Moving towards food security in South Asian region: Assessing the role of agricultural trade openness, production and employment.

Despite several accomplishments in addressing malnutrition, the issue of food scarcity remains a persistent concern all over the globe, particularly in the South Asian region. One recommended solution to address this situation involves advocating for further liberalization of global food trading and opening employment opportunities. In this context, using panel data spanning 2000-2019, this study makes a novel attempt to quantify the impact of agricultural trade openness and agricultural employment on food security in countries belonging to the South Asian region while controlling the tariff and agricultural production.

Generation of simulated data for Bengali text localization in natural images.

In the domain of vision-based applications, the importance of text cannot be underestimated due to its natural capacity to provide accurate and comprehensive information. The application of scene text editing systems enables the modification and enhancement of textual material included in natural images while maintaining the integrity of the overall visual layout. The complexity of keeping the original background context and font styles when altering, however, is an extremely difficult challenge considering the changed image must perfectly blend with the original without being altered.

CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data.

Background: The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal task in the biosignal classification process in the brain-computer interface (BCI) applications. Currently, this bio-engineering-based technology is being employed by researchers in various fields to develop cutting-edge applications. The classification of real-time MI-EEG signals is the most challenging task in these applications.

FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction.

The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict.

PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences.

Motivation: Extracting useful feature set which contains significant discriminatory information is a critical step in effectively presenting sequence data to predict structural, functional, interaction and expression of proteins, DNAs and RNAs. Also, being able to filter features with significant information and avoid sparsity in the extracted features require the employment of efficient feature selection techniques. Here we present PyFeat as a practical and easy to use toolkit implemented in Python for extracting various features from proteins, DNAs and RNAs.

CFSBoost: Cumulative feature subspace boosting for drug-target interaction prediction.

Drug target interaction prediction is a very labor-intensive and expensive experimental process which has motivated researchers to focus on in silico prediction to provide information on potential interaction. In recent years, researchers have proposed several computational approaches for predicting new drug target interactions. In this paper, we present CFSBoost, a simple and computationally cheap ensemble boosting classification model for identification and prediction of drug-target interactions using evolutionary and structural features.

iRecSpot-EF: Effective sequence based features for recombination hotspot prediction.

In genetic evolution, meiotic recombination plays an important role. Recombination introduces genetic variations and is a vital source of biodiversity and appears as a driving force in evolutionary development. Local regions of chromosomes where recombination events tend to be concentrated are known as hotspots and regions with relatively low frequencies of recombination are called coldspots.

Effective DNA binding protein prediction by using key features via Chou's general PseAAC.

DNA-binding proteins (DBPs) are responsible for several cellular functions, starting from our immunity system to the transport of oxygen. In the recent studies, scientists have used supervised machine learning based methods that use information from the protein sequence only to classify the DBPs. Most of the methods work effectively on the train sets but performance of most of them degrades in the independent test set.

iProtGly-SS: Identifying protein glycation sites using sequence and structure based features.

Glycation is chemical reaction by which sugar molecule bonds with a protein without the help of enzymes. This is often cause to many diseases and therefore the knowledge about glycation is very important. In this paper, we present iProtGly-SS, a protein lysine glycation site identification method based on features extracted from sequence and secondary structural information.

EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features.

Determining subcellular localization of proteins is considered as an important step towards understanding their functions. Previous studies have mainly focused solely on Gene Ontology (GO) as the main feature to tackle this problem. However, it was shown that features extracted based on GO is hard to be used for new proteins with unknown GO.

iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting.

Prediction of new drug-target interactions is critically important as it can lead the researchers to find new uses for old drugs and to disclose their therapeutic profiles or side effects. However, experimental prediction of drug-target interactions is expensive and time-consuming. As a result, computational methods for predictioning new drug-target interactions have gained a tremendous interest in recent times.