Researchers in bioinformatics use protein function prediction to classify proteins, study their roles in disease mechanisms. Many mechanisms have been proposed to address the significant gap between the rapid increase in identified proteins and the slower growth in annotated protein functions. While PPI network data can assist in protein function prediction, it is often overlooked. Moreover, existing approaches primarily rely on node2vec embeddings of PPI data, neglecting valuable information about the neighborhood relationships of proteins. In this paper, we proposed a novel multi-modal approach that leverages a Graph Convolutional Network (GCN) to analyze PPI network data and explore neighboring proteins. Additionally, we employ Multi-Head Self-Attention combined with a Convolutional Bi-LSTM on protein sequence data to enhance the prediction of protein functions. To evaluate the effectiveness of our method, we utilize benchmark datasets, specifically from homo sapiens, such as String database for PPI network information and UniprotKB for protein sequences, ensuring a comprehensive and robust analysis. Extensive experiments demonstrate that the proposed method, Multi-DeepProtGraphGO, achieves improvements of +18.28%, +4.56%, and +6.92% in terms of the $F_{\max}$ score for the BP, CC, and MF sub-ontologies, respectively, compared to the state-of-the-art method CrossPredGO.

Proteins are represented in various ways, each contributing differently to protein-related tasks. Here, information from each representation (protein sequence, 3D structure, and interaction data) is combined for an efficient protein function prediction task. Recently, uni-modal has produced promising results with state-of-the-art attention mechanisms that learn the relative importance of features, whereas multi-modal approaches have produced promising results by simply concatenating obtained features using a computational approach from different representations which leads to an increase in the overall trainable parameters. In this paper, we propose a novel, light-weight cross-modal multi-attention (CrMoMulAtt) mechanism that captures the relative contribution of each modality with a lower number of trainable parameters. The proposed mechanism shows a higher contribution from PPI and a lower contribution from structure data. The results obtained from the proposed CrossPredGO mechanism demonstrate an increment in in the range of +(3.29 to 7.20)% with at most 31% lower trainable parameters compared with DeepGO and MultiPredGO.

Deep learning approaches, such as convolution neural networks (CNNs) and deep recurrent neural networks (RNNs), have been the backbone for predicting protein function, with promising state-of-the-art (SOTA) results. RNNs with an in-built ability (i) focus on past information, (ii) collect both short-and-long range dependency information, and (iii) bi-directional processing offers a strong sequential processing mechanism. CNNs, however, are confined to focusing on short-term information from both the past and the future, although they offer parallelism. Therefore, a novel bi-directional CNN that strictly complies with the sequential processing mechanism of RNNs is introduced and is used for developing a protein function prediction framework, Bi-SeqCNN. This is a sub-sequence-based framework. Further, Bi-SeqCNN is an ensemble approach to better the prediction results. To our knowledge, this is the first time bi-directional CNNs are employed for general temporal data analysis and not just for protein sequences. The proposed architecture produces improvements up to +5.5% over contemporary SOTA methods on three benchmark protein sequence datasets. Moreover, it is substantially lighter and attain these results with (0.50-0.70 times) fewer parameters than the SOTA methods.

Theshort-and-longrange interactions amongst amino-acids in a protein sequence are primarily responsible for the function performed by the protein. Recently convolutional neural network (CNN)s have produced promising results on sequential data including those of NLP tasks and protein sequences. However, CNN's strength primarily lies at capturingshortrange interactions and are not so good atlongrange interactions. On the other hand, dilated CNNs are good at capturing bothshort-and-longrange interactions because of varied –short-and-long– receptive fields. Further, CNNs are quite light-weight in terms of trainable parameters, whereas most existing deep learning solutions for protein function prediction (PFP) are based on multi-modality and are rather complex and heavily parametrized. In this paper, we propose a (sub-sequence +dilated-CNNs)-based simple, light-weight and sequence-only PFP frameworkLite-SeqCNN. By varyingdilation-rates,Lite-SeqCNNefficiently captures bothshort-and-longrange interactions and has (0.50–0.75 times) fewer trainable parameters than its contemporary deep learning models. Further,Lite-SeqCNN$^+$is an ensemble of threeLite-SeqCNNs developed with different segment-sizes that produces even better results compared to the individual models. The proposed architecture produced improvements upto 5% over state-of-the-art approachesGlobal-ProtEnc Plus,DeepGOPlus, andGOLabeleron three different prominent datasets curated from the UniProt database.

This paper advances the self-attention mechanism in the standard transformer network specific to the modeling of the protein sequences. We introduce a novel context-window based scaled self-attention mechanism for processing protein sequences that is based on the notion of (i) local context and (ii) large contextual pattern. Both notions are essential to building a good representation for protein sequences. The proposed context-window based scaled self-attention mechanism is further used to build the multi context-window based scaled (MCWS) transformer network for the protein function prediction task at the protein sub-sequence level. Overall, the proposed MCWS transformer network produced improved predictive performances, outperforming existing state-of-the-art approaches by substantial margins. With respect to the standard transformer network, the proposed network produced improvements in F1-score of +2.30% and +2.08% on the biological process (BP) and molecular function (MF) datasets, respectively. The corresponding improvements over the state-of-the-art ProtVecGen-Plus+ProtVecGen-Ensemble approach are +3.38% (BP) and +2.86% (MF). Equally important, robust performances were obtained across protein sequences of different lengths.

