The effectiveness of feature attribution methods and its correlation with automatic evaluation scores

Explaining the decisions of an Artificial Intelligence (AI) model is increasingly critical in many real-world, high-stake applications. Hundreds of papers have either proposed new feature attribution methods, discussed or harnessed these tools in their work. However, despite humans being the target end-users, most attribution methods were only evaluated on proxy automatic-evaluation metrics (Zhang et al. 2018; Zhou et al. 2016; Petsiuk et al. 2018). In this paper, we conduct the first user study to measure attribution map effectiveness in assisting humans in ImageNet classification and Stanford Dogs fine-grained classification, and when an image is natural or adversarial (i.e., contains adversarial perturbations). Overall, feature attribution is surprisingly not more effective than showing humans nearest training-set examples. On a harder task of fine-grained dog categorization, presenting attribution maps to humans does not help, but instead hurts the performance of human-AI teams compared to AI alone. Importantly, we found automatic attribution-map evaluation measures to correlate poorly with the actual human-AI team performance. Our findings encourage the community to rigorously test their methods on the downstream human-in-the-loop applications and to rethink the existing evaluation metrics.Comment: NeurIPS 2021; 10 pages of Main text; 28 pages of Appendi

Theoretical and computational modeling of rna-ligand interactions

Ribonucleic acid (RNA) is a polymeric nucleic acid that plays a variety of critical roles in gene expression and regulation at the level of transcription and translation. Recently, there has been an enormous interest in the development of therapeutic strategies that target RNA molecules. Instead of modifying the product of gene expression, i.e., proteins, RNAtargeted therapeutics aims to modulate the relevant key RNA elements in the disease-related cellular pathways. Such approaches have two significant advantages. First, diseases with related proteins that are difficult or unable to be drugged become druggable by targeting the corresponding messenger RNAs (mRNAs) that encode the amino acid sequences. Second, besides coding mRNAs, the vast majority of the human genome sequences are transcribed to noncoding RNAs (ncRNAs), which serve as enzymatic, structural, and regulatory elements in cellular pathways of most human diseases. Targeting noncoding RNAs would open up remarkable new opportunities for disease treatment. The first step in modeling the RNA-drug interaction is to understand the 3D structure of the given RNA target. With current theoretical models, accurate prediction of 3D structures for large RNAs from sequence remains computationally infeasible. One of the major challenges comes from the flexibility in the RNA molecule, especially in loop/junction regions, and the resulting rugged energy landscape. However, structure probing techniques, such as the “selective 20-hydroxyl acylation analyzed by primer extension” (SHAPE) experiment, enable the quantitative detection of the relative flexibility and hence structure information of RNA structural elements. Therefore, one may incorporate the SHAPE data into RNA 3D structure prediction. In the first project, we investigate the feasibility of using a machine-learning-based approach to predict the SHAPE reactivity from the 3D RNA structure and compare the machine-learning result to that of a physics-based model. In the second project, in order to provide a user-friendly tool for RNA biologists, we developed a fully automated web interface, “SHAPE predictoR” (SHAPER) for predicting SHAPE profile from any given 3D RNA structure. In a cellular environment, various factors, such as metal ions and small molecules, interact with an RNA molecule to modulate RNA cellular activity. RNA is a highly charged polymer with each backbone phosphate group carrying one unit of negative (electronic) charge. In order to fold into a compact functional tertiary structure, it requires metal ions to reduce Coulombic repulsive electrostatic forces by neutralizing the backbone charges. In particular, Mg2+ ion is essential for the folding and stability of RNA tertiary structures. In the third project, we introduce a machine-learning-based model, the “Magnesium convolutional neural network” (MgNet) model, to predict Mg2+ binding site for a given 3D RNA structure, and show the use of the model in investigating the important coordinating RNA atoms and identifying novel Mg2+ binding motifs. Besides Mg2+ ions, small molecules, such as drug molecules, can also bind to an RNA to modulate its activities. Motivated by the tremendous potential of RNA-targeted drug discovery, in the fourth project, we develop a novel approach to predicting RNA-small molecule binding. Specifically, we develop a statistical potential-based scoring/ranking method (SPRank) to identify the native binding mode of the small molecule from a pool of decoys and estimate the binding affinity for the given RNA-small molecule complex. The results tested on a widely used data set suggest that SPRank can achieve (moderately) better performance than the current state-of-art models

