خواتین کی معاشرتی حیثیت: سیرت النبیﷺ اور عصری تحدیات

Before Islam, women were victims of slavery, humiliation, oppression, and exploitation. Islam came and liberated women from slavery, humiliation, oppression, and exploitation. Islam abolished all the evil customs which were against the human dignity of women and gave them the rights which they deserved. The Prophet (peace and blessings of Allah be upon him) endowed women with their rightful status and human dignity on both social and domestic levels. In the light of Islamic teachings, men and women have equal status as human beings. In that sense, no one is superior to another. Just as Islam calls men honorable and respectable, so too in the eyes of Islam women are honorable and have an important role to play in society. The biography of the Holy Prophet is a beacon for us. He proved by his practical life that a woman is pitiable and respectable in respect of mother, daughter, sister, wife, and other relationships. In Arab society, he proved by raising her four daughters that a woman is also a human being, therefore, she should also be given rights. The Prophet (peace and blessings of Allah be upon him) has left out such enlightening teachings on women's social rights that by following them, women can be granted their social rights. But even today, women are being deprived of many of their social rights. There is a need to bring the social issues of women before the society in the light of Sīrat-un-Nabī. Also mention the restrictions facing women in the present. The question arises, what is the social status of women in the light of Sīrat-un-Nabī? And what are the challenges facing women today? This article is written to answer these questions. In pre-Islamic society, women were not given any importance or status in any relationship. Socially, women's rights were completely taken away and some Arab families did not even have the right to live.

Investigating Machine Learning Based Prediction of Protein Interactions

Protein interactions are crucial in the cell for performing cellular functions and the study of protein interactions is a very important domain of research in bioinformatics. In reference to protein interactions, biologists are usually interested in three core problems: determining pairwise protein interactions, determination of binding affinity, and identification of the interface. Computational methods to solve these protein interaction problems have emerged as an active research area due to tedious, costly, and time-consuming experimental procedures. Our aim in this work is to develop novel machine learning based methods for protein interaction, binding affinity and interaction prediction with improved generalization performance. In this dissertation, we have developed host-pathogen protein interaction predictors using machine learning. One of our findings is that existing methods for protein interaction prediction that use K-fold cross-validation for performance assessment report over-estimated accuracy values as K-fold cross-validation does not take pairwise protein similarity between training and test examples into account. To control this data redundancy at pathogen protein level, we have proposed and advocated the use of an alternate evaluation scheme called Leave One Pathogen Protein Out (LOPO) cross-validation along with some biologist centric metrics for designing protein-protein interaction prediction methods. We have also designed a novel machine learning model called CaMELS (CalModulin intEraction Learning System) for interaction and interaction site prediction of Calmodulin (CaM) which is a very important and highly conserved protein across all eukaryotes. CaMELS relies on a novel implementation of multiple instance learning solver for protein binding site prediction that leads to significant improvement in predictive performance. One of our collaborators has confirmed the effectiveness of CaMELS through wet-lab experiments as well. We have also focused on the more generic problem of predicting binding affinity in protein interactions and presented various sequence-based machine learning models. xxiv For this purpose, we have developed a novel machine learning method which is based on the framework of Learning Using Privileged Information (LUPI). Our state-of-the-art method uses protein 3D structure as privileged information at training time while expecting only protein sequence information during testing. This makes our machine learning method flexible by allowing it to leverage protein structure information during training while requiring only protein sequence information during testing. We have also developed a webserver for an existing state-of-the-art protein-protein interface prediction method called PAIRPred. The accuracy of this webserver has also been validated by our collaborators through wet-lab experiments as well.