Aptamers are oligonucleotides or peptides with unique binding properties for specific target molecules, and they have shown great potential in diagnostics, therapeutics, and bio-sensing. However, the current in vitro SELEX-based method for discovering new target-selective aptamers is challenging, time-consuming, and often unsuccessful in finding high-affinity aptamers. Recently, in silico methods have gained immense attention. However, since labeled interaction-pair data collection is expensive and needs highly trained specialists, available data is sparse. Further, since acquiring positive-class samples is even more challenging, available datasets showcase high-class imbalance. This makes designing deep learning models incredibly challenging, as they require a sufficiently large training set and are biased towards the dominant class. Additionally, current models cannot be updated in real-time, and end-to-end re-training is necessary for each new aptamer-target interaction pair discovery. The present work is the first to address both these challenges. We present cAPTured, a novel fuzzy continual learning method for predicting aptamer-target protein interaction pairs in a continual learning environment. cAPTured continually updates its learned feature space on a non-stationary interaction-pair data stream. We performed extensive evaluation studies and experiments to establish the effectiveness of the proposed approach. cAPTured outperforms existing methods on the benchmark dataset by a significant margin.

Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net, a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain connectivity indices are estimated from the windowed EEG activity, and six different deep learning architectures (with varying neurons and hidden layers) are trained. The present study is the first which considers a large number of brain connectivity indices, especially for SCZ. A detailed study was also performed that identifies SCZ-related changes occurring in brain connectivity, and the vital significance of BCI is drawn in this regard to identify the biomarkers of the disease. Schizo-Net surpasses current models and achieves 99.84% accuracy. An optimum deep learning architecture selection is also performed for improved classification. The study also establishes that Late fusion technique outperforms single architecture-based prediction in diagnosing SCZ.

Non-invasive Visual Stimuli evoked-EEG-based P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants’ age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers a vast horizon, including medical assessment, assistance, diagnosis, applications, robotics, entertainment, etc. The analysis highlights an increasing potential for P300 detection using visual stimuli as a prominent and legitimate research area and demonstrates a significant growth in the research interest in the field of BCI spellers utilizing P300. This expansion was largely driven by the spread of wireless EEG devices, advances in computational intelligence methods, machine learning, neural networks and deep learning.

Deep learning-based Computer-Aided Diagnosis has gained immense attention in recent years due to its capability to enhance diagnostic performance and elucidate complex clinical tasks. However, conventional supervised deep learning models are incapable of recognizing novel diseases that do not exist in the training dataset. Automated early-stage detection of novel infectious diseases can be vital in controlling their rapid spread. Moreover, the development of a conventional CAD model is only possible after disease outbreaks and datasets become available for training (viz. COVID-19 outbreak). Since novel diseases are unknown and cannot be included in training data, it is challenging to recognize them through existing supervised deep learning models. Even after data becomes available, recognizing new classes with conventional models requires a complete extensive re-training. The present study is the first to report this problem and propose a novel solution to it. In this study, we propose a new class of CAD models, i.e., Deep-Precognitive Diagnosis, wherein artificial agents are enabled to identify unknown diseases that have the potential to cause a pandemic in the future. A de novo biologically-inspired Conv-Fuzzy network is developed. Experimental results show that the model trained to classify Chest X-Ray (CXR) scans into normal and bacterial pneumonia detected a novel disease during testing, unseen by it in the training sample and confirmed to be COVID-19 later. The model is also tested on SARS-CoV-1 and MERS-CoV samples as unseen diseases and achieved state-of-the-art accuracy. The proposed model eliminates the need for model re-training by creating a new class in real-time for the detected novel disease, thus classifying it on all subsequent occurrences. Second, the model addresses the challenge of limited labeled data availability, which renders most supervised learning techniques ineffective and establishes that modified fuzzy classifiers can achieve high accuracy on image classification tasks.

Antimicrobial peptides have gained immense attention in recent years due to their potential for developing novel antibacterial medicines, next-generation anti-cancer treatment regimes, etc. Owing to the significant cost and time required for wet lab-based AMP screening, researchers have framed the task as an ML problem. However, traditional models rely on the unrealistic premise of large medical data availability to achieve significant performance levels; otherwise, they overfit, decreasing model precision. The collection of such labeled medical data is a challenging and expensive task in itself. The current study is the first to examine models in a real-world setting, training them on restricted and highly imbalanced data. A Fuzzy Intelligence based model is proposed for short (<30 aa) AMP activity prediction, and its ability to learn on limited and severely skewed high-dimensional space mapping is demonstrated over a set of experiments. The proposed model significantly outperforms state-of-the-art ML models trained on the same data. The findings demonstrate the model's efficacy as a potential method for in silico AMP activity prediction.