Optimization of Automatic Target Recognition with a Reject Option Using Fusion and Correlated Sensor Data

This dissertation examines the optimization of automatic target recognition (ATR) systems when a rejection option is included. First, a comprehensive review of the literature inclusive of ATR assessment, fusion, correlated sensor data, and classifier rejection is presented. An optimization framework for the fusion of multiple sensors is then developed. This framework identifies preferred fusion rules and sensors along with rejection and receiver operating characteristic (ROC) curve thresholds without the use of explicit misclassification costs as required by a Bayes\u27 loss function. This optimization framework is the first to integrate both vertical warfighter output label analysis and horizontal engineering confusion matrix analysis. In addition, optimization is performed for the true positive rate, which incorporates the time required by classification systems. The mathematical programming framework is used to assess different fusion methods and to characterize correlation effects both within and across sensors. A synthetic classifier fusion-testing environment is developed by controlling the correlation levels of generated multivariate Gaussian data. This synthetic environment is used to demonstrate the utility of the optimization framework and to assess the performance of fusion algorithms as correlation varies. The mathematical programming framework is then applied to collected radar data. This radar fusion experiment optimizes Boolean and neural network fusion rules across four levels of sensor correlation. Comparisons are presented for the maximum true positive rate and the percentage of feasible thresholds to assess system robustness. Empirical evidence suggests ATR performance may improve by reducing the correlation within and across polarimetric radar sensors. Sensitivity analysis shows ATR performance is affected by the number of forced looks, prior probabilities, the maximum allowable rejection level, and the acceptable error rates

Interpretable deep neural networks for more accurate predictive genomics and genome-wide association studies

Genome-wide association studies (GWAS) and predictive genomics have become increasingly important in genetics research over the past decade. GWAS involves the analysis of the entire genome of a large group of individuals to identify genetic variants associated with a particular trait or disease. Predictive genomics combines information from multiple genetic variants to predict the polygenic risk score (PRS) of an individual for developing a disease. Machine learning is a branch of artificial intelligence that has revolutionized various fields of study, including computer vision, natural language processing, and robotics. Machine learning focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed. Deep learning is a subset of machine learning that uses deep neural networks to recognize patterns and relationships. In this dissertation, we first compared various machine learning and statistical models for estimating breast cancer PRS. A deep neural network (DNN) was found to be the most effective, outperforming other techniques such as BLUP, BayesA, and LDpred. In the test cohort with 50% prevalence, the receiver operating characteristic curves area under the curves (ROC AUCs) were 67.4% for DNN, 64.2% for BLUP, 64.5% for BayesA, and 62.4% for LDpred. While BLUP, BayesA, and LDpred generated PRS that followed a normal distribution in the case population, the PRS generated by DNN followed a bimodal distribution. This allowed DNN to achieve a recall of 18.8% at 90% precision in the test cohort, which extrapolates to 65.4% recall at 20% precision in a general population. Interpretation of the DNN model identified significant variants that were previously overlooked by GWAS, highlighting their importance in predicting breast cancer risk. We then developed a linearizing neural network architecture (LINA) that provided first-order and second-order interpretations on both the instance-wise and model-wise levels, addressing the challenge of interpretability in neural networks. LINA outperformed other algorithms in providing accurate and versatile model interpretation, as demonstrated in synthetic datasets and real-world predictive genomics applications, by identifying salient features and feature interactions used for predictions. Finally, it has been observed that many complex diseases are related to each other through common genetic factors, such as pleiotropy or shared etiology. We hypothesized that this genetic overlap can be used to improve the accuracy of polygenic risk scores (PRS) for multiple diseases simultaneously. To test this hypothesis, we propose an interpretable multi-task learning approach based on the LINA architecture. We found that the parallel estimation of PRS for 17 prevalent cancers using a pan-cancer MTL model was generally more accurate than independent estimations for individual cancers using comparable single-task learning models. Similar performance improvements were observed for 60 prevalent non-cancer diseases in a pan-disease MTL model. Interpretation of the MTL models revealed significant genetic correlations between important sets of single nucleotide polymorphisms, suggesting that there is a well-connected network of diseases with a shared genetic basis