Inferring the protein function(s) via the protein sub-sequence classification is often obstructed due to lack of knowledge about function(s) of sub-sequences in the protein sequence. In this regard, we develop a novel multi-aspect paradigm to perform the sub-sequence classification in an efficient way by utilizing the information of the parent sequence. The aspects are: (1) Multi-label: independent labelling of sub-sequences with more than one functions of the parent sequence, and (ii) Label-relevance: scoring the parent functions to highlight the relevance of performing a given function by the sub-sequence. The multi-aspect paradigm is used to propose the Multi-Attention Based Multi-Aspect Network for classifying the protein sub-sequences, where multi-attention is a novel approach to process sub-sequences at word-level. Next, the proposed Global-ProtEnc method is a sub-sequence based approach to encoding protein sequences for protein function prediction task, which is finally used to develop as ensemble methods, Global-ProtEnc-Plus. Evaluations of both the Global-ProtEnc and the Global-ProtEnc-Plus methods on the benchmark CAFA3 dataset delivered a outstanding performances. Compared to the state-of-the-art DeepGOPlus, the improvements in F_max with the Global-ProtEnc-Plus for the biological process is +6.50 percent and cellular component is +1.90 percent.

This paper explores the use of variants of tf-idf-based descriptors, namely length-normalized-tf-idf and log-normalized-tf-idf, combined with a segmentation technique, for efficient modeling of variable-length protein sequences. The proposed solution, ProtVecGen-Ensemble, is an ensemble of three models trained on differently segmented datasets constructed from an input dataset containing complete protein sequences. Evaluations using biological process (BP) and molecular function (MF) datasets demonstrate that the proposed feature set is not only superior to its contemporaries but also produces more consistent results with respect to variation in sequence lengths. Improvements of +6.07% (BP) and +7.56% (MF) over state-of-the-art tf-idf-based MLDA feature set were obtained. The best results were achieved when ProtVecGen-Ensemble was combined with ProtVecGen-Plus - the state-of-the-art method for protein function prediction - resulting in improvements of +8.90% (BP) and +11.28% (MF) over MLDA and +1.49% (BP) and +2.07% (MF) over ProtVecGen-Plus+MLDA. To capture the performance consistency with respect to sequence lengths, we have defined a variance-based metric, with lower values indicating better performance. On this metric, the proposed ProtVecGen-Ensemble+ProtVecGen-Plus framework resulted in reductions of 56.85 percent (BP) and 56.08 percent (MF) over MLDA and 10.37 percent (BP) and 26.48 percent (MF) over ProtVecGenPlus+MLDA.

The order of amino acids in a protein sequence enables the protein to acquire a conformation suitable for performing functions, thereby motivating the need to analyze these sequences for predicting functions. Although machine learning based approaches are fast compared to methods using BLAST, FASTA, etc., they fail to perform well for long protein sequences (with more than 300 amino acids). In this paper, we introduce a novel method for construction of two separate feature sets for protein using bi-directional long short-term memory network based on the analysis of fixed 1) single-sized segments and 2) multi-sized segments. The model trained on the proposed feature set based on multi-sized segments is combined with the model trained using state-of-the-art Multi-label Linear Discriminant Analysis (MLDA) features to further improve the accuracy. Extensive evaluations using separate datasets for biological processes and molecular functions demonstrate not only improved results for long sequences, but also significantly improve the overall accuracy over state-of-the-art method. The single-sized approach produces an improvement of +3.37 percent for biological processes and +5.48 percent for molecular functions over the MLDA based classifier. The corresponding numbers for multi-sized approach are +5.38 and +8.00 percent. Combining the two models, the accuracy further improves to +7.41 and +9.21 percent, respectively.

Automated static analysis tools are widely used in identifying software anomalies, such as memory leak, unsafe thread synchronization and malicious behaviors in smartphone applications. Such anomaly-prone scenarios can be bifurcated into: “ordinary” (analysis requires relatively simple automation) and “complex” (analysis poses extraordinary automation challenges). While automated static analysis tools can resolve ordinary scenarios with high accuracy, automating the analysis of complex scenarios can be very challenging and, at times, infeasible. Even when feasible the cost for full automation can be exorbitant: either in implementing the automation or in sifting through the large number of erroneous results manually. Instead, we appeal for a “Human-in-the-loop” approach called “Amplified Reasoning Technique” (ART). While some of the existing approaches do involve human in the analysis process, the roles played by man and machine are mainly segregated. Whereas, ART puts man and machine in a “loop” in an interactive and visualization-based fashion. This paper makes an attempt to convince its readers to make their analysis of software anomalies ART-based by presenting real-world case studies of complex anomalies and how an ART based approach can be very effective in resolving them. The case studies highlight the desired characteristics of an ART based tool and the type of role it plays in amplifying human intelligence.