Widespread adoption of artificial intelligence (AI) technologies is substantially affecting the human condition in ways that are not yet well understood. Negative unintended consequences abound ...including the perpetuation and exacerbation of societal inequalities and divisions via algorithmic decision making. We present six grand challenges for the scientific community to create AI technologies that are human-centered, that is, ethical, fair, and enhance the human condition. These grand challenges are the result of an international collaboration across academia, industry and government and represent the consensus views of a group of 26 experts in the field of human-centered artificial intelligence (HCAI). In essence, these challenges advocate for a human-centered approach to AI that (1) is centered in human well-being, (2) is designed responsibly, (3) respects privacy, (4) follows human-centered design principles, (5) is subject to appropriate governance and oversight, and (6) interacts with individuals while respecting human's cognitive capacities. We hope that these challenges and their associated research directions serve as a call for action to conduct research and development in AI that serves as a force multiplier towards more fair, equitable and sustainable societies.
Small farms are thought to produce around a third of the global crop supply. But, in the wake of the climate crisis, their existence is increasingly vulnerable to changes in the spatial and temporal ...availability of water. The small-scale irrigation systems which water these farms present a social-ecological dilemma: Upstream farms have prevailing access to a canal's resources, but all farms along the canal must contribute to maintaining the irrigation infrastructure. Thus, it is key to assess the social mechanisms which promote resilience in these systems and, more widely, in complex social-ecological dilemmas under changing conditions. Toward this, we build on previous work in which a stylized irrigation dilemma was simulated via a social lab experiment. Studies of the data produced from this experiment modeled participants' behavior with multiple, theoretically grounded agent-based models (ABMs). These models encode causal, human-interpretable hypotheses of decision making which generates the real-world behavior observed in the experiment. However, the accuracy of these models in fitting the experimental data is limited. Using Evolutionary Model Discovery, a recent algorithm for inverse generative social science (iGSS), we show the ability to automatically generate a wide variety of unique new ABMs which fit the experimental data more accurately and robustly than the original, manually-constructed ABMs. To do this, we algorithmically explore the space of possible behavioral rules for agents choosing how to contribute to the maintenance of the irrigation infrastructure. We find that, in contrast to the original models, our best-performing models typically have an additional element of stochasticity and favor factors such as other-regarding preferences and perceived relative income. Given that this change in just a small part of the original model has yielded such an advance, our results suggest that iGSS methods have great potential for continuing to derive more accurate models of complex social-ecological dilemmas.
Drug-target interaction (DTI) prediction through in vitro methods is expensive and time-consuming. On the other hand, computational methods can save time and money while enhancing drug discovery ...efficiency. Most of the computational methods frame DTI prediction as a binary classification task. One important challenge is that the number of negative interactions in all DTI-related datasets is far greater than the number of positive interactions, leading to the class imbalance problem. As a result, a classifier is trained biased towards the majority class (negative class), whereas the minority class (interacting pairs) is of interest. This class imbalance problem is not widely taken into account in DTI prediction studies, and the few previous studies considering balancing in DTI do not focus on the imbalance issue itself. Additionally, they do not benefit from deep learning models and experimental validation. In this study, we propose a computational framework along with experimental validations to predict drug-target interaction using an ensemble of deep learning models to address the class imbalance problem in the DTI domain. The objective of this paper is to mitigate the bias in the prediction of DTI by focusing on the impact of balancing and maintaining other involved parameters at a constant value. Our analysis shows that the proposed model outperforms unbalanced models with the same architecture trained on the BindingDB both computationally and experimentally. These findings demonstrate the significance of balancing, which reduces the bias towards the negative class and leads to better performance. It is important to note that leaning on computational results without experimentally validating them and by relying solely on AUROC and AUPRC metrics is not credible, particularly when the testing set remains unbalanced.
