Browsing by Author "Faisal, Ali"

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Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs
(2012) Khan, Suleiman A.; Faisal, Ali; Mpindi, John Patrick; Parkkinen, Juuso A.; Kalliokoski, Tuomo; Poso, Antti; Kallioniemi, Olli P.; Wennerberg, Krister; Kaski, Samuel
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The neural representation of abstract words may arise through grounding word meaning in language itself
(2021-10-15) Hulten, Annika; van Vliet, Marijn; Kivisaari, Sasa; Lammi, Lotta; Lindh-Knuutila, Tiina; Faisal, Ali; Salmelin, Riitta
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
In order to describe how humans represent meaning in the brain, one must be able to account for not just concrete words but, critically, also abstract words, which lack a physical referent. Hebbian formalism and optimization are basic principles of brain function, and they provide an appealing approach for modeling word meanings based on word co-occurrences. We provide proof of concept that a statistical model of the semantic space can account for neural representations of both concrete and abstract words, using MEG. Here, we built a statistical model using word embeddings extracted from a text corpus. This statistical model was used to train a machine learning algorithm to successfully decode the MEG signals evoked by written words. In the model, word abstractness emerged from the statistical regularities of the language environment. Representational similarity analysis further showed that this salient property of the model co-varies, at 280–420 ms after visual word presentation, with activity in regions that have been previously linked with processing of abstract words, namely the left-hemisphere frontal, anterior temporal and superior parietal cortex. In light of these results, we propose that the neural encoding of word meanings can arise through statistical regularities, that is, through grounding in language itself.
Reconstructing meaning from bits of information
(2019-02-25) Kivisaari, Sasa L.; van Vliet, Marijn; Hultén, Annika; Lindh-Knuutila, Tiina; Faisal, Ali; Salmelin, Riitta
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Modern theories of semantics posit that the meaning of words can be decomposed into a unique combination of semantic features (e.g., “dog” would include “barks”). Here, we demonstrate using functional MRI (fMRI) that the brain combines bits of information into meaningful object representations. Participants receive clues of individual objects in form of three isolated semantic features, given as verbal descriptions. We use machine-learning-based neural decoding to learn a mapping between individual semantic features and BOLD activation patterns. The recorded brain patterns are best decoded using a combination of not only the three semantic features that were in fact presented as clues, but a far richer set of semantic features typically linked to the target object. We conclude that our experimental protocol allowed us to demonstrate that fragmented information is combined into a complete semantic representation of an object and to identify brain regions associated with object meaning.
Retrieval of Gene Expression Measurements with Probabilistic Models
(2014) Faisal, Ali
School of Science | Doctoral dissertation (article-based)
A crucial problem in current biological and medical research is how to utilize the diverse set of existing biological knowledge and heterogeneous measurement data in order to gain insights on new data. As datasets continue to be deposited in public repositories it is becoming important to develop search engines that can efficiently integrate existing data and search for relevant earlier studies given a new study. The search task is encountered in several biological applications including cancer genomics, pharmacokinetics, personalized medicine and meta-analysis of functional genomics. Most existing search engines rely on classical keyword or annotation based retrieval which is limited to discovering known information and requires careful downstream annotation of the data. Data-driven model-based methods, that retrieve studies based on similarities in the actual measurement data, have a greater potential for uncovering novel biological insights. In particular, probabilistic modeling provides promising model-based tools due to its ability to encode prior knowledge, represent uncertainty in model parameters and handle noise associated to the data. By introducing latent variables it is further possible to capture relationships in data features in the form of meaningful biological components underlying the data. This thesis adapts existing and develops new probabilistic models for retrieval of relevant measurement data in three different cases of background repositories. The first case is a background collection of data samples where each sample is represented by a single data type. The second case is a collection of multimodal data samples where each sample is represented by more than one data type. The third case is a background collection of datasets where each dataset, in turn, is a collection of multiple samples. In all three setups the proposed models are evaluated quantitatively and with case studies the models are demonstrated to facilitate interpretable retrieval of relevant data, rigorous integration of diverse information sources and learning of latent components from partly related dataset collections.
Toward Computational Cumulative Biology by Combining Models of Biological Datasets
(2014) Faisal, Ali; Peltonen, Jaakko; Georgii, Elisabeth; Rung, Johan; Kaski, Samuel
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations—for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.