Deep Learning for Genomics: A Concise Overview

Advancements in genomic research such as high-throughput sequencing techniques have driven modern genomic studies into "big data" disciplines. This data explosion is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in a variety of fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning since we are expecting from deep learning a superhuman intelligence that explores beyond our knowledge to interpret the genome. A powerful deep learning model should rely on insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with a proper deep architecture, and remark on practical considerations of developing modern deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research, as well as pointing out potential opportunities and obstacles for future genomics applications.Comment: Invited chapter for Springer Book: Handbook of Deep Learning Application

The Effect of Visual Perceptual Load on Auditory Processing

Many fundamental aspects of auditory processing occur even when we are not attending to the auditory environment. This has led to a popular belief that auditory signals are analysed in a largely pre-attentive manner, allowing hearing to serve as an early warning system. However, models of attention highlight that even processes that occur by default may rely on access to perceptual resources, and so can fail in situations when demand on sensory systems is particularly high. If this is the case for auditory processing, the classic paradigms employed in auditory attention research are not sufficient to distinguish between a process that is truly automatic (i.e., will occur regardless of any competing demands on sensory processing) and one that occurs passively (i.e., without explicit intent) but is dependent on resource-availability. An approach that addresses explicitly whether an aspect of auditory analysis is contingent on access to capacity-limited resources is to control the resources available to the process; this can be achieved by actively engaging attention in a different task that depletes perceptual capacity to a greater or lesser extent. If the critical auditory process is affected by manipulating the perceptual demands of the attended task this suggests that it is subject to the availability of processing resources; in contrast a process that is automatic should not be affected by the level of load in the attended task. This approach has been firmly established within vision, but has been used relatively little to explore auditory processing. In the experiments presented in this thesis, I use MEG, pupillometry and behavioural dual-task designs to explore how auditory processing is impacted by visual perceptual load. The MEG data presented illustrate that both the overall amplitude of auditory responses, and the computational capacity of the auditory system are affected by the degree of perceptual load in a concurrent visual task. These effects are mirrored by the pupillometry data in which pupil dilation is found to reflect both the degree of load in the attended visual task (with larger pupil dilation to the high compared to the low load visual load task), and the sensory processing of irrelevant auditory signals (with reduced dilation to sounds under high versus low visual load). The data highlight that previous assumptions that auditory processing can occur automatically may be too simplistic; in fact, though many aspects of auditory processing occur passively and benefit from the allocation of spare capacity, they are not strictly automatic. Moreover, the data indicate that the impact of visual load can be seen even on the early sensory cortical responses to sound, suggesting not only that cortical processing of auditory signals is dependent on the availability of resources, but also that these resources are part of a global pool shared between vision and audition

Hidden Citations Obscure True Impact in Science

References, the mechanism scientists rely on to signal previous knowledge, lately have turned into widely used and misused measures of scientific impact. Yet, when a discovery becomes common knowledge, citations suffer from obliteration by incorporation. This leads to the concept of hidden citation, representing a clear textual credit to a discovery without a reference to the publication embodying it. Here, we rely on unsupervised interpretable machine learning applied to the full text of each paper to systematically identify hidden citations. We find that for influential discoveries hidden citations outnumber citation counts, emerging regardless of publishing venue and discipline. We show that the prevalence of hidden citations is not driven by citation counts, but rather by the degree of the discourse on the topic within the text of the manuscripts, indicating that the more discussed is a discovery, the less visible it is to standard bibliometric analysis. Hidden citations indicate that bibliometric measures offer a limited perspective on quantifying the true impact of a discovery, raising the need to extract knowledge from the full text of the scientific corpus