Modeling the information of social contagion processes has recently attracted a substantial amount of interest from researchers due to its wide applicability in network science, multi-agent-systems, ...information science, and marketing. Unlike in biological spreading, the existence of a reinforcement effect in social contagion necessitates considering the complexity of individuals in the systems. Although many studies acknowledged the heterogeneity of the individuals in their adoption of information, there are no studies that take into account the individuals' uncertainty during their adoption decision-making. This resulted in less than optimal modeling of social contagion dynamics in the existence of phase transition in the final adoption size versus transmission probability. We employed the Inverse Born Problem (IBP) to represent probabilistic entities as complex probability amplitudes in edge-based compartmental theory, and demonstrated that our novel approach performs better in the prediction of social contagion dynamics through extensive simulations on random regular networks.
PurposePostsecondary institutions use metrics such as student retention and college completion rates to measure student success. Multiple factors affect the success of first time in college (FTIC) ...and transfer students. Transfer student success rates are significantly low, with most transfer students nationwide failing to complete their degrees in four-year institutions. The purpose of this study is to better understand the degree progression patterns of both student types in two undergraduate science, technology, engineering and mathematics (STEM) programs: computer science (CS) and information technology (IT). Recommendations concerning academic advising are discussed to improve transfer student success.Design/methodology/approachThis study describes how transfer student success can be improved by thoroughly analyzing their degree progression patterns. This study uses institutional data from a public university in the United States. Specifically, this study utilizes the data of FTIC and transfer students enrolled in CS and IT programs at the targeted university to understand their degree progression patterns and analyzes the program curricula using network science curricular analytics method to determine what courses in the curriculum require more assistance to retain students.FindingsThe major findings of this study are: (1) students’ degree mobility patterns within an institution differ significantly between transfer and FTIC students; (2) some similarities exist between the CS and IT programs in terms of transfer students' degree mobility patterns; (3) transfer students' performance in basic and intermediate level core courses contribute to differences in transfer students' mobility patterns.Originality/valueThis study introduces the concept of “mobility patterns” and examines student degree mobility patterns of both FTIC and transfer students in a large public university to improve the advising process for transfer students regarding courses and identifying secondary majors.
With the increasing reliance on automated decision making, the issue of algorithmic fairness has gained increasing importance. In this paper, we propose a Generative Adversarial Network for tabular ...data generation. The model includes two phases of training. In the first phase, the model is trained to accurately generate synthetic data similar to the reference dataset. In the second phase we modify the value function to add fairness constraint, and continue training the network to generate data that is both accurate and fair. We test our results in both cases of unconstrained, and constrained fair data generation. We show that using a fairly simple architecture and applying quantile transformation of numerical attributes the model achieves promising performance. In the unconstrained case, i.e., when the model is only trained in the first phase and is only meant to generate accurate data following the same joint probability distribution of the real data, the results show that the model beats the state-of-the-art GANs proposed in the literature to produce synthetic tabular data. Furthermore, in the constrained case in which the first phase of training is followed by the second phase, we train the network and test it on four datasets studied in the fairness literature and compare our results with another state-of-the-art pre-processing method, and present the promising results that it achieves. Comparing to other studies utilizing GANs for fair data generation, our model is comparably more stable by using only one critic, and also by avoiding major problems of original GAN model, such as mode-dropping and non-convergence.
Abstract
While research into drug–target interaction (DTI) prediction is fairly mature, generalizability and interpretability are not always addressed in the existing works in this field. In this ...paper, we propose a deep learning (DL)-based framework, called BindingSite-AugmentedDTA, which improves drug–target affinity (DTA) predictions by reducing the search space of potential-binding sites of the protein, thus making the binding affinity prediction more efficient and accurate. Our BindingSite-AugmentedDTA is highly generalizable as it can be integrated with any DL-based regression model, while it significantly improves their prediction performance. Also, unlike many existing models, our model is highly interpretable due to its architecture and self-attention mechanism, which can provide a deeper understanding of its underlying prediction mechanism by mapping attention weights back to protein-binding sites. The computational results confirm that our framework can enhance the prediction performance of seven state-of-the-art DTA prediction algorithms in terms of four widely used evaluation metrics, including concordance index, mean squared error, modified squared correlation coefficient ($r^2_m$) and the area under the precision curve. We also contribute to three benchmark drug–traget interaction datasets by including additional information on 3D structure of all proteins contained in those datasets, which include the two most commonly used datasets, namely Kiba and Davis, as well as the data from IDG-DREAM drug-kinase binding prediction challenge. Furthermore, we experimentally validate the practical potential of our proposed framework through in-lab experiments. The relatively high agreement between computationally predicted and experimentally observed binding interactions supports the potential of our framework as the next-generation pipeline for prediction models in drug repurposing.
