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CRISPR Cas-9 is a groundbreaking genome-editing tool that harnesses bacterial defense systems to alter DNA sequences accurately. This innovative technology holds vast promise in multiple domains like biotechnology, agriculture and medicine. However, such power does not come without its own peril, and one such issue is the potential for unintended modifications (Off-Target), which highlights the need for accurate prediction and mitigation strategies. Though previous studies have demonstrated improvement in Off-Target prediction capability with the application of deep learning, they often struggle with the precision-recall trade-off, limiting their effectiveness and do not provide proper interpretation of the complex decision-making process of their models. To address these limitations, we have thoroughly explored deep learning networks, particularly the recurrent neural network based models, leveraging their established success in handling sequence data. Furthermore, we have employed genetic algorithm for hyperparameter tuning to optimize these models' performance. The results from our experiments demonstrate significant performance improvement compared with the current state-of-the-art in Off-Target prediction, highlighting the efficacy of our approach. Furthermore, leveraging the power of the integrated gradient method, we make an effort to interpret our models resulting in a detailed analysis and understanding of the underlying factors that contribute to Off-Target predictions, in particular the presence of two sub-regions in the seed region of single guide RNA which extends the established biological hypothesis of Off-Target effects. To the best of our knowledge, our model can be considered as the first model combining high efficacy, interpretability and a desirable balance between precision and recall. [ABSTRACT FROM AUTHOR]

Machine learning models have gained popularity nowadays for their potential to solve real-life issues when trained on pertinent data. In many cases, the real-life data are class imbalanced and hence the corresponding machine learning models trained on the data tend to perform poorly on metrics like precision, recall, AUC, F1, and G-mean score. Since class imbalance issue poses serious challenges to the performance of trained models, a multitude of research works have addressed this issue. Two common data-based sampling techniques have mostly been proposed-undersampling the data of the majority class and oversampling the data of the minority class. In this article, we focus on the former approach. We propose two novel algorithms that employ neural network-based approaches to remove majority samples that are found to reside in the vicinity of the minority samples, thereby undersampling the former to remove (or alleviate) the imbalance issue. We delineate the proposed algorithms and then test the proposed algorithms on some publicly available imbalanced datasets. We then compare the performance of our proposed algorithms to other popular undersampling algorithms. Finally, we conclude that our proposed algorithms outperform most of the existing undersampling approaches on most performance metrics. [ABSTRACT FROM AUTHOR]

The transit network design problem is concerned with the finding of a set of routes with corresponding schedules for a public transport system. This problem belongs to the class of NP-Hard problem because of the vast search space and multiple constraints whose optimal solution is really difficult to find out. The paper develops a Population based model for the transit network design problem. While designing the transit network, we give preference to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. Our approach to the transit network design problem is based on the Genetic Algorithm (GA) optimization. The Genetic Algorithm is similar to evolution strategy which iterates through fitness assessment, selection and breeding, and population reassembly. In this paper, we will show two different experimental results performed on known benchmark problems. We clearly show that results obtained by Genetic Algorithm with increasing population is better than so far best technique which is really difficult for future researchers to beat. [ABSTRACT FROM AUTHOR]

Abstract Nuclear Magnetic Resonance Spectroscopy (most commonly known as NMR Spectroscopy) is used to generate approximate and partial distances between pairs of atoms of the native structure of a protein. To predict protein structure from these partial distances by solving the Euclidean distance geometry problem from the partial distances obtained from NMR Spectroscopy, we can predict three-dimensional (3D) structure of a protein. In this paper, a new genetic algorithm is proposed to efficiently address the Euclidean distance geometry problem towards building 3D structure of a given protein applying NMR's sparse data. Our genetic algorithm uses (i) a greedy mutation and crossover operator to intensify the search; (ii) a twin removal technique for diversification in the population; (iii) a random restart method to recover from stagnation; and (iv) a compaction factor to reduce the search space. Reducing the search space drastically, our approach improves the quality of the search. We tested our algorithms on a set of standard benchmarks. Experimentally, we show that our enhanced genetic algorithms significantly outperforms the traditional genetic algorithms and a previously proposed state-of-the-art method. Our method is capable of producing structures that are very close to the native structures and hence, the experimental biologists could adopt it to determine more accurate protein structures from NMR data. [ABSTRACT FROM AUTHOR]

This paper presents a number of novel metaheuristic approaches that can efficiently map stream graphs on multicores. A stream graph consists of a set of actors performing different functions communicating through edges. Orchestrating stream graphs on multicores can be formulated as an Integer Linear Programming (ILP) problem but ILP solver takes exponential time to provide an optimal solution. We propose metaheuristic algorithms to achieve near optimal solutions within a reasonable amount of time. We employ six different variants of the Hill-Climbing (HC) algorithm employing different tweak operators that produce excellent result extremely quickly. We also propose six different variants of Genetic Algorithm (GA) to examine how effective these variants can be in escaping the local optima. We finally combine HC and GA techniques (which is also known as ‘ memetic algorithm ’) to produce hybrid techniques that outperform the individual performance of HC and GA techniques. We compare our results with the results generated by the CPLEX optimization tool. Our best technique has achieved a geometric mean speedup of 7.42× across a range of StreamIt benchmarks on an eight-core processor. [ABSTRACT FROM AUTHOR]