High-precision breast cancer prognosis is crucial for early disease identification, avoiding hazardous side-effects of unnecessary therapies, and decreasing mortality rates through personalized and tailored treatment regimens. However, designing a prognosis model continues to be challenging, given the intricate relationship between distinct genetic attributes, varied clinical results of drug therapies, the noisy nature of gene expressions, and the high-class imbalance seen in multimodal cancer data. Furthermore, because labeled omics data collection is costly and requires highly-trained experts, the data available is very limited. This makes the design of the conventional machine and deep learning models incredibly challenging as they require large quantities of data for learning the underlying intricate patterns and would otherwise overfit, decreasing model precision. Moreover, all present models suffer from a ‘closed world assumption.’ These models, once trained, cannot be updated in real-time (when more omics data is available in the future) without a complete re-training. The present study is the first to introduce the ‘Fuzzy’ way towards Breast cancer prognosis, framing the task as an incremental learning problem. The proposed approach allows the model to continually update its learned feature space on a non-stationary multimodal data stream emulating the human brain’s remarkable quality to learn over time. We demonstrate the model’s ability to learn complex relationships between different multimodal attributes, training on severely imbalanced and limited data by mapping it to a high-dimensional ‘fused’ feature space. The proposed model surpasses state-of-the-art machine learning (ML) models significantly. These results suggest that prediction through ‘fuzzy intelligence’ is a promising approach towards breast cancer prognosis.

Today, the AI community is obsessed with state-of-the-art scores (80% papers in NeurIPS) as the major performance metrics, due to which an important parameter, i.e., the environmental metric, remains unreported. Computational capabilities were a limiting factor a decade ago; however, in foreseeable future circumstances, the challenge will be to develop environment-friendly and power-efficient algorithms. The human brain, which has been optimizing itself for almost a million years, consumes the same amount of power as a typical laptop. Therefore, developing nature-inspired algorithms is one solution to it. In this study, we show that currently used ANNs are not what we find in nature, and why, although having lower performance, spiking neural networks, which mirror the mammalian visual cortex, have attracted much interest. We further highlight the hardware gaps restricting the researchers from using spike-based computation for developing neuromorphic energy-efficient microchips on a large scale. Using neuromorphic processors instead of traditional GPUs might be more environment friendly and efficient. These processors will turn SNNs into an ideal solution for the problem. This paper presents in-depth attention highlighting the current gaps, the lack of comparative research, while proposing new research directions at the intersection of two fields- neuroscience and deep learning. Further, we define a new evaluation metric 'NATURE' for reporting the carbon footprint of AI models.

The identification of B-cell epitopes that elicit an antigen-specific immune response is essential for a variety of immunodetection and immunotherapeutic applications, including the development of safe and high efficacy vaccines. Identifying diagnostically or therapeutically useful epitopes is a difficult, time-consuming, and resource-intensive procedure. In silico prediction of B-cell epitope has gained immense attention in recent years due to its low cost, fast results, and less labor-intensive method compared to NMR spectroscopy and 3D X-ray structural analysis of antibody-antigen complexes. However, one of the major problems that most established models confront is gathering huge volumes of data. Moreover, most models do not achieve high levels of accuracy. The current work is the first to propose the ‘Fuzzy’ approach to in silico B-cell epitope prediction. The effectiveness of the proposed approach is demonstrated on severely imbalanced and limited datasets through several experiments. The results show that using the proposed method enhances both accuracy and precision when compared to existing approaches. Further, the model is tested on the SARS-CoV-1 antigen-antibody PDB complex. The proposed approach outperforms state-of-the-art machine learning (ML) models trained on the same dataset. Results obtained indicate that applying the proposed method improves the prediction compared to the other approaches.

Vision is the most essential sense for human beings. But today, more than 2.2 billion people worldwide suffer from some form of vision impairment. This paper presents an end-to-end human-centric model for aiding the visually impaired by employing the deep recurrent architecture of the start-of-the-art image captioning models. A VGG-16 net convolutional neural network (CNN) is used to extract feature vectors from real-time video (image frames) and an long short-term memory (LSTM) network is employed to generate captions from these feature vectors. The model is tested on the Flickr 8K Dataset, one of the most popularly used image captioning dataset which contains over 8000 images. On real-time videos, the model generates rich descriptive captions which are converted to audio for a visually impaired person to listen. Comprehensively the model generates promising results which has great potential to enhance the lives of the visually impaired people by assisting them to get a better understanding of their surroundings.