PurposeSome degree programs in colleges and universities utilize entrance exams to ensure that students pursuing a given degree have mastered foundational concepts needed for that program. However, ...often these exams become a barrier to student success. The purpose of this study is to discuss the impact of policies governing an undergraduate Computer Science (CS) entry/qualifying exam at a large public university in the United States on overall student success in the program. This case study focuses on whether reforming program policies impacts students' time-to-degree, graduation and mastery in CS core skills.Design/methodology/approachThis case study describes how the CS student success was improved by updating program policies based on institutional data and the input of course instructors. The policy changes include introducing a maximum limit to attempt the exam, changing the exam requirements as well as the structure of the exam itself.FindingsThe pass rates of students taking this qualifying exam were significantly and consistently low over the years. Students who were potentially a better fit for programs other than CS delayed their start in those other programs by taking and failing to pass the CS qualifying exam multiple times. As a result of implementing new CS program policies, many more students attempted the exam at an appropriate time. Also, they graduated on time in the years 2016–2018.Originality/valueThis paper presents a case study in which the graduation rates and time to degree of a CS program with a qualifying exam were improved after a policy intervention. The results demonstrate that simple policy changes can improve student success. Findings from this study may guide other programs with similar characteristics to improve their student success.
Abstract
In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism to ...address the problem of drug–target interaction prediction. Our proposed model is inspired by sentence classification models in the field of Natural Language Processing, where the drug–target complex is treated as a sentence with relational meaning between its biochemical entities a.k.a. protein pockets and drug molecule. AttentionSiteDTI enables interpretability by identifying the protein binding sites that contribute the most toward the drug–target interaction. Results on three benchmark datasets show improved performance compared with the current state-of-the-art models. More significantly, unlike previous studies, our model shows superior performance, when tested on new proteins (i.e. high generalizability). Through multidisciplinary collaboration, we further experimentally evaluate the practical potential of our proposed approach. To achieve this, we first computationally predict the binding interactions between some candidate compounds and a target protein, then experimentally validate the binding interactions for these pairs in the laboratory. The high agreement between the computationally predicted and experimentally observed (measured) drug–target interactions illustrates the potential of our method as an effective pre-screening tool in drug repurposing applications.
Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has been the primary reason behind the COVID-19 global pandemic which has affected millions of lives worldwide. The fundamental cause of ...the infection is the molecular binding of the viral spike protein receptor binding domain (SP-RBD) with the human cell angiotensin-converting enzyme 2 (ACE2) receptor. The infection can be prevented if the binding of RBD-ACE2 is resisted by utilizing certain inhibitors or drugs that demonstrate strong binding affinity towards the SP RBD. Sialic acid based glycans found widely in human cells and tissues have notable propensity of binding to viral proteins of the coronaviridae family. Recent experimental literature have used N-acetyl neuraminic acid (Sialic acid) to create diagnostic sensors for SARS-CoV-2, but a detailed interrogation of the underlying molecular mechanisms is warranted. Here, we perform all atom molecular dynamics (MD) simulations for the complexes of certain Sialic acid-based molecules with that of SP RBD of SARS CoV-2. Our results indicate that Sialic acid not only reproduces a binding affinity comparable to the RBD-ACE2 interactions, it also assumes the longest time to dissociate completely from the protein binding pocket of SP RBD. Our predictions corroborate that a combination of electrostatic and van der Waals energies as well the polar hydrogen bond interactions between the RBD residues and the inhibitors influence free energy of binding.
Communicated by Ramaswamy H. Sarma
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