Topological biclustering ARTMAP for identifying within bicluster relationships.

Biclustering is a powerful tool for exploratory data analysis in domains such as social networking, data reduction, and differential gene expression studies. Topological learning identifies connected regions that are difficult to find using other traditional clustering methods and produces a graphical representation. Therefore, to improve the quality of biclustering and module extraction, this work combines the adaptive resonance theory (ART)-based methods of biclustering ARTMAP (BARTMAP) and topological ART (TopoART), to produce TopoBARTMAP. The latter inherits the ability to detect topological associations while performing data reduction. The capabilities of TopoBARTMAP were benchmarked using 35 real world cancer datasets and contrasted with other (bi)clustering methods, where it showed a statistically significant improvement over the other assessed methods on ordered and shuffled data experiments. In experiments with 12 synthetic datasets, the method was observed to perform better at identifying constant, scale, shift, and shift scale type biclusters. The produced graphical representation was refined to represent gene bicluster associations and was assessed on the NCBI GSE89116 dataset containing expression levels of 39,326 probes sampled over 38 observations.

Enhanced neurologic concept recognition using a named entity recognition model based on transformers.

Although deep learning has been applied to the recognition of diseases and drugs in electronic health records and the biomedical literature, relatively little study has been devoted to the utility of deep learning for the recognition of signs and symptoms. The recognition of signs and symptoms is critical to the success of deep phenotyping and precision medicine. We have developed a named entity recognition model that uses deep learning to identify text spans containing neurological signs and symptoms and then maps these text spans to the clinical concepts of a neuro-ontology. We compared a model based on convolutional neural networks to one based on bidirectional encoder representation from transformers. Models were evaluated for accuracy of text span identification on three text corpora: physician notes from an electronic health record, case histories from neurologic textbooks, and clinical synopses from an online database of genetic diseases. Both models performed best on the professionally-written clinical synopses and worst on the physician-written clinical notes. Both models performed better when signs and symptoms were represented as shorter text spans. Consistent with prior studies that examined the recognition of diseases and drugs, the model based on bidirectional encoder representations from transformers outperformed the model based on convolutional neural networks for recognizing signs and symptoms. Recall for signs and symptoms ranged from 59.5% to 82.0% and precision ranged from 61.7% to 80.4%. With further advances in NLP, fully automated recognition of signs and symptoms in electronic health records and the medical literature should be feasible.

No-boundary thinking: a viable solution to ethical data-driven AI in precision medicine.

Today Artificial Intelligence (AI) supports difficult decisions about policy, health, and our personal lives. The AI algorithms we develop and deploy to make sense of information, are informed by data, and based on models that capture and use pertinent details of the population or phenomenon being analyzed. For any application area, more importantly in precision medicine which directly impacts human lives, the data upon which algorithms are run must be procured, cleaned, and organized well to assure reliable and interpretable results, and to assure that they do not perpetrate or amplify human prejudices. This must be done without violating basic assumptions of the algorithms in use. Algorithmic results need to be clearly communicated to stakeholders and domain experts to enable sound conclusions. Our position is that AI holds great promise for supporting precision medicine, but we need to move forward with great care, with consideration for possible ethical implications. We make the case that a no-boundary or convergent approach is essential to support sound and ethical decisions. No-boundary thinking supports problem definition and solving with teams of experts possessing diverse perspectives. When dealing with AI and the data needed to use AI, there is a spectrum of activities that needs the attention of a no-boundary team. This is necessary if we are to draw viable conclusions and develop actions and policies based on the AI, the data, and the scientific foundations of the domain in question.

Memristor-based LSTM network with in situ training and its applications.

Artificial neural networks (ANNs), such as the convolutional neural network (CNN) and long short-term memory (LSTM), have high complexity and contain large numbers of parameters. Memristor-based neural networks, which have the ability of in-memory and parallel computing, are therefore proposed to accelerate the operations of ANNs. In this paper, a memristor-based hardware realization of long short-term memory (LSTM) network with in situ training is presented. The designed memristor-based LSTM (MbLSTM) network is composed of memristor-based LSTM cell and memristor-based dense layer. Sigmoid and tanh (hyperbolic tangent) activation functions are approximately implemented through intentionally designing circuit parameters. A weight update scheme with row-parallel characteristic is put forward to update the conductance of memristors in crossbars. The highlights of MbLSTM include an effective hardware-based inference process and in situ training. The validity of MbLSTM is substantiated through classification tasks. The robustness of MbLSTM to conductance variations is also analyzed.

Interfaith dialogue in medicine.

A moral crisis has swept through the United States dividing social, political, and religious organizations with corrupt and ineffectual leadership. However, the present moral crisis has its roots in the technological and cultural shifts of the last half century. The goal of interfaith dialogue is not merely to exchange pleasantries, but to build a mutual collaboration addressing the moral and ethical issues with a unified voice. Interfaith dialogue has the potential to pull us out of our individualism and, in focusing on our relationships, create a new sensibility about being human. And were that new sensibility understood to reflect some of the fundamentals of science, it would strengthen the ability of religions to pursue a more healing inclusivity and reveal a rich unity among religious faiths, stretching down from our personal relationships with each other to the divine and the very fabric of reality.

Admiring the Great Mountain: A Celebration Special Issue in Honor of Stephen Grossberg's 80th Birthday.

This editorial summarizes selected key contributions of Prof. Stephen Grossberg and describes the papers in this 80th birthday special issue in his honor. His productivity, creativity, and vision would each be enough to mark a scientist of the first caliber. In combination, they have resulted in contributions that have changed the entire discipline of neural networks. Grossberg has been tremendously influential in engineering, dynamical systems, and artificial intelligence as well. Indeed, he has been one of the most important mentors and role models in my career, and has done so with extraordinary generosity and encouragement. All authors in this special issue have taken great pleasure in hereby commemorating his extraordinary career and contributions.

BARTMAP: a viable structure for biclustering.

Clustering has been used extensively in the analysis of high-throughput messenger RNA (mRNA) expression profiling with microarrays. Furthermore, clustering has proven elemental in microRNA expression profiling, which demonstrates enormous promise in the areas of cancer diagnosis and treatment, gene function identification, therapy development and drug testing, and genetic regulatory network inference. However, such a practice is inherently limited due to the existence of many uncorrelated genes with respect to sample or condition clustering, or many unrelated samples or conditions with respect to gene clustering. Biclustering offers a solution to such problems by performing simultaneous clustering on both dimensions, or automatically integrating feature selection to clustering without any prior information, so that the relations of clusters of genes (generally, features) and clusters of samples or conditions (data objects) are established. However, the NP-complete computational complexity raises a great challenge to computational methods for identifying such local relations. Here, we propose and demonstrate that a neural-based classifier, ARTMAP, can be modified to perform biclustering in an efficient way, leading to a biclustering algorithm called Biclustering ARTMAP (BARTMAP). Experimental results on multiple human cancer data sets show that BARTMAP can achieve clustering structures with higher qualities than those achieved with other commonly used biclustering or clustering algorithms, and with fast run times.