X out of 100”. This framing effect, known as Ratio Bias, may have important consequences in resource allocation decisions.Framing effect, risk format, standard gamble, health state, dual-process theories.
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